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Campus Champions Fellows Program

The Fellows Program partners Campus Champions with Extended Collaborative Support Services (ECSS) staff and research teams to work side by side on real-world science and engineering projects.

2019-2020 Fellows Applications now open!

We are pleased to announce the opening of the 2019-20 Campus Champions Fellows application period.  These applications will be open through May 24, 2019, and can be made at  Potential projects for fellows are available for applicants at  Thank you for your interest in the Campus Champions Fellows program!


The cyberinfrastructure expertise developed by high-end application support staff in XSEDE's Extended Collaborative Support Services (ECSS) 

program can be difficult to disseminate to the large numbers of researchers who would benefit from this knowledge. Among their many roles, XSEDE Campus Champions (CC) serve as local experts on national cyberinfrastructure resources and organizations, such as XSEDE. Champions are closely connected to those doing cutting-edge research on their campuses. The goal of the Campus Champions (CC) Fellows program is to increase cyberinfrastructure expertise on campuses by including CCs as partners in XSEDE's Extended Collaborative Support Services (ECSS) projects.

The Fellows program partners Campus Champions with ECSS staff and research teams to work side by side on real-world science and engineering projects. In 2015, the types of projects offered have expanded beyond ECSS projects. In addition to ECSS, Champions now have the opportunity to work with XSEDE Cyberinfrastructure Integration (XCI) to develop on-ramps from campuses to XSEDE, to work with Community Engagment & Enrichment staff to help create a formal undergraduate or graduate minor, concentration, or certificate program at their institution, or to design a project of their choosing. Fellows will develop expertise within varied areas of cyberinfrastructure, and they are already well positioned to share their advanced knowledge through their roles as the established conduits to students, administrators, professional staff, and faculty on their campuses. A directory of Fellows will expand the influence even further by creating a network of individuals with these unique skill sets. In addition to the technical knowledge gleaned from their experiences, the individual Fellows will benefit from their personal interactions with the ECSS staff and will acquire the skills necessary to manage similar user or research group project requests on their own campuses. The Campus Champions Fellows program is a unique, rare opportunity for a select group of individuals to learn first-hand about the application of high-end cyberinfrastructure to challenging science and engineering problems.

A volunteer partner opportunity - ECSS Affiliates

Accepted Fellows make a 400-hour time commitment and are paid a $15,000 annual stipend for their efforts. The program includes funding for two one- to two-week visits to an ECSS or research team site to enhance the collaboration and also funding to attend and present at a Fellows symposium at an XSEDE conference.

The following are the types of skills that may be developed, depending on project assignments:

  • Use of profiling and tracking tools to better understand a code's characteristics
  • CUDA programming
  • Hybrid (MPI/OpenMP) programming
  • Optimal use of math libraries
  • Use of visualization tools, with a particular focus on large data sets
  • Use of I/O tools and software such as HDF5, MPI IO, and parallel file system optimization
  • Optimal use of scientific application software such as AMBER, ABAQUS, RaxML, MrBayes, etc.
  • Application of high-performance computing and high-throughput computing to non-traditional domains such as computational linguistics, economics, genomics
  • Single-processor optimization techniques
  • Benchmarking, including concepts of sockets, binding processes to cores, and impacts of these in optimization
  • Optimal data transfer techniques including the use of grid-ftp and Globus Online
  • Cluster scheduling, tuning for site-specific goals
  • Managing credentials, security practices
  • Monitoring resources with information services
  • Understanding failure conditions and programming for fault tolerance
  • Automated work and data flow systems
  • Web tools and libraries for building science gateways or portals that connect to high-end resources
  • Data management through tools such as iRODS and SAGA

For questions about the program please contact


Download a PDF (1.5MB) of the Memo of Understanding for the Fellows program

Key Points
Campus Champions as local experts
Campus Champions work with ECSS staff and research teams
Campus Champions responsibilities and skills


CMU Scientists Use XSEDE-Allocated Resources to Simulate Improved Battery Components

One of the predicted new low cobalt structures of Li Nix Mn y Co 1-x-y O2 with a ratio of nickel to maganese to cobalt of 18:5:1. The nickel is shown in grey, the maganese in magenta and the cobalt in blue. The lithium layer is shown in green and oxygen in red.

by Ken Chiacchia, Pittsburgh Supercomputing Center

The move toward cleaner, cheaper energy would be much easier if we had more powerful, safer battery technologies. Carnegie Mellon University (CMU) scientists have used the XSEDE-allocated Bridges system at the Pittsburgh Supercomputing Center (PSC) and Comet at the San Diego Supercomputer Center (SDSC) to simulate new battery component materials that are inherently safer and more powerful than currently possible.

Why It's Important:

Better batteries might not make all our energy problems vanish. But they'd be a really good start. Companies as varied as Tesla, Chevrolet, Jaguar and Audi have begun selling electric cars. This promises a generation of vehicles that run on whatever technology at a given time provides the most economical—and cleanest—electricity. But the performance of today's batteries falls short for use in larger vehicles, such as trucks and aircraft. By the same token, wind and, increasingly, solar power are becoming important actors in the U.S. energy grid. But they'd be far more cost-efficient if we could store the peak power they generate during the day, so that it can be used whenever needed. Again, today's batteries aren't quite up to it.

"Many companies are moving toward personal vehicles being electrified. Moving to larger vehicles such as trucks or aviation requires a higher energy density; and as we approach higher energy densities, the technical problems become bigger."—Gregory Houchins, CMU

There's a gap between what today's battery technology can do and what's needed for these transformations. Batteries' ability to store energy—their "energy density"—has to increase, and it has to happen without risk of fires, as seen in some devices. It would also be nice if these batteries didn't contain so much cobalt, which is found in very few parts of the world and so increases their cost.

"We're trying to find new solid electrolytes that can conduct ions quickly as [today's] liquid electrolytes, which are flammable. And we need anodes with a very high energy density."—Zeeshan Ahmad, CMU

Graduate students Zeeshan Ahmad and Gregory Houchins, working in the CMU lab of Assistant Professor Venkat Viswanathan, have been pursuing different avenues in their group's quest to find safer, more powerful solid-state lithium batteries. To do this, they turned to simulations and machine learning on two XSEDE-allocated systems: Bridges at PSC and Comet at SDSC.

How XSEDE Helped:

Houchins worked on the problem of finding cathodes—the positive pole of a battery—that contain lower amounts of expensive cobalt. He wrote software that randomly explores different cathode compositions and tests their efficiencies using the known properties of each simulated material's components. This would have been a problem on a traditional supercomputer. That's because the two steps of generating a candidate material and simulating its properties require repeatedly refining the application. A traditional system would have forced him to perform both steps for each material and wait to see if they worked properly—or failed, in which case he had to start again.

"One thing I like about Bridges is the interactive feature … I've written a code that will sample the composition space randomly, and from that read-in try and find the best model. It's all automated, and debugging is difficult to do [in a single submitted batch to a traditional supercomputer]. I'm able to use the interactive mode to quickly debug that code."—Gregory Houchins, CMU

Bridges, though, emphasizes interactive access. That feature allowed Houchins to monitor the computation's progress as it happened, and correct where needed. This helped him to debug quickly, wasting far less time. To date his software has identified over a dozen alternative cathodes predicted to perform as well as the high cobalt-containing materials now used in lithium batteries.

Ahmad, meanwhile, was working on another problem. Batteries consist of a positive cathode, a negative anode, and an electrolyte that allows electricity to flow between them. Lithium batteries are powerful and compact. But their liquid electrolytes are highly flammable. Also, the tendency of dendrites—literally, "little fingers"—to form on the anode and reach toward the cathode further risks fire by causing a short circuit between the anode and cathode. It also limits the lifetime of the battery.

"XSEDE has provided my group with the computational resources needed to tackle some of these very important problems."—Venkat Viswanathan, CMU

Ahmad and his collaborators used PSC's Bridges and SDSC's Comet to simulate non-flammable solid electrolytes that also wouldn't allow dendrites to form on the anode. This project used machine learning, a type of artificial intelligence that makes the computer experiment with many random solutions until it finds ones that meet the goal. The scientists screened almost 13,000 candidate solid electrolytes, finding 10 predicted to discourage dendrite formation. They reported these results in the journal ACS Central Science last year.

Both projects need further development—both will screen for more candidates, and the materials identified need to be made and tested in the real world to confirm they have the predicted properties. But they've taken the first steps in cracking the fundamental problems that limit battery storage.

Deeper Dive: Machine Learning on Multiple Systems

Zeeshan Ahmad's project using machine learning to predict whether anode materials would be likely to form dendrites had two major components, each of which required a different computer architecture to run well. The first method, used by Ahmad's coauthor Tian Xie of Massachusetts Institute of Technology (MIT), was a convolutional neural network. CNNs consist of layers of analysis in which parts of the computation are programmed to behave roughly like nerve cells in the brain's optical cortex, with each virtual "neuron" connecting with a set of neurons in the layer above and below it.

In the collaborators' CNN, the neural network focused on two major properties of the materials—the shear modulus, or resistance against a shearing force, and the bulk modulus, or the resistance against compression. These properties determine whether dendrites will form in a battery using that material as the electrolyte. The CNN has good predictive power for these two properties since the training data are accurate and well established from first-principles.

But the candidate materials are anistropic—their properties vary in different directions. Because of this, the shear and bulk modulus are not enough to determine whether a material will form dendrites. To test the different anisotropic properties of each material, Ahmad used different regression techniques suited to the available data. The types of regression that worked best for these computations were gradient boosting and kernel ridge regression.

"We're running calculations that have to store a lot of data on the quantum mechanical wavefunction of electron in different systems … the stored data increase to the third power of the number of electrons in the system. Bridges' high memory per core enables us to use just one node; in another system, we would have to use multiple nodes—besides the data transfer reason, in conventional Slurm queuing systems, it would take more time for a multiple-node job to clear the queue."—Zeeshan Ahmad, CMU

GPUs, or graphics processing units, tend to make CNN computations run fastest. But the quantum mechanical calculations underlying the computation are extremely memory hungry. At the time, the GPU resources available would have been overcome by a roadblock in accessing the data. So each computation in effect required a different kind of computer.

The scientists ran the CNN on the GPU nodes in SDSC's Comet, and the regression on the "regular memory" CPU nodes in PSC's Bridges. The latter had enough memory—128 gigabytes, enough to qualify as large-memory on most HPC systems—to speed the regression and quantum mechanical computations. The two systems helped the scientists to run both computations quickly and efficiently. Since this phase of the research concluded, a new GPU-AI resource has been added to Bridges, including a DGX-2 node that will enable the entire workflow. The MIT group is continuing their research on this resource, which will make future such computations even faster and more efficient.

XSEDE Scholars Program

The XSEDE Scholars Program extended skills to underrepresented students in the areas of supercomputers, data collections, new tools, digital services and increased productivity for thousands of scientists around the world.

The XSEDE Scholars Program (XSP) served U.S. students from underrepresented groups in the area of computational science (2011-2017). Participants learned about high performance computing and XSEDE resources, networked with cutting-edge researchers and professional leaders, and were part of a cohort that established a community of academic leaders.

Meet the some of the Scholars! Follow the links below to access their bios:

XSEDE Scholars:

  • Received a stipend of $5000 and 30,000 computing service unit hours to work on an HPC educational/research internship during the academic year.
  • Received a travel grant to attend the annual XSEDE Conference (PEARC)
  • Met other XSEDE Scholars in special sessions at the XSEDE conference and via an online community throughout the year
  • Participated in at least six online technical training and mentoring webinars with other XSEDE scholars
  • Networked with leaders in the XSEDE research community
  • Learned about research, internships, and career opportunities
  • Traveled and participated in the 2-week Blue Waters Petascale Institute

Past recorded computing sessions are available on the XSP Course Materials page.

The XSP was directed by Professor Richard Tapia, recipient of the 2011 Medal of Science, professor in the Department of Computational and Applied Mathematics and Director of the Center for Excellence and Equity in Education at Rice University.

Key Points
XSP aimed to increased diversity in computing
Contact Information

Previous XSEDE Scholar Cohorts

XSEDE Scholars from past years

Abbie Groff

I was born and raised in Lancaster, PA and then moved across the country to study at Stanford University where I majored in cell and molecular biology ('11). I'm currently completing a tech year at Stanford in an epithelial biology lab before moving back across the continent in the coming fall to start grad school in the Harvard systems biology department.

Adolfo Escobedo

Hi, my name is Adolfo Escobedo. I am a second-year Industrial and Systems Engineering PhD student at Texas A&M University. I studied mathematics and history at California State University, Los Angeles. I was born in Guatemala, and I lived in Mexico City for more than 10 years. I have a wide range of interests and I enjoy learning about different cultures. 

Adrian Flowers

Adrian Flowers is about to begin his second year of Graduate School enrolled at the University of Rhode Island in Computer Science.  His focal area is Computer Vision with research interests in Localization and Mapping, and Augmented Reality.

There is a very strong chance that he read too much Science Fiction growing up.

Agnes Ramos

My name is Agnes Ramos. I come from the country side of Puerto Rico. I just finished my BS in Computer Science at the University of Puerto Rico. I will be starting Master's degree in CS with emphasis on High Performance Computing at Mississippi State University. I have been working with FPGA's programming and soon will start programming GPU's.

Alejandro Weibel

I am currently a senior at the University of Texas at Austin majoring in Computer Science. I've helped create a start-up company with several other UT students, I have a position at IBM as a software developer intern that I've worked at and enjoyed for the last 6 months, and will be getting my INFOSEC Security Certificate from UT next semester. I am also planning on getting a Master's Degree in Computer Science from UT Austin, and have already submitted my application. I've attended three other Computing Conferences over the last 3 years from Florida to California, and I am looking forward to attending my fourth conference in Chicago.

Alemsthay Abeje

I was born and raised in Ethiopia. I moved to the United States four years ago. I attended Orange Coast Community college for two years. I got accepted to University of California, San Diego in 2011. I am currently majoring in pure mathematics and doing a minor in biology.

Alex Grabacki

I am currently an undergraduate Computer Engineering student at the University of Illinois: Urbana-Champaign.  I'm very interested in Linux and supercomputers.  I enjoy traveling to new places and learning new things.

Amanuel Weldermariam

My name is Amanuel Weldemariam. I went to Herbert Hoover High School in San Diego, class of 2010..  I'm originally from Ethiopia and currently live in San Diego. I'm a sophomore at UCSD studying Nanoengineering with a material science focus. I plan to attend graduate school for either in Nanoengineering or Business Administration.

Anja Guillory

I am currently a sophomore at the University of Texas at Austin.  I am an undergraduate student pursing  degrees in both Computer Science and Linguistics.  I was born and raised in San Antonio, my family is multi-cultural and fostered an appreciation for other cultures.  We travel often, I have seen nearly half of the United States, as well as Tokyo, Osaka and Kyoto, Japan and Mexico.

Briana Miller

I am currently an Undergraduate at Hampton University. I am majoring Chemistry with a concentration in Forensics. I was raised in Brooklyn, NY but I now reside in Virginia for college. I am the youngest of two children. I am also the first grandchild to attend a four year accredited university. I have recently been introduced to the world of computational chemistry, and thoroughly enjoy the experience. I have participated in research that uses methods of analytical chemistry. 

Carla D. Smith

My name is Carla D. Smith. I am a sophomore at Purdue University majoring in Electrical and Computer Engineering Technology.  I have an Associates Degree in Paralegal Studies.  My career goal is to merge fields and specialize in Intellectual Property. 

