XSEDE EMPOWER Positions

Please note that XSEDE EMPOWER ended with the conclusion of the XSEDE project on August 31, 2022. The following information is provided for archival purposes only.

This page provides a listing of positions that are under review for the next iteration of the program, as well as those that have been approved for current and previous iterations of the program. As part of the review and approval process, accepted student applicants are associated with an approved position.

If you are intending to mentor a student, you should create a position. If you also have a student in mind with whom you want to work, that student should submit an application. If you intend to mentor multiple students, please create a separate position for each student.

Project Title / Status	Summary	Creator / Institution
An Open-Source High-Performance Computational Framework for Thermal-Hydrological-Mechanical-Chemical Modeling and Data Analytics Completed	In the last two decades, thermal-hydrological-mechanical - chemical (THMC) codes have been widely used in simulating subsurface engineering problems but they lack tools for uncertainty quantification and optimization (i.e. data analytics). The objective of this project is to develop an open-source computational framework that would integrate these tools with THMC modeling. Open-source software programs, OpenGeoSys - for THMC modeling - and Sandia Dakota - for optimization and uncertainty quantification – will be coupled. Furthermore, the coupled software would be successfully applied to benchmarks. The computational framework developed can be applied to solve a wide range of subsurface problems, including, carbon dioxide sequestration, geothermal heat extraction, geological disposal of nuclear waste, etc.	Anjali Sandip University of North Dakota Grand Forks, ND
Large Scale Genetic Programming for Image Understanding Completed	We are looking for students interested joining our research team to help develop software to scale a prototype Simple Evolutionary Exploration (SEE) library to utilize large scale computing systems. This exploration will look into leveraging High Performance Computing Resources (XSEDE), HTCondor and Cloud Resources. The long-term goal of the project is to build image annotation system that works in "real time" with the researchers to explore the algorithm space for solutions to scientific image understanding problems.	Dirk Colbry Michigan State University East Lansing, MI
Molecular Mechanisms of HIV-1 Infection Completed	Develop large-scale analysis tools for molecular dynamics simulations. Perform molecular dynamics simulations of HIV-1 relevant systems. Collaborate with biophysical scientist in the design of experiments for the validation of the discoveries.	Juan Perilla University of Delaware Newark, DE
A Full Program Cache Profiler for High Performance Applications Completed	Students will be working on building a profiler for high performance applications specifically focusing on cache behavior. Students will measure the performance of data structures under different workloads and observe hotspots in code and find out where opportunities for further optimizations exist and testing implementations of various cache-oblivious algorithms to increase performance.	Michael Shah Northeastern University Boston, MA
Multiscale Dynamics Simulations of Membrane Proteins Completed	Membrane proteins are attached to or associated with membranes, and they perform a wide range of functions from signal transduction to membrane fusion that are vital to cell growth, replication, and movement. Here, we develop and apply highly accurate and efficient methods for multiscale simulations of critical membrane proteins in order to gain insights into their acting mechanisms and underlying dynamics.	Hai Lin University of Colorado Denver/Anschutz Medical Campus Denver, CO
3D Object Reconstruction from Multi-View RGB-D Data Completed	Advances in sensor technology have made possible the utilization of commodity RGB-D cameras to generate high-quality 3D models of physical objects required to many applications such as robotics, fabrication, augmented and virtual reality. In this project, we will investigate an accelerated implementation of 3D reconstruction from multi-view RGB-D data, by taking advantage of the power of high-performance computing systems. We are seeking students to be involved in this project who would like to develop an understanding of 3D reconstruction process and practical skills of utilizing high-performance clusters to process RGB-D data.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
RUM: Empowering Students in Scientific Computing Completed	To allow the participants to acquire and develop the basic skills necessary to effectively participate in UR, specially in STEM domains. The targeted skills are base on the set of competencies identified at HPC University website, and created by faculty and an advisory committee. The project will focus on 3 of the 7 main areas of the HPC University Competencies; these are Simulation and Modeling, Programming and Algorithms, and Parallel Programming.	Ana Gonzalez University of Puerto Rico-Mayaguez Mayaguez, PR
Mechanisms of Stille Cross-Coupling Reactions Completed	The Stille cross coupling is a robust and versatile method to construct C-C bonds, widely used in organic synthesis. Perplexingly, the stereochemistry of the reaction is currently unpredictable, with both inversion and retention of chiral centers reported. We are using DFT (density functional theory) studies in Gaussian to understand the mechanism of these reactions, particularly the step in which stereochemistry is controlled. We will develop a predictable model that makes Stille reactions more useful for the construction of chiral centers in organic synthesis.	Buck Taylor University of Portland Portland, OR
Computational Insight into Plant Hormone Signaling Mechanisms Completed	Strigolactones are a class of plant hormones that regulate shoot branching in flowering plants.They also stimulate germination in theStrigagenus of parasitic weeds, which destroy∼10 billionworth of crops annually. The current model of strigolactone signaling in plants entails binding ofthe strigolactone hormone to a receptor protein, D14, enzymatic hydrolysis of the hormone by D14,a conformational change of the D14-hormone complex, and association of D14 with a signalingpartner, D3/MAX2. Recent biochemical analyses have shown that an interaction between D14and D3/MAX2 inhibits the enzymatic activity of D14 toward the hormone. This project aims tounderstand the molecular mechanism by which D14 enzymatic activity inhibited by D3. Ultimately,an enhanced understanding of strigolactone signaling will aid the development of effective controlsfor witchweed and help improve food security in witchweed-vulnerable regions	Diwakar Shukla University of Illinois Urbana-Champaign Urbana-Champaign, IL
Making the Computational Microscope Accessible to Blind Researchers Completed	Develop the computational tools necessary to enable a blind researcher to setup, run, and analyze molecular dynamics simulations of proteins, including scripts that improve accessibility of running calculations on national supercomputers through the interfaces of screen-readers and Braille displays. Develop strategies for visualization and conceptualization of protein structure and dynamics through data analysis and presentation techniques that do not depend on the sense of sight.	Jodi Hadden-Perilla University of Delaware Newark, DE
Ammonia Oxidation Facilitated by Single Atom Fe Coordinated to N4 Graphene Defects Completed	We investigate various graphene defects as supports for single metal atoms. Previous investigations have been on Pt, as well as earth abundant metals like Fe, V, Ta, and Mo. We would like to investigate the magnetic and electronic properties of double vacancy defects in graphene with varying numbers of N atoms coordinated to Fe, an earth abundant transition metal, and its catalytic behavior for ammonia oxidation, a promising technology to generate H2 gas.	Chloe Groome University of California-Irvine Irvine, CA
Molecular Dynamics Simulations of Gap Junction Intercellular Channels Completed	The Reichow lab uses cryo-electron microscopy (CryoEM) and molecular dynamics (MD) simulations to understand the fundamental relationship between structure, dynamics and function in complex biomolecular systems. Gap junctions