Carlos Sanchez

I study Computer Science and Mathematics at Rice University. Though my career may be tied to Computer Science in the future, I enjoy studying all sorts of fields that undergraduate life can provide. 

Cynthia Wood

Cynthia Wood is a third year graduate student in the department of Computational and Applied Mathematics at Rice University. She completed her undergraduate education in Mathematics at the University of California San Diego in 2010. Wood's research interests include topics in discrete optimization. More specifically, her research focuses in the development of a graph theoretical approach towards understanding certain aspects of memory. 

Dan Calderon

I am a second-year Electrical Engineering PhD student at Rice pursuing a specialization in Machine Learning for Education, studying under the supervision of Prof. Richard Baraniuk. I am interested in researching and applying novel, effective machine learning techniques towards the goal of affordably personalizing online education at large scale.

David Manosalvas


David Manosalvas, originally from Quito-Ecuador, is a PhD student and MS candidate in Aeronautics and Astronautics at Stanford University.  His research focuses on the aerodynamic effects of transpiration cooling for hypersonic vehicles, with a special focus on boundary layer analysis.  Prior to this, he received a BS in Mechanical Engineering from Bradley University for which his focus was on Energy and Thermodynamics.  He has worked in multiple industries which include high speed machinery design, power generation, and aerospace design and manufacturing. 

Elizabeth Alvarez


My name is Elizabeth Alvarez and I am a fourth year student pursuing a degree in Interdisciplinary Computing in the Arts over at UCSD. I come from a Latino background with a family of three older brothers and two hard working parents. I have decided I would like to go to graduate school and receive a masters or doctorate in computation. I hope that this year XSEDE will guide me to find my area focus in which I am passionate about and will give me the necessary steps to achieve my short-term goals. 

Eric Santos

Eric D. Santos Sosa, an undergraduate student seeking a in major computer science at the University of Puerto Rico. He is a collaborator in a research involving Networking and Visualization at the University of Puerto Rico. He recently started a research involving image processing and watermarks, which he will continue next semester. Currently he works at the Dean of Graduate Studies and Research at the University of Puerto Rico as a web designer and developer. His research interest are High Performance Computing, Visualization, Networking, Software Engineering and Machine Learning. His goals are to get his bachelor's degree in computer science, get a Ph.D degree in Machine Learning combined with a little of Software Engineering and Robotics, get a decent job and later in his life teach at the University of Puerto Rico.

Erika Jones


Erika Jones is a Ph.D. candidate at George Mason University in the Computational Sciences and Informatics Program.  Her concentration is computational physics.  She is researching how (a) features of quantum information science might inspire new designs in writing genetic algorithms (GAs) which can run on conventional/classical computers and (b) GAs might be designed specifically for running on quantum computing hardware yet to be realized.  Through the federal Student Career Experience Program, she also works part-time as an analyst at the U.S. Department of Transportation.  Before going back to school for her second undergraduate degree—a B.S. in computer and information science—she worked in engineering in the semiconductor manufacturing industry.  She also holds a B.S. in physics and an M.S. in chemical physics.


Erin Vehstedt

Ms. Vehstedt was born and raised in Westchester, New York. She attended Tulane University, where she studied Physics, Mathematics, and French. She completed her undergraduate research in experimental condensed matter physics under the advisement of Prof. Zhiqiang Mao. Since 2010 she has been pursuing her Ph. D. in theoretical condensed matter physics at Texas A&M University with Prof. Jairo Sinova. She is currently a Marie Curie visiting research fellow at the Institute of Physics of the Academy of Sciences of the Czech Republic under the supervision of Prof. Tomas Jungwirth. Her previous research focused primarily on Ruthenates and other transition metal oxides, as well as FeSe-type novel superconductors. Her current research interests lie in theoretical condensed matter, specifically the relationship between physical and electronic structure and properties in the strong spin - orbit coupling regime. She aims to continue her research as a bridge between analytic, numerical, and experimental efforts to improve the understanding of spin dynamics and strongly-correlated systems.

Gerardine Lamble

Hi, my name is Gerardine ( Geri ) Lamble.  I am a re-entry Computer Engineering graduate student at Santa Clara University.  I have an M.S. and Engineer Degree in Computer Engineering earned in 1996 and 2010.  I was a research assistant in the Vision Group at NASA Ames Research Center in 2011.  As a recent SolarWorks Workforce Development Grant beneficiary I earned a Power Systems Engineering Certificate in June 2012.  I now plan to study energy efficient software focusing on high performance computing.  


Alexandria Robinson

Alexandria Robinson is a third year baccalaureate candidate at Xavier University of Louisiana, where she majors in mathematics and minors in computer science. Over the summer, Alexandria is working in the Computer Science Department as a student leader for the BP STEM Scholars Program. During the Fall semester, Alexandria will be conducting an interdisciplinary research project on bank fraud prevention security. After graduating, Alexandria plans on entering into a PhD program concentrating in information security. Alexandria currently resides in New Orleans, Louisiana and is looking forward to the new places that the XSEDE Scholars Program will take her.

Allison Hall

I am currently a third year undergraduate at UC Berkeley. I am majoring in Electrical Engineering and Computer Science with a minor in Mechanical Engineering. I will be spending the summer interning at LinkedIn working on the web development team. I also work with a research group at Cal exploring computational game theory and designing game interfaces.

Amy Prager

I am a computational mathematician interested in educational issues.
My dream is to design and administer outreach programs to underrepresented groups in STEM fields.

Andre King

Hello! I am a first-year Electrical Engineering and Computer Science undergrad at UC Berkeley. I spent a few years in Tijuana, Mexico, but have grown up mainly in San Diego, CA. I enjoy learning, exercising, coding, trying new things and meeting new people everyday! I am interested in startups and entrepreneurship and aim to one day start a company that will help millions of people around the world.

Augusto Seminario

My name is Augusto Seminario, i am originally from Peru and came to the U.S. 9 years ago. i am currently an undergraduate student at U.C. Berkeley in the department of Electrical Engineering and Computer Science, this is my third year (junior).

Brandon Jacobs

Hola, my name is Brandon Jacobs, and I am a Computer Science major at Georgia Tech.My field of study is theoretical computer science and artificial intelligence. I one day hope to do research in quantum computing. Besides that I was born in New Orleans, and you can always find me trying new things.

Cassidy Williams

My name is Cassidy Williams.  I just completed my junior year at Iowa State University with a B.S. in computer science.  I enjoy front end development and mobile development.  On the side, I love salsa dancing, karaoke, drawing and playing guitar.

Charmara Mays

I attend the University of Texas at Austin, I am a first year undergrad, and I am planning on graduating in 2016. After graduating, I wish to work for the government, so I can give back to my country, as my father did.

Daniel Cuevas Vasquez

I am a junior student at the University of Puerto Rico, Mayagüez Campus studying Computer Engineering; I will focus on hardware electronics. I am currently working on a research under the supervision of Dr. Nayda Santiago with the purpose of developing a target detection algorithm that can be implemented on a FGPA for medical purposes.

David Manosalvas

David Manosalvas, originally from Quito- Ecuador, is currently a graduate student in the Aeronautics and Astronautics department of Stanford University. His research focuses in the study of active flow control for vehicle pressure drag optimization using Computational Fluid Dynamics (CFD). In addition to his academic commitments, he is the president of "Ecuatorianos @ Stanford" (ECUS) as well as the co-president of the Stanford student chapter of the American Institute of Aeronautics and Astronautics (AIAA). Before joining Stanford, David received a BS in Mechanical Engineering from Bradley University in 2011.

Diana Riveros

Diana is currently an undergraduate student at UC San Diego majoring in Applied Mathematics. After graduation, she hopes to get into a mathematical engineering graduate program.
Besides studying mathematics, Diana is a member of the Global Teams in Engineering Services (TIES) program at UCSD where she is the leader of the Fiji kindergarten project.
In the future, she hopes to work for a government agency as a cryptanalyst.

Diego Mesa

I graduated from the University of Florida (Go Gators!) with a Bachelors in Computer Engineering and am now pursuing a PhD in neural engineering through the department of Bioeng@UCSD. I'm trying to develop novel analysis techniques to trace the flow of information in large neural networks.

Erika Lomeli-Uribe

I was born in Guadalajara, Mexico and emigrated to Las Vegas, Nevada with my immediate family when I was in elementary school. I graduated with a BS degree in Environmental Geology in December 12' and began an MS in Geology with an emphasis in Remote Sensing and GIS. I hope to get more experience and training in mass data storage, computing and querying with XSEDE.

Fernando Ramirez

My name is Fernando Ramirez and I grew up in Waco, Tx.  I have an interest in doing my part to equalize the opportunities available to all, and believe that computer science along with other initiatives is the only way we will get there.  I am taking the summer to learn as much about computer science as I can so that I can develop apps for non-profits in my free time.

Genaro Hernandez Jr

Genaro Hernandez Jr is a first-year PhD student in Computer Science at UMBC. He enjoys working with his research advisor, Dr. Tim Oates. He aims to contribute to advances in the application of machine learning to solve biomedical problems in cancer. A Cancer Research Training Award will enable him to work in the laboratory of Dr. Javed Khan at the NCI this summer. He is grateful, for the opportunity to join the XSEDE Scholars. XSEDE will help him advance in his career.

Giancarlo Sanguinetti

My name is Giancarlo Sanguinetti. I am currently a rising Junior at Lehigh University, studying for a double major in Computer Science and Global Studies. I was born in Bronx, New York but was raised and live in Philadelphia, Pennsylvania. I love trying new things in life and being challenged.

Grace Silva

I earned a B.S. in bioinformatics and computational biology from the University of Maryland, Baltimore County where as a Meyerhoff Scholar I was first introduced to biological and biomedical research opportunities. During my time as an undergraduate I attended the Research Experience for Undergraduates at Princeton University and the Stanford Summer Research Program where I developed an interest in cancer research. Currently, I am a 4th year bioinformatics and computational biology doctoral candidate at the University of North Carolina, at Chapel Hill. My thesis work involves profiling copy number alterations in breast cancer using human tumors and mouse models.

Jaime Arteaga

Hi! I'm from Colombia. My background is in Electronics Engineering with expertise in Hardware/Software Co-Design on FPGAs. Since Summer 2012, I've been working at the University of Delaware, Newark - DE, in the Computer Architecture and Parallel Systems Laboratory - CAPSL - under the supervision of Professor Guang R. Gao.

Janeth Vargas

I'm currently a rising sophomore at Purdue University majoring in Computer & Information Technology with a concentration in Network Engineering Technology. My interests are in cyber security and supercomputing. I hope to do undergraduate research soon and attend graduate school in the future. I also love to dance, travel, and learn new languages.

Jessica Chery

I'm a 4th year PhD candidate in the Molecular Biology, Cell Biology, and Biochemistry program at Brown University. My research involves the study of coordinate gene regulation: the establishment of domains of coordinate regulation across the genome. I focus on elucidating the DNA sequences across the genome that are predictive of DNA binding for a specific transcription factor to facilitate specificity of the whole X-chromosome for global regulation. Our work has implications for cancer and neurodegenerative diseases. I acquired B.S. in chemistry and a B.A. in Biology before beginning my PhD. Outside of lab, I like to have fun with friends.

Jorge Ortiz

My name is Jorge Ortiz and I was born and raised in Las Vegas, Nevada. I am current a senior in both electrical and computer engineering at the University of Nevada, Las Vegas. I'm interested in electronics, solid state materials and semiconductors. I like learning about new technologies and cutting edge research. When I have time, I like to catch up on sleep, because for engineers that's somewhat of a luxury.

Josue Salazar

Hi there, my name is Josue Salazar, native of Tamaulipas, México. I migrated to the US at the age of seventeen. At the time I knew "nada" of English, so I had to retake three years of High School. In retrospect, those years were not a loss. I've received a B.S. degree in Computer Systems Engineering from the University of Houston – Clear Lake in 2009. Then I worked as a research assistant at the Lunar and Planetary Institute developing methods for detecting spatial, temporal and modal change in geo-spatial datasets using association analysis. An thanks to the support of so many wonderful people I am currently a Ph.D. student at Rice University in the department of Computer Science. My current research interests involve the development and application of statistical machine learning and data mining methods to solve real problems. And my uttermost interest is to serve my Creator.

Kevin James

I was born in Lake Charles, LA. I am currently a Engineering PhD student at the University of Oklahoma.  I have a Bachelors of Science in Computer Science and a Masters of Science in Electrical and Computer Engineering.  Education is very important to me though the cost of some programs is concerning to me!!!

Latifa Jackson

My research interests have grown out of my experiences living and working abroad in East and West Africa. One cannot see the impact of infectious diseases on human populations without thinking about possible solutions. For me, this means understanding how immune adaptive genetic variation is partitioned across geographical scales and how this variation contributes to immune defense of infectious diseases in human populations. I am currently working on a project to examine the variation in key immune function pathways in a global distribution of human populations. This work is fundamental to understanding not only reasons that certain human populations are immune to specific diseases, but it also had predictive ability to look at how various ethnic populations respond to drugs targeting immune pathways.

Lydon Ellsworth

Ya'tah'hey! My name is Lydon Ellsworth and I am attending Navajo Technical College in my hometown of Crownpoint, New Mexico on the Navajo Reservation; Eastern Agency.

Malcolm Chitsa

I am entering my Junior year at Old Dominion University and currently major in Information Technology. I am originally from Maryland. I've fell in love with HPC. I'm an intern for Web Technologies at a company called EPE and a High Performance Computing assistant at ODU. I look forward to expanding my experience and knowledge of HPC as technology continues to mature.

Manuel Zubieta

Manuel is a Southern Regional Education Board (SREB) Doctoral Scholar and a student at the University of New Orleans in the Doctorate in Engineering and Applied Sciences (DENAS) program.  Manuel has a double bachelors in Computer Science and Mathematics from the University of New Orleans.  Manuel has twice been selected as an XSEDE Scholar and is now returning as an XSEDE Elder Statesman to help mentor the new XSEDE Scholars.

Mariela Barrera

I am a junior level student at West Texas A&M University majoring in Computer Science. I was born in Oklahoma but was raised in the Panhandle of Texas. I plan to attend graduate school and get a masters in Computer Science and get involved with biological research to solve some of humanities biggest problems. Besides having an interest in technology, I also enjoy writing, reading, filming, playing music and learning about psychology, astronomy, and biology.

Marvin Turner

I attend Morehouse College where I study Computer Science. My interests involve the application of computer science concepts to interdisciplinary fields. I have taken classes in Bioinformatics and Human Computer Interaction. I have previously done Human Centered Computing research, and will be doing Computational Science and Engineering research before I graduate. In my spare time I help plan student events and develop websites.

Melva James

I am a third year Ph.D. student in Clemson University's School of Computing. My personal research interests include: information visualization, scientific modeling and simulation, educational software design, and the design of interactive systems that promote positive behavioral change. I'm a Mississippi native, and my background is in physical science.  I have a B.S. in Chemistry from Ole Miss and an S.M. in Chemistry from MIT.

Nicholas Szapiro

I'm a graduate student at the University of Oklahoma in meteorology. I studied math and engineering as an undergraduate and have an MS in computational engineering. My research is the verification and validation of a new, coupled global weather model.

Norma Easter

I am an undergraduate at Georgia Institute of Technology pursuing two undergraduate degrees in Computational Media and Applied Languages and Intercultural Studies with a minor in International Affairs. I hope to pursue a graduate degree in Computer Science or Digital Media. Prior to starting my senior year I have seen the Northern Lights, caved in the depths of Budapest, and experienced the day in the life of a geisha.

Paul Delgado

I'm a 3rd year PhD Student in Computational Mechanics, and I'm researching multiscale methods for coupled flow & deformation mechanics in porous media, with applications in Carbon Sequestration and Enhanced Oil Recovery.

Pedro Bello-Maldonado

I was born and raised in Colombia before coming to the U.S. three years ago. I was majoring in Mechanical Engineering but after coming I had to transfer to Electrical Engineering. My true love, however, is Computer Science. I am interested in using computational tools and applied math to solve science problems. I play the harp and the guitar, and I love dancing, cartoons, and studying.

Ressi Miranda

I'm Ressi Miranda and am currently, a sophomore from Mount Holyoke College, majoring in Computer Science and Physics. I enjoy the diverse environment that Computer Science provides. As for Physics, it's to fulfillment my enjoyment for understanding physical phenomena. I have a passion for teaching just about anything!

Rodney Pickett

Rodney is a graduate student at Michigan State University studying in Computer Science and Engineering.  He earned his bachelors degree in Aeronautical Engineering at Western Michigan University. Rodney has interned with the Remote Data and Visualization Center as well as the Blue Waters Undergraduate Petascale Education Program dealing with data visualization, HPC and computational sciences. He currently works at Michigan State's BEACON Center.

Shanadeen Begay

Hello! Ya'a'eeh! My name is Shanadeen Begay and I am a graduate student studying the Conformations of Methionine Enkephaline using Statistical Temperature Molecular Dynamics CHARMM. I am at Boston University and work with Professor Thomas Keyes in the areas of Theoretical and Computational Chemistry.  I am interested in how algorithms can be applied to bio-physical phenomena.  My work falls within the framework of understanding how mis-folding can perpetuate diseases and cancers such as breast cancer.  I enjoy international travel, politics, and creative writing in my personal time.  I look forward to participating in the XSEDE Scholars program and thank you for taking time to read this post!

Shenna Shearin

My name is Shenna Shearin and I am from Henderson, North Carolina. I obtained both BS (2008) and MS (2011) degrees in Chemistry at North Carolina Agricultural and Technical State University (NC A&T  SU). Currently, I am a PhD student in the Computational Science and Engineering Program at NC A&T SU . My research entails investigating the Activation of Blood Coagulation Protein Factor IX via the Intrinsic and Extrinsic pathways by Molecular Dynamic Simulations.

Travas Lenard

I was born in New Rochelle, New York but I moved south to Brentwood, Tn.  I am currently a senior at Fisk University in Nashville and I'm looking forward to help build our excellent computer science school. Computer science has always been my hobby although I was not aware of the field.I strive for opportunities to advance myself and get better at living life.

Victoria Nneji

Victoria Nneji is an Applied Mathematics student in Columbia Engineering, minoring in Entrepreneurship & Innovation. She believes that there is value in being able to think about numbers in a more useful way. Victoria wants to continue building her skills and understanding of computational science. Ultimately, she aspires for her career to be of service to communities, Victoria desires to use her technical talents across disciplines to solve problems and build better products that are needed.

Wendy Vazquez

I am currently an undergraduate Computer Engineering student attending the University of California, San Diego.  I was born and raised in the city of Los Angeles. On my free time, I enjoy exploring and learning new things from technology to someone's cultural background.


Scholar Jason Regina

Jason Regina (University of Wyoming; graduate, student, civil engineering)

Project Title: OnRamp to Parallel Computing Internship,

Research Advisor: Dr. Samantha Foley, Department of Computer Science, Univ. Wisconsin-La Crosse

 HPC Resources: Stampede, Blue Waters


OnRamp to Parallel Computing is a web portal front-end to parallel computers that allows users to learn about parallel computing while being able to run parallel jobs from the start. It includes a web front-end and necessary light-weight backend scripts to interface with various parallel computers. The project is designed to teach users how to use a parallel computer gradually through guided experimentation until they are able to confidently logon to a parallel machine on their own.


The project has been started and there is a prototype implementation that allows a user to successfully launch an existing parallel code on a remote parallel computer. This internship would further develop the front-end to include educational materials (borrowed from NCSI curricular modules), as well as port the backend portion to other parallel platforms, including LittleFe. The balance of front-end and backend work is negotiable and can be tailored to the student's interests and experience. The front-end work includes developing different user views based on parallel computing experience, writing and porting tutorials on parallel computing concepts, developing forms to allow users to specify batchscript details from the web, and job statistics presentation. The backend work includes porting the existing backend to LittleFe from a Rocks cluster environment, setting up the source code for the curriculum modules on the target machines, development of helper scripts for managing job execution remotely, and job statistics gathering. The backend is written in Python. This is a large project that may not be completed in the summer, but I believe we can make excellent progress towards a product that can be used in the classroom starting in the Fall.


Scholar Efrain Vargas Ramos

Efrain Vargas Ramos (University of Puerto Rico - Rio Piedras; graduate student; applied mathematics)

Project Title: Parallelizing Suffix Array Construction


Research Advisor: Ana Gonzalez, Department of Mathematical Sciences, University of Puerto Rico at Mayaguez

HPC Resources: Stampede, Blue Waters

This educational event will expose the student to the complete research process, with the benefit of:  incorporating Blue Waters resources and developing skills in computational thinking, computer programming, high performance computing and algorithm development. The student together with the mentor will study the problems of suffix array construction. Serial and parallel algorithms will be designed and implemented using C, MPI, OpenMP. Performance tests will be conducted and results analyzed. The data collected from experiments performed in Blue Waters system will be compared with results obtained from other resource from XSEDE. Also results documented in the literature will be compared with our results. The project will be documented in an appropriate format: technical papers, posters, oral presentations or combinations.The experience from this event will be used to develop educational modules that will allow to incorporate a parallel computing culture throughout the Computer Science and Mathematics curriculum at UPR-Mayaguez.


Scholar Olivia Irving

Olivia Irving (University of California at Los Angeles; Undergraduate Student; physical chemistry)


Project Title: Surface-deposited molecules and clusters: structure, surface-patterning, and bonding analysis

Research Advisor: Anastassia Alexandrova, Professor of Chemistry and Biochemistry, University of California at Los Angeles

HPC Resources: Stampede, Blue Waters


Dr. Anastassia Alexandrova lab is hosting one student for the XSEDE Scholars program, with a focus on studying small clusters/molecules on surfaces. Specifically we may focus on Carboranedithiols, which have been shown to assemble on Au{111} substrates and to form two-dimensional plastic lattices.  We may also extend our effort to catalytic clusters of transition metals deposited on semiconductors. Plane-wave density functional theory (DFT) is employed to elucidate the energetics of different binding sites through the program Quantum Espresso. Analysis of Bader charge will be done in VASP. Mechanism analysis will be done in Gaussian and Quantum Espresso and In Silico STM images generated. The second goal involves the development of the maximally localized Wannier functions method facilitated by machine learning, for the analysis of chemical bonding and electron transport mechanisms and pathways in surface-deposited entities, such as molecules and clusters. Wannier Functions (WFs)are interesting as they can give a different perspective on chemical bonding in materials. Instead of interpreting bonding from the "chemical" Lewis-structure-like prospective, WFs determine the maximal electron density, which correlates well to electron position. This internship will allow me to further develop tools related to the use of supercomputers in the chemical-physical sciences.

Scholar Glenna Dunn

Glenna Dunn (Vanderbilt University; graduate student; physics & astronomy)

Project Title: Forming direct collapse black holes with realistic Lyman-Werner radiation fields in cosmological hydrodynamic simulations

Research Advisor: Kelly Holley-Bockelmann, Department of Astronomy, Vanderbilt Univeristy


HPC Resources: Stampede


While we know that quasars must have formed as massive seeds, the mechanism by which this occurs is unresolved. One popular model that can explain how these objects form is known as direct collapse black hole formation. This model proposes that massive black holes form from the rapid collapse of a pristine gas cloud just as their host galaxies begin to take shape. A significant challenge that this model faces is how such a large quantity of gas, usually ten to one hundred thousand times the mass of our Sun, can collapse without breaking apart into smaller clouds that will form stars. The tipping point between forming a black hole or stars relies on a delicate interplay between gravity, the chemical properties of the gas, the local radiation field, and internal gas physics. These processes are best understood through detailed simulations.

We propose to implement an updated black hole physics module in the massively parallel cosmological hydrodynamic code Gasoline to simulate the formation of direct collapse black holes in the early Universe. We are particularly interested in the role of ambient ultraviolet radiation in a specific band, known as Lyman-Werner (LW) radiation, in creating an environment conducive to direct collapse black hole formation. Previous work, such as that of Shang et al (2009), has shown that if the LW radiation exposure of a primordial gas cloud exceeds a critical threshold, the cloud may meet the physical requirements to collapse into a massive black hole. We intend to use cosmological simulations to study the conditions under which primordial gas clouds experience high levels of LW radiation, and how this radiation affects massive black hole formation.

The first phase of this project will involve the development of a modified black hole formation module that will reflect the physical interplay between the amount of Lyman-Werner radiation felt by a gas cloud and the probability that the cloud will form a black hole. We plan to test this module by implementing it in a low-resolution simulation. The second phase of this project will involve the implementation of the black hole formation module in high-resolution Gasoline simulations. We expect to launch three simulations that explore the parameter space of the critical LW threshold to study how this value changes black hole seed formation, and plan to submit the production- level simulations to the XSEDE/TACC supercomputer Stampede. We intend to use this research to answer some of the open questions regarding the type of environment that allows massive black holes to form. The most highly anticipated results of this work will explore the proximity between LW sources and potential black hole formation sites required for direct collapse to occur. Additional results will offer constraints on massive black hole occupation fraction in the quasar epoch, and implications for reionization, high-redshift X-ray background radiation, and gravitational waves.

Scholar Jorge Alarcon Ochoa

Jorge Alarcon Ochoa (Rensselaer Polytechnic University; undergraduate student; physics)

Project Title: Molecular Dynamics Simulations of Cosolvent Effects on Protein Stability

Research Advisor: Angel Garcia, Department of Physics, Rensselaer Polytechnic University


One of the main issues in molecular modeling is the inability of force fields to properly reproduce experimentally determined thermodynamics and conformations of biomolecules. For example, current force fields fail to reproduce the radius of gyration of proteins in their unfolded states, showing more compact states. This may be the result of overestimation of protein-protein interactions. We can remedy this discrepancy by modifying the interactions between amino acid cosolvents and proteins.

Through Molecular Dynamics simulations we will study cosolvent-protein interactions, and parameterize the force fields utilized for this work by matching extensive experimental data, including osmotic pressure and preferential interaction coefficients.

Scholar Michael Taft

Michael Taft (North Carolina A&T University; chemical engineering)

Project Title: Computational study of adsorption of Cu and Ni Metallic clusters on Titania(TiO2) and Cerium Oxide(CeO2) Surface

Research Advisor: Divi Venkatswarlu, Department of Chemistry and Chemical Engineering North Carolina Agricultural and Technical State University

HPC Resources: Stampede

Hydrogen is an alternative energy resource that can be extracted from methanol for the use in commercial and mobile applications. This process is called methanol steam reforming (MSR). MSR, a catalytic process, has a very complex reaction network with several pathways leading to H2 + CO2 products [1,2,3]. The MSR process takes place in the presence of a metal catalyst/support framework. The structural and chemical properties of the catalysts and support are areas where the reaction efficiency for hydrogen production can be improved. Experimentally, many metal catalysts over various support have been studied. [6,7,8] Yet, these experiments are limited in providing details about the properties and interactions of catalyst and support specifically at the molecular level. Thus, there is an innate desire to capture perceived critical information about those molecular level interactions provided only through a computational approach. Construction of molecular model systems will provide the visual insight for the molecular investigation of catalyst and catalyst-support properties along with their energetic binding behavior.The goals of the project are: (1) to develop energetically stable monometallic Cu and Ni catalysts using a complementary combination of Birmingham Cluster Genetic Algorithm (BCGA) combined with Gupta potential and density functional theory (DFT) calculations using NWchem (2) perform adsorption studies of Cu and Ni clusters on titania (TiO2) and cerium oxide (CeO2) substrates. We will employ periodic DFT calculations using the Quantum Expresso package and employ a basis set of plane waves, ultrasoft pseudopotentials and the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. The results will help us clarify the importance of energetically favorable catalytic framework between catalyst and support, for the overall improvement of methanol steam reforming (MSR).

Scholar Wanda Moses

Wanda Moses (Clemson University; computer science)

Project Title: Petascale Curriculum Intern – GalaxSee

Research Advisor: Aaron Weeden, Shodor

HPC Resources: Stampede, Blue Waters


The intern will be responsible for porting existing curricular materials to the Blue Waters architecture. The materials were previously developed as part of the Blue Waters Undergraduate Petascale Education Program from 2009-2011. The intern will specifically work with the GalaxSee HPC 1 and 2 Modules ( and The intern will revise the documentation, code, and exercises for the modules so that they apply to the Blue Waters system, taking advantage of its unique architecture.


Jovian Lazare

(Clark Atlanta University, Master Student, Chemistry)

Project Title: Computational Study of the Binding of Single and Double Methane with Polycyclic Aromatic Hydrocarbons

Research Advisor: Dinadayalane Tandabany, Department of Chemistry, Clark Atlanta University

Methane storage and sensors are of high importance to world-wide and in particular, Environmental Protection Agency (EPA) because methane is a green-house gas but it is also a source of energy. About 90% of methane gas is produced during formation of coal and capturing for storage after its release due to mining and erosion is therefore a priority. The Environmental Defense Fund called the leak at the Aliso Canyon Gas Storage Field is "one of the largest U.S. natural gas leaks ever recorded". Thousands of families have been forced to move from their homes, with some people complaining of headaches and nosebleeds. According to the EPA, methane traps heat so well therefore its impact on climate change is 25 times greater than carbon dioxide. Most U.S. methane emissions (29 percent) come from the production, processing, storage and distribution of natural gas, according to the EPA, followed by agriculture (26 percent) and landfills (18 percent).

It is important to design materials for methane storage and better sensors. Carbon based materials in particular graphene or graphane could be a potential medium for methane storage and/or sensor. In order to understand the viability of graphene based materials, we propose to explore the single and double methane binding with smaller polycyclic aromatic hydrocarbons (benzene, pyrene, and coronene) that are building blocks for graphene. In our computational study, a systematic investigation of binding of one and two methane with the above-mentioned systems will be carried out by considering several possibilities. All our computations will be performed using the density functional theory (DFT) calculations with double and triple-z basis sets. Since the M06-2X functional is reliable for interactions dominating dispersion forces, we will use that functional. The scholar (Mr. Lazare) has recently started the project with mentor Dr. Tandabany. He is currently using NWChem program package. He has access to the PI, Dr. Tandabany's XSEDE campus champion allocation. In the past three (3) months, he has gained experience in running calculations using Stampede and Gordon. Since several calculations should be performed with various possibilities of binding modes of methane with three systems, XSEDE resources will be very useful for the successful completion of this project. Some of the possibilities of different binding modes of methane with carbon systems are (1) three C-H…p, two C-H…p, one C-H…p, both methane at the same side for pyrene and coronene and both methane at opposite sides of these two systems. After completion of this project, we intend to explore the binding of single and double methane with graphene. The results and the knowledge gained from the smaller polycyclic aromatic systems will be a reference guide as we move to the large graphene system. Our computational study will assist experimentalists in developing new carbon-based materials for methane storage or sensor.  

Quentarius Moore

(Jackson State University, Master Student, Computational Quantum Chemistry)

Project Title: Computational Investigation of the Structural & Electronic Properties of FeS2 Nanoparticle and Consequence on the Formation of Non-Pyrite Sulfide Phases

Research Advisor: Jerzy Leszczynski, Director of the Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Jackson State University

HPC Resources: Stampede

The team of Quentarius Moore and Dr. Jerzy Leszczynski seek to discover knowledge about the binding interactions of two systems, a single-walled carbon nanotube (SWCNT) and  5,10,15,20-tetrakis(pentafluorophenyl)porphyrin (TPPF 20), to exploit the electronic and mechanical properties of SWCNTs. TPPF 20 ,itself a very aromatic material, is a part of a class of large aromatic dyes called porphyrinoids, which have applications in fields such as electronic devices, photovoltaic cells, medicine, and optical sensors.. The team hypothesizes that the two systems of interest will undergo this same non-covalent interaction and has a goal of studying those interactions in order to assess the potential for this novel material to be implemented for photovoltaic, bio-sensing and pollutant absorbent applications. This study will be carried out by utilizing quantum chemical calculations to model and predict the electronic structure and chemical properties of these systems as well as correct for basis set superposition error. Density functional theory (DFT) will be applied to reveal the binding interactions at different sites with the tool, Gaussian09, being used for executing such calculations. The XSEDE Scholars Program facilitates in the access to resources such as Stampede, which is a great improvement over current resource, as the project is of very large systems. More importantly, the program adds training of HPC, exposure to more fields in computational science, and the social aspect needed to take the research project to advanced levels.

Ronald Gonzales

(Idaho State University, Master Student, Mathematics)

Project Title: Modeling Cloud Formation with the Discontinuous Petrov-Galerkin Method

Research Advisor: Yuriy Gryazin, Department of Mathematics, Idaho State University

The goal of this research project is to develop and assess the performance of a new numerical technique that will increase the order of approximation and improve the accuracy of modeling the cloud development process. This project will begin the second week of May 2016 and will conclude mid-August 2016. A high level of mathematics and computer science will be applied in order to achieve this goal.

The learning goals are to obtain the ability to develop advanced numerical processes and gain the proper programming skills to be able to apply these processes to real world problems. As a student, the applicable experience I have is a two semester course in introductory numerical analysis and two introductory courses in C++. My tasks will be to assist in the proof of convergence of the new numerical technique and developing the proper parallelized algorithm.

The advanced numerical methods that this research project aims to develop and assess the performance of are very computationally intensive. This will require the use of C++ and MPI to efficiently parallelize the numerical methods. This will make use of multiple nodes on the Blue Waters supercomputer.

Bernard Scott Jr.

(Georgia State University, Master Student, Biophysical Chemistry)

Project Title: Dynamical Studies on Liver Receptor homolog-1 bound to small molecule effectors

Research Advisor: Ivaylo Ivanov, Department of Microbiology and Immunology, Georgia State University

The nuclear receptor liver receptor homolog-1 (LRH-1), is unique in its use of various phospholipids (PLs) as signaling molecules. Through interaction with PLs and co-regulating peptides, LRH-1 regulates several processes including cell-cycle progression, steroid synthesis, as well as lipid and glucose homeostasis. Specific activators such as dilauroylphosphatidylcholine (DLPC) have been shown to lower serum lipid levels and improve glucose tolerance in diabetic rat models1, making LRH-1 a prime pharmaceutical target. Understanding more about the structural and dynamical details of its regulation at the molecular level can enhance the success of such efforts.

Through molecular dynamics studies (using NAMD) have recently reported a previously unidentified allosteric network running through the core of the LRH-1 ligand binding domain (LBD) The network forms a communication tether between the mouth of the ligand binding pocket and the AF-H/AF2 coregulator-binding region of the LRH-1 LBD2. This tether allows for LRH-1 to coordinate coregulator binding with ligand characteristic (i.e. agonist or antagonist). Furthermore, using principal component analysis (PCA), we have determined that LRH-1's displacement along two specific principal components is determined by the species of bound ligand and coregulator. Taken together, we view this allosteric tether and the accompanied conformational characteristics as dynamical fingerprints that can be used to identify whether a ligand will act in an agonistic or antagonistic role when bound to LRH-1.

We have recently been provided an x-ray crystal structure of LRH-1 bound to a synthetic agonist, Ent-2. I intend to perform analyses on this system along with several others (bound to different compounds) in accord to our earlier work with LRH-1/phospholipid regulation. If the mechanism of activation of LRH-1 by our small molecule mimics that of natural substrates, then strong communication between the ligand binding pocket and AF-H/AF2 is expected. Similarly, if the synthetic agonist impacts LRH-1 in a manner analogous to that of natural agonists, LRH-1 can be expected to reside in a conformational basin consistent with co-activator binding. The principle methods applied to this problem will include classical molecular dynamics with PMEMD followed by dynamic network analysis, and principle component analysis (PCA). The molecular dynamics simulations for these systems will likely be run on Comet. 

Luis Niebla Rios

 (Arizona State University, Undergraduate Student, Earth and Space Exploration)

Project Title: Chemodynamical Cosmological Simulations with RAMSES and KROME

Research Advisor: Evan Scannapieco, School of Earth & Space Exploration, Arizona State University,

This project will provide a description of how the abundances evolve with time on galaxy scales at high redshift. Tables of these abundances as function of basic parameters, such as surface density of gas or SFR, on different time scales can then be implemented in models of the spectra, making valuable predictions for future observations large radio telescopes such as ALMA, JVLA, GBT and LMT, thereby increasing our knowledge of the ISM, in particlar, at the early stages of the Universe. 

Christina Davis

 (Vanderbilt University, Ph.D Student, Astrophysics)

Project Title: Understanding Galaxy Transformation from Flyby Encounters

Research Advisor: Kelly Holley-Bockelmann, Department of Physics and Astronomy, Vanderbilt

Galaxy flybys are transient events where two halos interpenetrate and later detach forever. Although these encounters are surprisingly common, their dynamical effects have been largely ignored. By examin- ing flybys within a cosmological context, we get a better picture of how the encounters shape the nearby galaxies. The Illustris Simulation is a high-resolution hydrodynamical simulation of a (106.5Mpc)3 vol- ume. With 136 snapshots over 13.8 billion years of evolution, the Illustris Simulation provides a large number of halos with a wide variety of interaction histories. The simulation includes physics of star formation and stellar feedback, supermassive black hole growth, AGN and supernova feedback, and gas cooling. We will create a publicly available database of all halos and their interactions that includes flybys. In addition, we will quantify physical changes on both the host and intruder galaxies in a flyby event. Being a state of the art cosmological simulation, the amount of data demands high performance computing techniques to be implemented. Illustris 1 snapshot data is a total of 204 TB with over 4 million galaxies being identified in the final snapshot. In order to identify the flybys in the simulation, we must keep track of every halo and all of it's interactions throughout time. By using parallelization and vectorization, the data can be quickly sorted and optimized for scientific usage. 

Natalie Zimmer

 (Louisiana State University, Undergraduate Student, Physics & Astronomy)

Project Title:  Using GEANT4 for Transition Radiation Simulations to Reduce the Proton-Kaon Contamination Level

Research Advisor: Michael Cherry, Department of Physics and Astronomy,  Louisiana State University.

An upcoming project at CERN will observe high energy (multi-TeV) inelastic proton-proton and proton-heavy ion collisions in the very-forward direction- the region where particles are emitted at very small angles with respect to the incoming particle. In order to identify the protons, pions, and kaons produced in the collisions, a transition radiation detector array must be used. Transition radiation occurs when a charged particle traverses the interface between two media, producing photons in the process. By looking at the energy spectra produced in the transiton radiaiton detectors, protons, pions, and kaons can be distinguished from one another. For this experiment, the misidentification of one species from another must be on the order of 10-4. Preliminary calculations have estimated that pions can be identified reasonably well, but protons and kaons can only be separated at a level of ~10-2. In order to improve this number, the number of x-ray photons produced in the transition radiation of the setup must be increased. The best way to do this is to use GEANT4, a C++ based toolkit designed for building particle simulations, to build simulations of this setup to find the optimal parameters that will maximize the number of x-ray photons produced in the space of 8m allotted for the transition radiation detectors. Through the use of simulations, we will find the ideal transition radiation detector setup to most effectively identify protons, pions, and kaons, which will eventually be built into an experimental setup.

Daniel Dummett Torres

(University of Illinois at Urbana Champaign, PhD Student, Chemistry)

Project Title: DFT Study of Doping and Cation Exchange in CdSe Nanocrystals

Research Advisor: Prashant K. Jain, Department of Chemistry, University of Illinois at Urbana Champaign

My project is aimed at achieving advances in the control of material properties at the nanoscale such as chemical reactivity. Defects in nanoscale solids play an important role in their electronic transport properties, their optical properties, and their chemical reactivity. However, an exact mechanistic/electronic explanation for how defects tune the properties of these materials is largely missing, limiting present understanding. This motivates my theoretical investigation with density functional theory (DFT) in which I sample the potential energy surface made up by the intermediate structures that arise throughout transformations of CdSe and Cu2Se. These electronic structure calculations will help elucidate the origins of cooperativity that has been experimentally observed by our group in the cation exchange reaction of Cd2+ for Cu+ in CdSe and will also describe the cation migration pathways that characterize superionicity in Cu 2Se among other insights that we will gain into the rich properties of these materials.

Mario Bencomo

(Rice University; PhD Student; Computational and Applied Mathematics)

Project Title: Multipole Point Representation and Full Waveform Inversion for (Anisotropic) Seismic Sources

Research Advisor: William Symes, Department of Computational and Applied Mathematics, Rice University

The seismic inversion problem consists of determining information about subsurface geological structures given seismic data. This inversion is posed as a PDE constrained optimization problem where one seeks to find optimal medium (and source) parameters that minimize misfit between observed and predicted data. Accurate mathematical representation and estimation of sources is essential for the recoverability of medium parameters and the main focus of this work. I propose a representation that takes into account anisotropy based on approximating sources via a truncated series of multipole point-sources (MPS). Moreover, my proposed work focuses on two main aspects related to the source estimation subproblem: preconditioning and regularization. Incorporating source estimation in the inversion formulation will undoubtedly come at higher computational cost. It is therefore vital to exploit the parallelizability of core computations for reasonably sized problems: mainly, parallelization over sources for multiple source simulations, and domain decomposition for core finite difference solves.

My proposed work consists of implementing MPS parameter estimation algorithms as part of the seismic inversion software package IWave, developed by the Rice Inversion Project (TRIP) group at Rice University. Incorporating source parameters estimation in the inversion formulation will undoubtedly come at higher computational cost. It is therefore vital for the sake of computational feasibility to exploit the parallelizability of core computations, primarily that of finite difference solves, for reasonably sized problems. Two main modes of parallelization are considered: parallelization over sources for multiple source simulations, and domain decomposition for the core finite difference solvers. 

Maurice Fabien

 (Rice University, PhD Student, Computational and Applied Mathematics)

Project Title: Multilevel methods for Discontinuous Galerkin Discretizations

Research Advisors: Matthew Knepley and Béatrice Rivière, Department of Computational and Applied Mathematics, Rice University

The focus of my research is the application, theory, and computer implementation of numerical methods for partial differential equations that model multiphase flows. More specifically I will investigate the effectiveness of discontinous Galerkin (DG) geometric multigrid (GMG) discretization and solver for PDEs arising from two phase flow and magma dynamics. In addition, the mathematical and numerical analysis of DG–GMG will be explored. Finally, my computer implementation will utilize the many–core architecture that accelerators offer.

The DG discretization is an ideal pairing for GMG, because of its ability to localize computations, handle complex domains, conservation properties, as well as the ease of adapting to high order. High order DG discretizations ensure that accurate simulations are generated, and, also will keep the multi-core and many- core architectures arithmetically saturated.

The DG discretization of PDEs gives rise to large sparse linear systems. Solving these large linear systems efficiently poses a significant challenge. For the past 40 years, there has been no method that is compet itive with geometric multrigrid for solving elliptic PDEs. The geometric multigrid method is well known to offer optimal complexity, that is, it only requires O(N) floating point operations to reduce the error to discretization level. Heterogeneous computing is required if larger simulations are to be generated, or if we want simulations to run faster. Exploring GMG in a heterogeneous computing needs careful consideration because it is inherently a multiplicative algorithm. 


XSEDE Resources Speed Up Electronics Design


By: Jorge Salazar

Scientists at Georgia Tech are using machine learning with supercomputers to analyze the electronic structure of materials and ultimately find ways to build more capable capacitors. (Left) Density functional theory (DFT) charge density of a molecular dynamics snapshot of a benzene. (Right) Charge density difference between machine learning prediction and DFT for the same benzene structure. Credit: Rampi Ramprasad, Georgia Tech.


Capacitors, given their high energy output and recharging speed, could play a major role in powering the machines of the future from electric cars to cell phones. 

But the biggest hurdle for capacitors as energy storage devices is that they store much less energy than a battery of similar size.

Anand Chandrasekaran, a postdoctoral researcher, and Rampi Ramprasad, a professor in the School of Materials Science and Engineering, stand in a room with a high-powered computer dedicated to machine learning. (Credit: Allison Carter)

Researchers at the Georgia Institute of Technology (Georgia Tech) are tackling the problem in a novel way with the help of the National Science Foundation-funded Extreme Science and Engineering Discovery Environment (XSEDE) project. They've combined machine learning with XSEDE-allocated supercomputers to find ways to build more capable capacitors, which could lead to better power management for electronic devices.

The method was described in Nature Partner Journals Computational Materials, published February of 2019. The study involved teaching a computer to analyze at the atomic level two materials, aluminum and polyethylene, used to make some capacitors. 

The researchers focused on finding a way to more quickly analyze the electronic structure of the capacitor materials, looking for features that could affect performance.

"The electronics industry wants to know the electronic properties and structure of all of the materials they use to produce devices, including capacitors," said Rampi Ramprasad, a professor in the School of Materials Science and Engineering at Georgia Tech. 

For example, polyethylene is a very good insulator with a large band gap, the energy range forbidden to electrical charge carriers. But if it has a defect, unwanted charge carriers are allowed into the band gap, reducing efficiency, he said.  

"In order to understand where the defects are and what role they play, we need to compute the entire atomic structure, something that so far has been extremely difficult," said Ramprasad. "The current method of analyzing those materials using quantum mechanics is so slow that it limits how much analysis can be performed at any given time."

Analyzing the electronic structure of a material with quantum mechanics involves solving the Kohn-Sham equation of density functional theory, which generates data on wave functions and energy levels. That data is then used to compute the total potential energy of the system and atomic forces.

Ramprasad and colleagues were awarded allocations of supercomputer resources by XSEDE. "XSEDE was mostly used to develop the training dataset used in our work," said study co-author Deepak Kamal, a graduate student advised by Ramprasad at the Georgia Tech School of Materials Science and Engineering. 

Deepak Kamal, Georgia Tech School of Materials Science and Engineering.

"We had to do a lot of trials to choose the best dataset for the work," Kamal continued, "and later to develop and test our model. This involved hundreds of calculations for generating the structures that we used for the dataset. That included computationally-expensive simulations like ab initio (first principles) molecular dynamics simulations on polyethylene slabs and crystals with about 500 atoms; and aluminum slabs and crystals with about 1,000 atoms. We used XSEDE then because the compute nodes on XSEDE resources are fast and have a large memory associated with them, which makes them ideal for calculations on large structures."

The researchers used the Stampede2 supercomputer at the Texas Advanced Computing Center (TACC), part of the University of Texas at Austin. Stampede2 is an XSEDE-allocated resource capable of 18 petaflops, with 4,200 Intel Knights Landing nodes complemented with 1,736 Intel Xeon Skylake nodes. "We exclusively used Stampede2 for this work. The calculations were very fast and queue time was reasonable as well," Kamal said.

Ramprasad was also awarded supercomputing time through XSEDE on the Comet system of the San Diego Supercomputer Center (SDSC), an Organized Research Unit of the University of California San Diego. Comet is capable of 2.76 petaflops achieved mainly through 1,944 Intel Haswell Standard Compute Nodes. "In the work leading up to the study, we used to use the Comet cluster extensively for high-throughput polymer electronic property calculation, such as the effect of polymer morphology on the band gap of polymers." Kamal said. "We used Comet because it was fast and efficient at handling large number and quantities of calculations."

The results of the quantum mechanical analysis of aluminum and polyethylene on Stampede2 and Comet produced a sample of data, which in turn was used as an input to teach a powerful computer how to simulate that analysis.

Using the new machine learning method developed by Ramprasad and colleagues produced similar results several orders of magnitude faster than using the conventional technique based on quantum mechanics. 

"This unprecedented speedup in computational capability will allow us to design electronic materials that are superior to what is currently out there," Ramprasad said. "Basically we can say, ‘Here are defects with this material that will really diminish the efficiency of its electronic structure.' And once we can address such aspects efficiently, we can better design electronic devices."

Overview of the process used to generate surrogate models for the charge density and density of states. The first step entails the generation of the training dataset by sampling random snapshots of molecular dynamics trajectories. First-principles calculations were then performed on these systems (shown in Figure S1) to obtain the training atomic configurations, charge densities, and local density of states. The scalar (S), vector (V), and tensor (T) fingerprint invariants are mapped to the local electronic structure at every grid-point. For the charge density, this mapping is achieved using a simple fully connected neural network with one output neuron. The local density of states (LDOS) spectrum, on the other hand, is learned via a recurrent neural network architecture, wherein the LDOS at every energy window is represented as a single output neuron (linked via a recurrent layer to other neighboring energy windows). The trained model is then used to predict the electronic structure (i.e, DOS and charge density) of an unseen configuration. Credit: Rampi Ramprasad, Georgia Tech.

While the study focused on aluminum and polyethylene, machine learning could be used to analyze the electronic structure of a wide range of materials. Beyond analyzing electronic structure, other aspects of material structure now analyzed by quantum mechanics could also be hastened by the machine learning approach, Ramprasad said.

"In part we selected aluminum and polyethylene because they are components of a capacitor. But we also demonstrated that you can use this method for vastly different materials, such as metals that are conductors and polymers that are insulators," Ramprasad said.

The faster processing allowed by the machine learning method would also enable researchers to more quickly simulate how modifications to a material will impact its electronic structure, potentially revealing new ways to improve its efficiency.  

Said Kamal: "Supercomputing systems allow high-throughput computing, which enables us to create vast databases of knowledge about various material systems. This knowledge can then be utilized to find the best material for a specific application."

The study, "Solving the electronic structure problem with machine learning," was published in February 2019 in the journal Nature Partner Journals Computational Materials. The authors are Anand Chandrasekaran, Deepak Kamal, Rohit Batra, Chiho Kim, Lihua Chen, and Rampi Ramprasad of the School of Materials Science and Engineering, Georgia Institute of Technology. This research was supported by the Office of Naval Research under grant No. N0014-17-1-2656.

CITATION:  Anand Chandrasekaran, Deepak Kamal, Rohit Batra, Chiho Kim, Lihua Chen and Rampi Ramprasad, "Solving the electronic structure problem with machine learning," (Computational Materials, 2019).

XSEDE awarded scientists access to the Comet supercomputer at the San Diego Supercomputer Center (left) and the Stampede2 supercomputer at the Texas Advanced Computing Center (right).

Original press release from Georgia Tech at this link:


XSEDE resources Stampede1, Stampede2, Comet support multi-fault earthquake research

Scientists are using supercomputers to better predict the behavior of the world's most powerful, multiple-fault earthquakes. A science team used simulations to find dynamic interactions of a postulated network of faults in the Brawley seismic zone in southern California. Map (left panels) and 3D (right panels) view of supercomputer earthquake simulations in the Brawley Seismic Zone, CA. The figure shows how different stress conditions affect rupture propagation across the complex network of faults. The top panels show a high-stress case scenario (leading to very fast rupture propagation, higher than the S wave speed) while the bottom panels show a medium stress case simulation. Credit: Christodoulos Kyriakopoulos, UC Riverside.


By: Jorge Salazar, Texas Advanced Computing Center

Scientists are using supercomputers to better predict the behavior of the world's most powerful, multiple-fault earthquakes. The NSF-funded Extreme Science and Engineering Discovery Environment (XSEDE) project supports multi-fault earthquake research through supercomputer allocations and expertise awarded to researchers.

Multi-fault earthquakes can span fault systems of tens to hundreds of kilometers, with ruptures propagating from one segment to the other. During the last decade, scientists have observed several cases of this complicated type of earthquake. Major examples include the magnitude (M) 7.2 2010 Darfield earthquake in New Zealand; the M7.2 El Mayor – Cucapah earthquake in Mexico immediately south of the US-Mexico border; the 2012 magnitude 8.6 Indian Ocean Earthquake; and perhaps the most complex of all, the M7.8 2015 Kaikoura earthquake in New Zealand. 

A magnitude 7.8 earthquake struck the northeastern South Island of New Zealand on November 14, 2016. The Kaikōura earthquake was the most powerful experienced in the region in more than 150 years. Multi-fault earthquakes in this case ruptured to shake the ground and cause coastal uplift of several meters, shown here. Credit: Mike Locke

"The main findings of our work concern the dynamic interactions of a postulated network of faults in the Brawley seismic zone in Southern California," said Christodoulos Kyriakopoulos, a Research Geophysicist at the University of California, Riverside. He's the lead author of a study published in April of 2019 in the Journal of Geophysical Research, Solid Earth, by the American Geophysical Union. 

The researchers used physics-based dynamic rupture models that allowed them to simulate complex earthquake ruptures using supercomputers. They ran dozens of numerical simulations, and documented a large number of interactions that they analyzed using advanced visualization software, according to Kyriakopoulos.

"This research has provided us with a new understanding of a complex set of faults in Southern California that have the potential to impact the lives of millions of people in the United States and Mexico," said National Science Foundation Earth Sciences Program Director Eva Zanzerkia. "Ambitious computational approaches, such as those undertaken by this research team in collaboration with XSEDE, make more realistic physics-based earthquake models possible."

Modeling realistic earthquakes on a computer isn't easy. Kyriakopoulos and his collaborators faced three main challenges. "The first challenge was the implementation of these faults in the finite element domain, in the numerical model. In particular, this system of faults consists of an interconnected network of larger and smaller segments that intersect each other at different angles. It's a very complicated problem," Kyriakopoulos said. 

Christodoulos Kyriakopoulos, University of California, Riverside. Credit: UC Riverside.

The second challenge was to run dozens of large computational simulations. "We had to investigate as much as possible a very large part of parameter space. The simulations included the prototyping and the preliminary runs for the models. The Stampede1 supercomputer at TACC was our strong partner in this first and fundamental stage in our work because it gave me the possibility to run all these initial models that helped me set my path for the next simulations," Kyriakopoulos said. 

The third challenge was to use optimal tools to properly visualize the 3-D simulation results, which in their raw form consist simply of huge arrays of numbers. Kyriakopoulos did that by generating photorealistic rupture simulations using the freely available software ParaView ( 

Example figure from the paper showing different patterns of interaction between main faults and cross faults as evidenced by the final fault slip. Rupture with cross faults prestressed at a higher level than the main faults. Left panels show models with nucleation to the north (N2S propagation) while right panel show nucleation to the south (S2N propagation). Credit: Christodoulos Kyriakopoulos, UC Riverside.

To overcome these challenges, Kyriakopoulos and colleagues used the resources of XSEDE, including Stampede1 and Stampede2 at the Texas Advanced Computing Center; and Comet at the San Diego Supercomputer Center (SDSC). Kyriakopoulos' ongoing research includes XSEDE allocations on TACC's Stampede2 system.

Approximately one-third of the simulations for this work were done on Stampede1, specifically, the early stages of the work. 

"This work was developed over the last three years, so it's a long project," Kyriakopoulos said. "I'd like to emphasize how the first simulations, again, the prototyping of the models, are very important for a group of scientists that have to methodically plan their time and effort. Having available time on Stampede was a game-changer for me and my colleagues because it allowed me to set the right conditions for the entire set of simulations. To that, I would like to add that Stampede and in general XSEDE is a very friendly environment and the right partner to have for large-scale computations and advanced scientific experiments."

Their team also used the Comet system of SDSC in this research, mostly for test runs and prototyping. 

"My overall experience with SDSC is very positive. I'm very satisfied from the interaction with the support team that was always very fast in responding to my emails and requests for help. This is very important for an ongoing investigation, especially in the first stages where you're making sure that your models work properly. The efficiency of the SDSC support team kept my optimism very high and helped me think positively for the future of my project." 

XSEDE had a big impact on this earthquake research. "The XSEDE support helped me optimize my computational work and organize better the scheduling of my computer runs. Another important aspect is the resolution of problems related to the job scripting and selecting the appropriate resources (e.g amount of RAM, and number of nodes). Based on my overall experience with XSEDE I would say that I saved 10-20% of personal time because of the way XSEDE is organized," Kyriakopoulos said.

"My participation in XSEDE gave a significant boost in my modeling activities and allowed me to explore better the parameter space of my problem. I definitely feel part of a big community that uses supercomputers and has a common goal to push forward science and produce innovation," Kyriakopoulos said.

A dynamic rupture model is a model that allows scientists to study the fundamental physical processes that take place during an earthquake. With this type of model, supercomputers can simulate the interactions between different earthquake faults. For example, the models allow study of how seismic waves travel from one fault to and influence the stability of another fault. In general, Kyriakopoulos said that these types of models are very useful to investigate big earthquakes of the past, and perhaps more importantly, possible earthquake scenarios of the future.

The numerical model Kyriakopoulos developed consists of two main components. First is a finite element mesh that implements the complex network of faults in the Brawley seismic zone. 

"We can think of that as a discretized domain, or a discretized numerical world that becomes the base for our simulations. The second component is a finite element dynamic rupture code, known as FaultMod (Barall et. al. 2009) that allows us to simulate the evolution of earthquake ruptures, seismic waves, and ground motion with time," Kyriakopoulos said. "What we do is create earthquakes in the computer. We can study their properties by varying the parameters of the simulated earthquakes. Basically, we generate a virtual world where we create different types of earthquakes. That helps us understand how earthquakes in the real world are happening."

Looking at the bigger scientific context, Kyriakopoulos said that their research contributed towards a better understanding of multi-fault ruptures, which could lead to better assessments of the earthquake hazard. "If we know how faults interact during earthquake ruptures, we can be better prepared for future large earthquakes—in particular, how several fault segments could interact during an earthquake to enhance or interrupt major ruptures," Kyriakopoulos said.

Some of the results from this research point to the possibility of a multi-fault earthquake in Southern California, which could have dire consequences. 

"Under the current parametrization and the current model assumptions, we found that a rupture on the Southern San Andreas fault could propagate south of Bombay Beach, which is considered to be the southern end of the southern San Andreas fault. In this case, if a rupture actually propagates south of Bombay Beach, it could conceivably sever Interstate 8, which is considered to be a lifeline between the eastern and western California in the case of a large event," Kyriakopoulos said.

"Second, we found that a medium-sized earthquake nucleating on one of these cross faults could actually trigger a major event on the San Andreas fault. It's actually the topic of our ongoing and future work," he added.

Said Kyriakopoulos: "Our planet is a complex physical system. Without the support from supercomputer facilities, we would not be able to numerically represent this complexity and specifically in my field analyze in depth the geophysical processes behind earthquakes."

This video shows a simulation of a dynamic rupture model. The model is based on a postulated network of faults in the Salton Sea area, southern California. The hypocenter of this synthetic event is placed approximately 30km north of Bombay beach. The first seconds of this simulation show the initiation phase of the earthquake, also known as the "nucleation phase". After this initial phase, the earthquake rupture propagates spontaneously towards the right-hand side of the screen (south-east in the map). From that point on what we observe is the interaction between different faults in this system. More specifically, this animation highlights how the network of perpendicular faults (known as cross-faults) implemented in the middle of the domain affect the evolution of this synthetic earthquake. The top two panels represent a different scenario than the bottom two panels. The difference between the top and bottom panels lies in the tendency of the cross-faults to participate in the rupture process, which is significantly higher in the second case. For that reason, in the bottom panels, we observe a cascade of cross-faults events that in the end will modulate the final magnitude of this specific model.


The study, "Dynamic Rupture Scenarios in the Brawley Seismic Zone, Salton Trough, Southern California," was published in April of 2019 in the Journal of Geophysical Research: Solid Earth, published by the American Geophysical Union. Funding was provided by the U.S. Geological Survey (G12AC20038), the National Science Foundation (EAR‐1033462, EAR‐1114446, EAR‐1135455), and the Southern California Earthquake Center (8889). The study co-authors are Christodoulos Kyriakopoulos and David Oglesby of the University of California, Riverside; Thomas Rockwell of San Diego State University; Aron Meltzner of Nanyang Technological University, Singapore; Michael Barall of Invisible Software, Inc.; John Fletcher of the Centro de Investigacion Cientifica y de Educacion Superior de Ensenada (Mexico); and Drew Tulanowski of Rutgers University. 

Stampede1, Stampede2, Comet, and the Extended Collaborative Support Services program are allocated resources of the Extreme Science and Engineering Discovery Environment (XSEDE) funded by the National Science Foundation (NSF).

Advanced Computing for Social Change Institute

Learn. Collaborate. Change the World.

Learning through ACSCI

With support from XSEDE, the Advanced Computing for Social Change Institute offers two unique opportunities for undergraduate students who want to enhance their skillset and create positive change in their community.

The programs recruit students from diverse disciplines and backgrounds who want to work collaboratively to:

  • Learn to apply data analysis and computational thinking to a social challenge
  • Experience the latest tools and techniques for exploring data through visualization
  • Expand skills in team-based problem solving
  • Learn how to communicate ideas more effectively to the general public

To be eligible for these exciting programs, you must:

  • Be currently enrolled as a full time undergraduate student at an accredited college/university
  • Be a U.S. citizen or permanent resident of the United States (for ACSC only)
  • Not plan to graduate before May 2020
  • Have a minimum overall GPA of at least 2.5/4.0 (or equivalent)
  • Be able to attend a full challenge or competition during program dates
  • Complete the online application form before the deadline

Students from any undergraduate background are eligible, although some preference will be given to women, minorities, students from majors outside computer science, and students at the sophomore or junior level.

Students will be assigned to teams to ensure a balance of backgrounds, and an advisor will be assigned to each team. The costs of airfare, lodging, meals, and conference registration will be provided.

Application Period Closed

The next Advanced Computing for Social Change (ACSC) event will held in Chicago, IL co-located with the PEARC19 conference July 27-Aug 1, 2019.

APPLICATION DEADLINE: The application for this event has passed. Notification of acceptance will be sent in June 2019.

Visit the ACSC FAQ for details.

The next Computing4Change (C4C) event will be held in Denver, CO co-located with the SC19 conference Nov 16-22, 2019.

APPLICATION DEADLINE: The application deadline for this event has passed. Notification of acceptance will be sent in June 2019.

Key Points
Developing a Diverse Workforce
Infusing Computational Science
Expanding Instructional Resources
Contact Information

Getting Started

If you are a US-based researcher and currently use, or want to use, advanced research computing resources and services, XSEDE can help. Whether you intend to use XSEDE-allocated resources or resources elsewhere, the XSEDE program works to make such resources easier to use and help more people use them. Once you are ready to take the next step, you can become an XSEDE user in a matter of minutes and be on your way to taking your computational activities to the next level.

Discover and Explore

If you haven't already, now would be a good time to explore the primary resources offered through XSEDE to understand which resource or resources best meet your computational needs and research objectives. Don't forget to check out the Science Gateways Listing, which might provide the tools you need in a convenient, easy-to-use Web interface.

Access to Resources

You can request to use the nearly two dozen resources allocated via XSEDE. Almost all U.S.-based university and non-profit researchers are eligible to request resource allocations, at no cost to you. Allocations are available if you are just getting started in computational science, visualization, or data analysis, if you are an experienced HPC user with large-scale needs or if you are deploying a science gateway. XSEDE also supports allocations for academic courses and training activities. XSEDE provides in depth support for all of these activities through its Extended Collaborative Support Services program.

Become an XSEDE User

If you're ready to join the thousands of other XSEDE users, create your XSEDE User Portal account. The XSEDE User Portal is your single point of entry to all things XSEDE, and your XSEDE User Portal accounts unlocks your ability to submit allocation requests, get up-to-date information on your existing projects, subscribe to user news and announcements, and register for training classes. Once you've got your portal account, check out the Getting Started Guide for what to do next.

Support Services

XSEDE's Extended Collaborative Support Services (ECSS) encompasses more than 80 computational scientists with experience in a wide range of research domains. You can ask to work with these experts on your computational projects in collaborations lasting up to a year to help you advance your use of XSEDE ecosystem resources. A related effort, the Novel and Innovative Projects (NIP) effort provides mentoring to scientists, scholars, and educators from disciplines that are just beginning to use advanced computing infrastructure.


XSEDE offers live and recorded training classes to teach you how to maximize your productivity in the XSEDE ecosystem. Consider listening to the most recent XSEDE New User Training course, in which XSEDE staff walk through the basics of the XSEDE ecosystem and describe how we've set up the environment for users. Many training classes focus on specific systems and software supported in the XSEDE ecosystem, while other classes cover general topics in programming principles, visualization, data management, science gateways, and more.

Getting Help

We realize this is a lot to take in, so XSEDE has lots of help options. You might begin by searching for your XSEDE Campus Champion. Your local champion can direct you to the staff or resources you need. This robust community of research computing professionals is spread out over 300 member institutions.

Still not sure if XSEDE is right for you? The XSEDE Help Desk staff will answer your question or put you in touch with the experts who can. XSEDE has numerous ways to search for information and get help on a variety of topics. Our staff can help with problems such as choosing a platform or resource, choosing a software package, improving file transfer performance, and help with visualization. Send your questions to .

Key Points
XSEDE provides live and recorded training on a wide range of research computing topics.
XSEDE programs offer our users in-depth collaborations and on-campus facilitators.
Most US-based researchers are eligible for no-cost XSEDE allocations. Get started in two weeks or less!
Contact Information

Current Campus Champions

Current Campus Champions listed by institution. Participation as either an Established Program to Stimulate Competitive Research (EPSCoR) or as a minority-serving institution (MSI) is also indicated.

Campus Champion Institutions  
Total Academic Institutions 268
     Academic institutions in EPSCoR jurisdictions 79
    Minority Serving Institutions 53
    Minority Serving Institutions in EPSCoR jurisdictions 18
Non-academic, not-for-profit organizations 31
Total Campus Champion Institutions 299
Total Number of Champions 614

LAST UPDATED: June 18, 2019

See also the lists of Leadership Team and Regional LeadersDomain Champions and Student Champions.

Institution Campus Champions EPSCoR MSI
Alabama A & M University Damian Clarke, Raziq Yaqub
Albany State University Olabisi Ojo  
Arizona State University Michael Simeone (domain) , Sean Dudley, Barnaby Wasson, Johnathan Lee, Lee Reynolds, William Dizon, Jorge Henriquez, Ian Shaeffer, Dalena Hardy, Gil Speyer, Sirong Lu, Richard Gould    
Arkansas State University Hai Jiang  
Auburn University Tony Skjellum  
Austin Peay State University Justin Oelgoetz    
Bates College Kai Evenson  
Baylor College of Medicine Pavel Sumazin , Hua-Sheng Chiu, Hyunjae Ryan Kim    
Baylor University Mike Hutcheson, Carl Bell, Brian Sitton    
Bentley University Jason Wells    
Bethune-Cookman University Ahmed Badi  
Boise State University Kyle Shannon, Mike Henry (student), Jason Watt, Kelly Byrne, Mendi Edgar  
Boston Children's Hospital Arash Nemati Hayati    
Boston University Wayne Gilmore, Charlie Jahnke, Augustine Abaris, Shaohao Chen, Brian Gregor, Katia Oleinik, Jacob Pessin    
Bowdoin College Dj Merrill  
Brandeis University John Edison    
Brown University Helen Kershaw, Maximilian King, Paul Hall, Khemraj Shukla, Mete Tunca, Paul Stey  
California Baptist University Linn Carothers  
California Institute of Technology Tom Morrell    
California State Polytechnic University-Pomona Chantal Stieber    
California State University-Sacramento Anna Klimaszewski-Patterson  
Carnegie Institution for Science Floyd A. Fayton, Jr.    
Carnegie Mellon University Bryan Webb, Franz Franchetti    
Case Western Reserve University Roger Bielefeld, Hadrian Djohari, Emily Dragowsky, James Michael Warfe, Sanjaya Gajurel    
Centre College David Toth  
Children's Research Institute, Children's Mercy Kansas City Shane Corder    
Citadel Military College of South Carolina John Lewis  
Claremont McKenna College Jeho Park    
Clark Atlanta University Dina Tandabany  
Clarkson Univeristy Jeeves Green, Joshua A. Fiske    
Clemson University Marcin Ziolkowski, Xizhou Feng, Ashwin Srinath, Jeffrey Denton, Corey Ferrier  
Cleveland Clinic Foundation Iris Nira Smith, Daniel Blankenberg    
Clinton College Terris S. Riley
Coastal Carolina University Will Jones, Thomas Hoffman  
Colby College Randall Downer  
College of Charleston Berhane Temelso  
College of Staten Island CUNY Sharon Loverde  
College of William and Mary Eric Walter    
Colorado School of Mines Torey Battelle    
Columbia University Rob Lane, George Garrett, John Villa    
Complex Biological Systems Alliance Kris Holton    
Cornell University Susan Mehringer    
Dakota State University David Zeng  
Dillard University Tomekia Simeon, Brian Desil (student), Priscilla Saarah (student)
Doane University-Arts & Sciences Adam Erck, Mark Meysenburg  
Dominican University of California Randall Hall    
Drexel University David Chin    
Duke University Tom Milledge    
Earlham College Charlie Peck    
Federal Reserve Bank Of Kansas City (CADRE) BJ Lougee, Chris Stackpole    
Federal Reserve Bank Of Kansas City (CADRE) - OKC Branch Greg Woodward  
Federal Reserve Bank Of New York Ernest Miller, Kevin Kelliher    
Felidae Conservation Fund Kevin Clark    
Ferris State University Luis Rivera, David Petillo    
Fisk University Michael Watson  
Florida A and M University Hongmei Chi, Temilola Aderibigbe (student), George Kurian (student), Jesse Edwards, Stacyann Nelson (student)  
Florida Atlantic University Rhian Resnick    
Florida International University David Driesbach, Cassian D'Cunha  
Florida Southern College Christian Roberson    
Florida State University Paul van der Mark    
Francis Marion University K. Daniel Brauss, Jordan D. McDonnell
George Mason University Jayshree Sarma, Jeffrey Bassett, Alastair Neil    
George Washington University Hanning Chen, Adam Wong, Glen Maclachlan, William Burke    
Georgetown University Alisa Kang    
Georgia Institute of Technology Mehmet Belgin, Semir Sarajlic, Nuyun (Nellie) Zhang, Sebastian Kayhan Hollister (student)    
Georgia Southern University Brandon Kimmons    
Georgia State University Neranjan "Suranga" Edirisinghe Pathiran, Ken Huang, Thakshila Herath (student), Melchizedek Mashiku (student)  
Gettysburg College Charles Kann    
Great Plains Network Kate Adams, James Deaton    
Harvard Medical School Jason Key    
Harvard University Scott Yockel, Plamen Krastev, Francesco Pontiggia    
Harvey Mudd College Aashita Kesarwani    
Hood College Xinlian Liu    
Howard University Marcus Alfred  
Idaho National Laboratory Ben Nickell, Eric Whiting, Tami Grimmett  
Idaho State University Keith Weber, Randy Gaines, Dong Xu  
Illinois Institute of Technology Jeff Wereszczynski    
Indiana University Abhinav Thota, Sudahakar Pamidighantam (domain) , Junjie Li, Thomas Doak (domain) , Carrie L. Ganote (domain) , Sheri Sanders (domain) , Bhavya Nalagampalli Papudeshi (domain) , Le Mai Weakley    
Indiana University of Pennsylvania John Chrispell    
Iowa State University Andrew Severin, James Coyle, Levi Baber, Justin Stanley (student)    
Jackson State University Carmen Wright, Duber Gomez-Fonseca (student)
James Madison University Yasmeen Shorish, Isaiah Sumner    
John Brown University Jill Ellenbarger  
Johns Hopkins University Anthony Kolasny, Jaime Combariza, Jodie Hoh (student)    
Juniata College Burak Cem Konduk    
KINBER Jennifer Oxenford    
Kansas Research and Education Network Casey Russell  
Kansas State University Dan Andresen, Mohammed Tanash (student), Kyle Hutson  
Kennesaw State University Dick Gayler, Jon Preston    
Kentucky State University Chi Shen
Lafayette College Bill Thompson, Jason Simms    
Lamar University Larry Osborne    
Langston University Franklin Fondjo, Abebaw Tadesse, Joel Snow
Lawrence Berkeley National Laboratory Andrew Wiedlea    
Lawrence Livermore National Laboratory Todd Gamblin    
Lehigh University Alexander Pacheco    
Lock Haven University Kevin Range    
Louisiana State University Feng Chen, Blaise A Bourdin  
Louisiana State University Health Sciences Center-New Orleans Mohamad Qayoom  
Louisiana Tech University Don Liu  
Marquette University Craig Struble, Lars Olson, Xizhou Feng    
Marshall University Jack Smith, Justin Chapman  
Massachusetts Green High Performance Computing Center Julie Ma    
Massachusetts Institute of Technology Christopher Hill, Lauren Milechin    
Medical University of South Carolina Starr Hazard  
Michigan State University Andrew Keen, Yongjun Choi, Dirk Colbry    
Michigan Technological University Gowtham    
Middle Tennessee State University Dwayne John    
Midwestern State University Eduardo Colmenares-Diaz (student), Broday Walker    
Mississippi State University Trey Breckenridge  
Missouri State University Matt Siebert    
Missouri University of Science and Technology Buddy Scharfenberg, Don Howdeshell    
Monmouth College Christopher Fasano    
Montana State University Jonathan Hilmer  
Montana Tech Bowen Deng  
Morehouse College Jigsa Tola, Doreen Stevens  
NCAR/UCAR Davide Del Vento    
National University Ali Farahani    
Navajo Technical University Jason Arviso
New Jersey Institute of Technology Glenn "Gedaliah" Wolosh, Roman Voronov    
New Mexico State University Alla Kammerdiner, Diana Dugas, Strahinja Trecakov, Matt Henderson
New York University Shenglong Wang    
North Carolina A & T State University Dukka KC  
North Carolina Central University Caesar Jackson, Alade Tokuta  
North Carolina State University at Raleigh Lisa Lowe    
North Dakota State University Dane Skow, Nick Dusek, Oluwasijibomi "Siji" Saula, Khang Hoang  
Northern Arizona University Christopher Coffey    
Northern Illinois University Jifu Tan    
Northwest Missouri State University Jim Campbell    
Northwestern State University (Louisiana Scholars' College) Brad Burkman  
Northwestern University Pascal Paschos, Alper Kinaci    
OWASP Foundation Learning Gateway Project Bev Corwin, Laureano Batista, Zoe Braiterman, Noreen Whysel    
Ohio State University Keith Stewart, Sandy Shew    
Ohio Supercomputer Center Karen Tomko    
Oklahoma Innovation Institute John Mosher  
Oklahoma State University Dana Brunson, Brian Couger (domain) , Jesse Schafer, Raj Shukla (student), Christopher J. Fennell (domain) , Phillip Doehle, Evan Linde, Venkat Padmanapan Rao (student), Nathalia Graf Grachet (student)  
Old Dominion University Rizwan Bhutta, Wirawan Purwanto    
Oregon State University David Barber, Chuck Sears, Todd Shechter, CJ Keist    
Penn State University Wayne Figurelle, Guido Cervone, Diego Menendez    
Pittsburgh Supercomputing Center Stephen Deems, John Urbanic    
Pomona College Asya Shklyar    
Portland State University William Garrick    
Princeton University Ian Cosden    
Purdue University Xiao Zhu, Tsai-wei Wu, Matthew Route (domain) , Stephen Harrell, Marisa Brazil, Eric Adams (domain)    
Reed College Trina Marmarelli, Johnny Powell    
Rensselaer Polytechnic Institute Joel Giedt, James Flamino (student)    
Rhodes College Brian Larkins    
Rice University Qiyou Jiang, Erik Engquist, Xiaoqin Huang, Clinton Heider, John Mulligan    
Rochester Institute of Technology Andrew W. Elble , Emilio Del Plato, Charles Gruener, Paul Mezzanini, Sidney Pendelberry    
Rowan University Ghulam Rasool    
Rutgers University Kevin Abbey, Shantenu Jha, Bill Abbott, Leslie Michelson, Paul Framhein, Galen Collier, Eric Marshall, Kristina Plazonic, Vlad Kholodovych    
SBGrid Consortium      
SUNY at Albany Kevin Tyle, Nicholas Schiraldi    
Saint Louis University Eric Kaufmann, Frank Gerhard Schroer IV (student)    
Saint Martin University Shawn Duan    
San Diego State University Mary Thomas  
San Jose State University Sen Chiao, Werner Goveya    
Slippery Rock University of Pennsylvania Nitin Sukhija    
Smithsonian Conservation Biology Institute Jennifer Zhao    
Sonoma State University Mark Perri  
South Carolina State University Biswajit Biswal, Jagruti Sahoo
South Dakota School of Mines and Technology Rafal M. Oszwaldowski  
South Dakota State University Kevin Brandt, Maria Kalyvaki  
Southeast Missouri State University Marcus Bond    
Southern Connecticut State University Yigui Wang    
Southern Illinois University-Carbondale Shaikh Ahmed, Chet Langin, Majid Memari (student), Aaron Walber (student)    
Southern Illinois University-Edwardsville Kade Cole, Andrew Speer    
Southern Methodist University Amit Kumar, Merlin Wilkerson, Robert Kalescky    
Southern University and A & M College Shizhong Yang, Rachel Vincent-Finley
Southwest Innovation Cluster Thomas MacCalla    
Southwestern Oklahoma State University Jeremy Evert  
Spelman College Yonas Tekle  
Stanford University Ruth Marinshaw, Zhiyong Zhang    
Swarthmore College Andrew Ruether    
Temple University Richard Berger    
Tennessee Technological University Tao Yu, Mike Renfro    
Texas A & M University-College Station Rick McMullen, Dhruva Chakravorty    
Texas A & M University-Corpus Christi Ed Evans, Joshua Gonzalez  
Texas A&M University-San Antonio Smriti Bhatt  
Texas Southern University Farrukh Khan  
Texas State University Shane Flaherty  
Texas Wesleyan University Terrence Neumann    
The College of New Jersey Shawn Sivy    
The Jackson Laboratory Shane Sanders  
The University of Tennessee-Chattanooga Craig Tanis, Ethan Hereth, Carson Woods (student)    
The University of Texas at Austin Kevin Chen    
The University of Texas at Dallas Frank Feagans, Gi Vania, Jaynal Pervez, Christopher Simmons    
The University of Texas at El Paso Rodrigo Romero, Vinod Kumar  
The University of Texas at San Antionio Brent League, Jeremy Mann, Zhiwei Wang, Armando Rodriguez, Thomas Freeman  
Tinker Air Force Base Zachary Fuchs, David Monismith  
Trinity College Peter Yoon    
Tufts University Shawn Doughty, Georgios (George) Karamanis (student)    
Tulane University Hideki Fujioka, Hoang Tran, Carl Baribault  
United States Department of Agriculture - Agriculture Research Service Nathan Weeks    
United States Geological Survey Janice Gordon, Jeff Falgout, Natalya Rapstine    
University at Buffalo Cynthia Cornelius    
University of Alabama at Birmingham John-Paul Robinson  
University of Alaska Fairbanks Liam Forbes, Kevin Galloway
University of Arizona Jimmy Ferng, Mark Borgstrom, Michael Bruck, Moe Torabi, Adam Michel, Chris Reidy, Chris Deer, Cynthia Hart, Ric Anderson, Todd Merritt, Dima Shyshlov, Blake Joyce    
University of Arkansas David Chaffin, Jeff Pummill, Pawel Wolinski, James McCartney, Timothy "Ryan" Rogers (student)  
University of Arkansas at Little Rock Albert Everett  
University of California-Berkeley Aaron Culich, Steve Masover , Chris Paciorek    
University of California-Davis Bill Broadley    
University of California-Irvine Harry Mangalam  
University of California-Los Angeles TV Singh    
University of California-Merced Jeffrey Weekley, Sarvani Chadalapaka, Luanzheng Guo (student)    
University of California-Riverside Bill Strossman, Charles Forsyth  
University of California-San Diego Cyd Burrows-Schilling, Claire Mizumoto    
University of California-San Francisco Jason Crane    
University of California-Santa Barbara Sharon Solis, Sharon Tettegah  
University of California-Santa Cruz Shawfeng Dong  
University of Central Florida Paul Wiegand, Amit Goel (student), Jason Nagin    
University of Central Oklahoma Evan Lemley  
University of Chicago Igor Yakushin    
University of Cincinnati      
University of Colorado Thomas Hauser, Shelley Knuth, Andy Monaghan    
University of Delaware Anita Schwartz, Parinaz Barakhshan (student)  
University of Florida Alex Moskalenko, David Ojika    
University of Georgia Guy Cormier    
University of Guam Rommel Hidalgo, Eugene Adanzo, Randy Dahilig, Jose Santiago, Steven Mamaril
University of Hawaii Gwen Jacobs, Sean Cleveland
University of Houston Jerry Ebalunode, Amit Amritkar (domain)  
University of Houston-Clear Lake David Garrison, Liwen Shih    
University of Houston-Downtown Eashrak Zubair (student), Hong Lin  
University of Idaho Lucas Sheneman  
University of Illinois at Chicago Himanshu Sharma, Jon Komperda, Babak Kashir Taloori (student)  
University of Illinois at Urbana-Champaign Mao Ye (domain) , Rob Kooper (domain) , Dean Karres, Tracy Smith    
University of Indianapolis Steve Spicklemire    
University of Iowa Ben Rogers, Baylen Jacob Brus (student), Sai Ramadugu, Adam Harding, Joe Hetrick, Cody Johnson, Genevieve Johnson, Glenn Johnson, Brendel Krueger, Kang Lee, Gabby Perez, Brian Ring, John Saxton    
University of Kansas Riley Epperson  
University of Kentucky Vikram Gazula, James Griffioen  
University of Louisiana at Lafayette Raju Gottumukkala  
University of Louisville Harrison Simrall  
University of Maine System Bruce Segee, Steve Cousins, Michael Brady Butler (student)  
University of Maryland Eastern Shore Urban Wiggins  
University of Maryland-Baltimore County Roy Prouty, Randy Philipp  
University of Maryland-College Park Kevin M. Hildebrand  
University of Massachusetts Amherst Johnathan Griffin    
University of Massachusetts-Boston Jeff Dusenberry, Runcong Chen  
University of Massachusetts-Dartmouth Scott Field, Gaurav Khanna    
University of Memphis Qianyi Cheng    
University of Miami Dan Voss, Warner Baringer    
University of Michigan Brock Palen, Simon Adorf (student), Gregory Teichert (student)    
University of Minnesota Jim Wilgenbusch, Eric Shook (domain) , Ben Lynch, Eric Shook, Joel Turbes, Doug Finley    
University of Missouri-Columbia Timothy Middelkoop, Jacob Gotberg, Micheal Quinn, Alexander Barnes (student)    
University of Missouri-Kansas City Paul Rulis, Derek Howard, Asif Ahamed Magdoom Ali, Brian Marxkors    
University of Montana Tiago Antao  
University of Nebraska Adam Caprez, Jingchao Zhang  
University of Nebraska Medical Center Ashok Mudgapalli  
University of Nevada-Reno Fred Harris, Scotty Strachan, Engin Arslan  
University of New Hampshire Scott Valcourt  
University of New Mexico Hussein Al-Azzawi, Matthew Fricke
University of North Carolina Mark Reed, Mike Barker    
University of North Carolina Wilmington Eddie Dunn, Ellen Gurganious, Cory Nichols Shrum (student)    
University of North Dakota Aaron Bergstrom  
University of North Georgia Luis A. Cueva Parra    
University of North Texas Charles Peterson, Damiri Young    
University of Notre Dame Dodi Heryadi, Scott Hampton    
University of Oklahoma Henry Neeman, Kali McLennan, Horst Severini, James Ferguson, David Akin, S. Patrick Calhoun, George Louthan, Jason Speckman  
University of Oregon Nick Maggio, Robert Yelle, Jake Searcy, Mark Allen, Michael Coleman    
University of Pennsylvania Gavin Burris    
University of Pittsburgh Kim Wong, Matt Burton, Fangping Mu, Shervin Sammak    
University of Puerto Rico Mayaguez Ana Gonzalez
University of Richmond Fred Hagemeister    
University of South Carolina Paul Sagona, Ben Torkian, Nathan Elger  
University of South Dakota Doug Jennewein, Adison Ann Kleinsasser (student)  
University of South Florida-St Petersburg (College of Marine Science) Tylar Murray    
University of Southern California Virginia Kuhn (domain) , Cesar Sul, Erin Shaw    
University of Southern Mississippi Brian Olson , Gopinath Subramanian  
University of Tulsa Peter Hawrylak  
University of Utah Anita Orendt, Tom Cheatham (domain) , Brian Haymore (domain)    
University of Vermont Andi Elledge, Yves Dubief  
University of Virginia Ed Hall, Katherine Holcomb    
University of Washington-Seattle Campus Nam Pho    
University of Wisconsin-La Crosse David Mathias, Samantha Foley    
University of Wisconsin-Milwaukee Dan Siercks, Jason Bacon, Shawn Kwang    
University of Wyoming Bryan Shader, Rajiv Khadka (student)  
University of the Virgin Islands Marc Boumedine
Utah Valley University George Rudolph    
Valparaiso University Paul Lapsansky, Paul M. Nord, Nicholas S. Rosasco    
Vanderbilt University Will French    
Vassar College Christopher Gahn    
Virginia Tech University James McClure, Alana Romanella, Srijith Rajamohan, David Barto (student)    
Washburn University Karen Camarda, Steve Black  
Washington State University Rohit Dhariwal, Peter Mills    
Washington University in St Louis Xing Huang, Matt Weil, Matt Callaway    
Wayne State University Patrick Gossman, Michael Thompson, Aragorn Steiger    
Weill Cornell Medicine Joseph Hargitai    
West Chester University of Pennsylvania Linh Ngo, Jon C. Kilgannon (student)    
West Virginia Higher Education Policy Commission Jack Smith  
West Virginia State University Sridhar Malkaram
West Virginia University Don McLaughlin, Nathan Gregg, Guillermo Avendano-Franco  
West Virginia University Institute of Technology Sanish Rai  
Wichita State University Terrance Figy  
Winston-Salem State University Xiuping Tao, Daniel Caines (student)  
Wofford College Beau Christ  
Woods Hole Oceanographic Institution Roberta Mazzoli, Wei-Hao (Andrei) Huang    
Yale University Andrew Sherman, Kaylea Nelson, Benjamin Evans    
Youngstown State University Feng George Yu    

LAST UPDATED: June 18, 2019


Key Points
Contact Info
Contact Information

Champion Leadership Team

This page includes the Champions Leadership team and Regional Champions

Champion Leadership Team
Name Institution Position
Dana Brunson Internet2 Campus Engagement Co-manager
Henry Neeman University of Oklahoma Campus Engagement Co-manager
Marisa Brazil Purdue University Champion Coordinator
Jeff Pummill University of Arkansas Champion Science Coordinator
Jay Alameda University of Illinois Urbana-Champaign Champion Technology Coordinator
Aaron Culich University of California-Berkeley Champion Leadership Team (2017-2019)
Doug Jennewein University of South Dakota Champion Leadership Team (2018-2020)
Timothy Middelkoop University of Missouri Champion Leadership Team (2018-2020)
Julie Ma MGHPCC Champion Leadership Team (2018-2020)
Hussein Al-Azzawi University of New Mexico Champion Leadership Team (2018-2020)
Shelley Knuth University of Colorado Champion Leadership Team (2019-2021)
BJ Lougee Federal Reserve Bank of Kansas (CADRE) Champion Leadership Team (2019-2021)
Torey Battelle Colorado School of Mines Champion Leadership Team (2019-2021)
Leadership Team Alumni    
Jack Smith West Virginia Higher Education Policy Commission  Champion Leadership Team (2016-2018)
Dan Voss University of Miami Champion Leadership Team (2016-2018)
Erin Hodges University of Houston Champion Leadership Team (2017-2018)
Alla Kammerdiner New Mexico State University Champion Leadership Team (2017-2019)

Updated: June 18, 2019

Regional Champions

The Regional Champion Program is built upon the principles and goals of the XSEDE Champion Program. The Regional Champion network facilitates education and training opportunities for researchers, faculty, students and staff in their region that help them make effective use of local, regional and national digital resources and services. Additionally, the Regional Champion Program provides oversight and assistance in a predefined geographical region to ensure that all Champions in that region receive the information and assistance they require, as well as establish a bi-directional conduit between Champions in the region and the XSEDE champion staff, thus ensuring a more efficient dissemination of information, allowing finer grained support. Finally, the Regional Champions acts as a regional point of contact and coordination, to assist in scaling up the Champion program by working with the champion staff to coordinate and identify areas of opportunity for expanding outreach to the user community.

Regional Champions are coordinated by Jeff Pummill.

Ben Nickell Idaho National Labs Nick Maggio University of Oregon 1
Ruth Marinshaw Stanford University Aaron Culich University of California, Berkeley 2
Kevin Brandt South Dakota State University  Chet Langin Southern Illinois University 3
Dan Andresen Kansas State University BJ Lougee Federal Reserve Bank Of Kansas City CADRE  4
Mark Reed University of North Carolina Craig Tanis University of Tennessee, Chattanooga 5
Scott Hampton University of Notre Dame Stephen Harrell Purdue University 6
Scott Yockel Harvard University Scott Valcourt University of New Hampshire 7
Anita Orendt University of Utah Shelley Knuth University of Colorado 8

Updated: August 6, 2018


Key Points
Leadership table
Regional Champions table
Contact Information

Student Champions

Campus Champions programs include Regional, Student, and Domain Champions.


Student Champions

Student Champion volunteer responsibilities may vary from one institution to another and depending on your Mentor. Student Champions may work with their Campus Champion Mentor to provide outreach on campus to help users access the best advanced computing resource that will help them accomplish their research goals, provide training to users on campus, or work on special projects assigned by your Mentor. Student Champions are also encouraged to attend the annual PEARC conference and participate in PEARC student program as well as submit posters or papers to the conference. 

Interested in applying to become a Student Champion?

Fill out this form and someone will be in touch soon! (Please note that your institution must be part of the Champions program and you must have a Campus Champion mentor. To check to see if your institution is part of the Champions program and to get in touch a Champion on your campus, check here. Can't find your institution on the list? Fill out the application form and we will work to help you!)


Boise State University Mike Henry Kyle Shannon     2020
Dillard University Priscilla Saarah Tomekia Simeon     2022
Dillard University Brian Desil Tomekia Simeon     2022
Florida A&M Univerisity George Kurian Hongmei Chi      
Florida A&M Univeristy Temilola Aderibigbe Hongmei Chi     2019
Florida A&M Univeristy Stacyann Nelsom Hongmei Chi     2020
Georgia Institute of Technology Sebastian Kayhan Hollister Semir Sarajlic     2021
Georgia State University Kenneth Huang Suranga Naranjan     2020
Georgia State University Thakshila Herath Suranga Naranjan     2018
Georgia State University  Melchizedek Mashiku Neranjan "Suranga" Edirisinghe Pathiran     2022
Iowa State University Justin Stanley Levi Barber     2020
Jackson State University Duber Gomez-Fonseca Carmen Wright     2019
John Hopkins University Jodie Hoh Jaime Combariza, Anthony Kolasny, Kevin Manalo     2022
Kansas State University Mohammed Tanash Dan Andresen     2022
Midwestern State University Broday Walker Eduardo Colmenares     2020
Oklahoma State University Raj Shukla Dana Brunson     2018
Oklahoma State University Venkat Padmanapan Rao Philip Doehle     2019
Oklahom State University  Nathalia Graf Grachet Philip Doehle     2019
Rensselaer Polytechnic Institute James Flamino Joel Geidt     2022
Saint Louis University Frank Gerhard Schroer IV Eric Kaufmann     2021
Southern Illinois University

Majid Memari

Chet Langin     2021
Southern Illinois University Aaron Walber Chet Langin     2020
Tufts University Georgios (George) Karamanis Shawn G. Doughty     2018
The University of Tennessee at Chattanooga Carson Woods Craig Tanis     2021
Univerity of Arkansas Timothy "Ryan" Rogers Jeff Pummill     2021
University of California - Merced Luanzheng Guo Sarvani Chadalapaka     2020
University of Central Florida Amit Goel Paul Weigand      
University of Delaware Parinaz Barakhshan Anita Schwartz     2024
University of Houston-Downtown Eashrak Zubair Hong Lin     2020
University of Illinois at Chicago Babak Kashir Taloori Jon Komperda     2020
University of Iowa Baylen Jacob Brus Ben Rogers     2020
University of Maine Michael Brady Butler Bruce Segee     2022
University of Michigan Simon Adorf Brock Palen     2019
University of Missouri Alexander Barnes Timothy Middelkoop     2018
University of North Carolina Wilmington Cory Nichols Shrum Eddie Dunn      
University of South Dakota Adison Ann Kleinsasser Doug Jennewein     2020
Virginia Tech University David Barto Alana Romanella     2020
West Chester University of Pennsylvania Jon C. Kilgannon Linh Ngo     2020
Winston-Salem State University Daniel Caines Xiuping Tao     2019
Georgia State University Mengyuan Zhu Suranga Naranjan     2017
Jackson State Univeristy Ebrahim Al-Areqi Carmen Wright     2018
Mississippi State University Nitin Sukhija Trey Breckenridge     2015
Oklahoma State University Phillip Doehle Dana Brunson     2016
Rensselaer Polytechnic Institute Jorge Alarcon Joel Geidt     2016
Southern Illinois University Alex Sommers Chet Langin     2018
Southern Illinois University Sai Susheel Sunkara Chet Langin     2018
Southern Illinois University Monica Majiga Chet Langin     2017
Southern Illinois University Sai Sandeep Kadiyala  Chet Langin     2017
Southern Illinois University Rezaul Nishat Chet Langin     2018
Southern Illinois University Alvin Gonzales Chet Langin     2020
University of Arkansas Shawn Coleman Jeff Pummill     2014
University of Florida David Ojika Oleksandr Moskalenko     2018
University of Houston Clear Lake Tarun Kumar Sharma Liwen Shih     2014
University of Maryland Baltimore County Genaro Hernadez Paul Schou     2015
University of North Carolina Wilmington James Stinson Gray Eddie Dunn     2018
University of South Dakota Joseph Madison Doug Jennewein     2018
University of Pittsburgh Shervin Sammak Kim Wong     2016
Virginia Tech University Lu Chen Alana Romanella     2017

Updated: June 17, 2019


Key Points
Student Champions
Regional Champions
Domain Champions
Contact Information

XSEDE Partnerships

Check out the partner institutions funded by the XSEDE Project:

XSEDE is led by the University of Illinois' National Center for Supercomputing Applications. The partnership includes the following institutions:

XSEDE Leadership Committees

XSEDE Leadership Team

John Towns

National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign

Kelly Gaither

Texas Advanced Computing Center
University of Texas at Austin

Questions from the public and the media about the XSEDE project should be directed to:

Hannah Remmert

National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign / 217-300-5976

Collaborations with Funded Activities

NSF Proposal Title PI/Contact Award Abstract Amount
Mainstreaming Volunteer Computing David Anderson 1105572 $548,546
SI2-SSI: SciDaaS -- Scientific data management as a service for small/medium labs Ian Foster 1148484 $2,398,999
Collaborative Research: Integrated HPC Systems Usage and Performance of Resources Monitoring and Modeling (SUPReMM- SUNY Buffalo) Abani Patra 1203560 $458,561
Collaborative Research: Integrated HPC Systems Usage and Performance of Resources Monitoring and Modeling (SUPReMM- UT-Austin) Abani Patra 1203604 $457,919
Center for Trustworthy Scientific Cyberinfrastructure (CTSC) Von Welch 1234408 $4,518,845
Latin America-US Institute 2013: Methods in Computational Discovery for Multidimensional Problem Solving Kevin Franklin 1242216 $99,986
EAGER proposal: Toward a Distributed Knowledge Environment for Research into Cyberinfrastructure: Data, Tools, Measures, and Models for Multidimensional Innovation Network Analysis Nicholas Berente 1348461 $204,935
Multiscale Software for Quantum Simulations in Materials Design, Nano Science and Technology Jerzy Bernholc 1339844 $500,000
MRI: Acquisition of SuperMIC-- A Heterogeneous Computing Environment to Enable Transformation of Computational Research and Education in the State of Louisiana Seung-Jong Park 1338051 $3,924,181
Open Gateway Computing Environments Science Gateways Platform as a Service (OGCE SciGaP) Marlon Pierce 1339774  $2,500,000
Sustaining Globus Toolkit for the NSF Community (Sustain-GT) Steven Tuecke 1339873 $1,200,000
CC-NIE Integration: Developing Applications with Networking Capabilities via End-to-End SDN (DANCES) Kathy L. Benninger 1341005 $650,000
A Large-Scale, Community-Driven Experimental Environment for Cloud Research Dr. Kate Keahey 1419141 $10,049,765
MRI: Acquisition of a National CyberGIS Facility for Computing- and Data-Intensive Geospatial Research and Education Shaowen Wang 1429699 $1,787,335
Acquisition of an Extreme GPU cluster for Interdisciplinary Research Todd Martinez 1429830 $3,500,000
The Centrality of Advanced Digitally-ENabled Science: CADENS Donna Cox 1445176 $1,499,535
CloudLab: Flexible Scientific Infrastructure to Support Fundamental Advances in Cloud Architectures and Applications Robert Ricci 1419199 $9,999,999
RUI: CAREER Organizational Capacity and Capacity Building for Cyberinfrastructure Diffusion Dr. Kerk F. Kee, Almadena Y. Chtchelkanova 1453864  $519,753
Fostering Successful Innovative Large-Scale, Distributed Science and Engineering Projects through Integrated Collaboration Nicolas Berente 1551609  $100,000
EarthCube RCN: Collaborative Research: Research Coordination Network for HighPerformance Distributed Computing in the Polar Sciences Allen Pope 1541620 $299,977
MRI Collaborative Consortium: Acquisition of a Shared Supercomputer by the Rocky Mountain Advanced Computing Consortium Thomas Hauser 1532236  $2,730,000
BD Hubs: Midwest: "SEEDCorn: Sustainable Enabling Environment for Data Collaboration that you are proposing in response to the NSF Big Data Regional Innovation Hubs (BD Hubs): Accelerating the Big Data Innovation Ecosystem (NSF 15-562) solicitation Edward Seidel 1550320  $1,499,999
Secure Data Architecture: Shared Intelligence Platform for Protecting our National Cyberinfrastructure" that you are proposing in response to the NSF Cybersecurity Innovation for Cyberinfrastructure (NSF 15-549) solicitation Alexander Withers 1547249  $499,206
CILogon 2.0 project that you are proposing in response to the NSF Cybersecurity Innovation for Cyberinfrastructure (NSF 15-549) solicitation James Basney 1547268  $499,973
DIBBs: Merging Science and Cyberinfrastructure Pathways: The Whole Tale Bertram Ludaescher 1541450 $4,986,951
Associated Universities, Inc. (AUI) and the National Radio Astronomy Observatory (NRAO) Philip J. Puxley 1519126  $1
SI2-SSE: Multiscale Software for Quantum Simulations of Nanostructured Materials and Devices J. Bernholc 1615114 $29,232
Collaborative Research: SI2-SSI: Adding Volunteer Computing to the Research Cyberinfrastructure David Anderson 1550601 $259,999
Molecular Sciences Software Institute (MolSSI) that you are proposing in response to the NSF Scientific Software Innovation Institutes (S2I2, NSF 15-553) solicitation Thomas Crawford 1547580 $5,880,491
Science Gateways Software Institute for NSF Scientific Software Innovation Institutes (S2I2, NSF 15-553) solicitation Nancy Wilkins-Diehr 1547611 $6,599,000
CC* Compute: BioBurst in response to the Campus Cyberinfrastructure (CC*) Program solicitation (NSF 16-567) Ron Hawkins 1659104 $494,066
CC* Networking Infrastructure: Building HPRNet (High-Performance Research Network) for advancement of data intensive research and collaboration Farzad Mashayek 1659255 $499,745
Cybertraining:CIP – Professional Training for CyberAmbassadors Dirk Colbry 1730137 $498,330
SI2-SSI: Pegasus: Automating compute and data intensive science Ewa Deelman 1664162 $2,500,000
Quantum Mechanical Modeling of Major Mantle Materials Renata Wentacovitch 0635990  $805,227
MRI: Acquisition of the Lawrence Supercomputer to Advance Multidisciplinary Research in South Dakota Doug Jennewein 1626516 $504,911
Collaborative Research: CyberTraining: CIU: Hour of Cyberinfrastructure: Developing Cyber Literacy for Geographic Information Science Eric Shook 1829708 $373,990
CC* NPEO: A Sustainable Center for Engagement and Networks Jennifer M. Schopf 1826994 $1,166,667
Collaborative Research: Building the Community for the Open Storage Network Alex Szalay 1747493 $165,185
CICI: CSRC: Research Security Operations Center (ResearchSOC) Von Welch 1840034 $4,933,641
CC* NPEO: Towards the National Research Platform Larry Smarr 1826967 $2,500,000
Collaborative Research: ABI Sustaining: The National Center for Genome Analysis Support Thomas Doak 1759906 $313,673
SI2-SSI Collaborative Research: SCALE-MS-Scalable Adaptive Large Ensembles of Molecular Simulations Peter Kasson 1835780 $763,289.00
A Workshop to Jumpstart High-Performance Computing in Finance/ BIGDATA: IA: Collaborative Research: Understanding the Financial Market Ecosystem Mao Ye 1838183 $422,288.00
Elements: Software: Multidimensional Fast Fourier Transforms on the Path to Exascale Dmitry Pekurovsky 1835885 $477,460.00


Key Points
XSEDE is comprised of partnerships with 19 institutions
Contact Information

Supercomputer-Generated Models Shed Light on Color-Changing Material Applications


By: Kimberly Mann Bruch, San Diego Supercomputer Center

Coupling Comet-generated computational models with anodically coloring electrochromes (ACEs), researchers demonstrated how small chemical modifications change the electronic structure of the molecules' radical cation states, which in turn alter the color. Credit: Aimée Tomlinson, University of North Georgia


According to a press release issued in April by the Georgia Institute of Technology (Georgia Tech), a serendipitous discovery by graduate student Dylan T. Christiansen has led to materials that quickly change color from completely clear to a range of vibrant hues – and back again. 

The work could have potential applications in everything from skyscraper windows that control the amount of light and heat coming in and out of a building, to switchable camouflage and visors for military applications, and even color-changing cosmetics and clothing. The study also helps fill a knowledge gap in a key area of materials science and chemistry, according to the researchers.

A recent Journal of the American Chemical Society article entitled New Design Paradigm for Color Control in Anodically Coloring Electrochromic Molecules explained the research in detail, including the explanatory computational models that were run on the Comet supercomputer at the San Diego Supercomputer Center (SDSC) at UC San Diego, allocated through XSEDE. 

For two decades, first author John R. Reynolds, who has joint appointments in the School of Chemistry and Biochemistry and the School of Materials Science and Engineering at Georgia Tech, has been studying and developing electrochromic materials that can change colors. Much of Reynolds' work has focused on how applying a small electrical voltage changes electrochromic materials, called cathodically coloring polymers, from a wide range of vibrant colors to opaque but with a slight blue tint. "That's fine for many applications – including rear-view mirrors that cut the glare from oncoming cars by turning dark – but not for all potential uses," said Reynolds. 

For example, the U.S. Air Force is working toward visors for its pilots that would automatically switch from dark to clear when a plane flies from bright sunlight into clouds. "And when they say clear, they want it crystal clear, not a light blue," Reynolds said. "We'd like to get rid of that tint."

Toward Clarity

Another family of electrochromic materials that can change color when exposed to an oxidizing voltage is known as anodically coloring electrochromes (ACEs), colorless materials that take on color upon oxidation. But there has been a lack of understanding in the science behind those colored oxidized states, known as radical cations. Researchers have not entirely understood the absorption mechanism of these cations, so the colors could not be controllably tuned. 

Enter Dylan T. Christiansen, a graduate student in the Reynolds group. While tinkering with some ACE molecules, he experimented with a new approach to controlling color in radical cations. Specifically, he created four different ACE molecules by making tiny changes to the ACEs' molecular structures that have little effect on the neutral or clear state, but significantly change the absorption of the colored, or radical cation state. 

The results were spectacular. "I expected some color differences between the four molecules, but thought they'd be very minor," Christiansen said.

Instead, upon the application of an oxidizing voltage, the four molecules produced four very different colors: two vibrant greens, a yellow, and a red. And unlike their cathodic counterparts, they are crystal clear in the neutral state, with no tint. Finally, just like mixing inks, the researchers found that a blend of the molecules that switch to green and red made a mixture that is clear and switches to an opaque black. Suddenly those Air Force visors that switch from crystal clear to black looked more attainable.

"The beauty of this is it's so simple. These minor chemical changes – literally the difference of a few atoms – have such a huge impact on color," said Aimée L. Tomlinson, a professor in the Department of Chemistry and Biochemistry at the University of North Georgia and co-author of the paper with Reynolds and Christiansen. 

Supercomputer-generated ACE models

How could such tiny changes have such an effect? That's where Tomlinson, a computational chemist, and SDSC's Comet supercomputer came into play. For the last five years, Tomlinson has used Comet to analyze Reynolds' electrochromic materials with computational models that provide insights into what's happening at the sub-molecular level.  

The study findings provided significant insight into how molecule alterations change colors and the work continues to generate insights into new ACE molecules, thanks to continuous feedback between Tomlinson's models and the experimental data. The models generated by Comet help guide efforts in the lab to create new ACE molecules, while the experimental data from those molecules makes the Comet models even stronger.

The Comet-generated models, coupled with Christiansen's data for the new ACE molecules, showed how the small chemical changes can drastically alter the electronic structure of the molecules' radical cation states, and ultimately control the color. "While I was the only person doing the computational work for this particular project, I have worked with 39 undergraduate students and 25 of them have gone on to or have plans to attend graduate or medical school," said Tomlinson. "I have been fortunate enough to have been awarded over two million core-hours to complete my work, which has led to this paper as well as eleven additional manuscripts where seven included undergraduate authors."

Tomlinson noted that the visualizations helped to illuminate how radical cations work. However, they are still not well understood. She said that this study could now help others manipulate them for future use in fields beyond electrochromism.

"I think what makes science really interesting is that [sometimes] you see something you really did not expect, you pursue it, and you end up with something that is better than you expected when you started," said Reynolds in commenting on the serendipitous nature of the initial discovery.






This work was funded by the U.S. Air Force Office of Scientific Research. The computational portion of the study was funded in part by the National Science Foundation Extreme Science and Engineering Discovery Environment (XSEDE) allocation TG-DMR160146. Tomlinson also acknowledges the support of her university, while Reynolds acknowledges support for his electrochromic polymer research program from NXN Licensing.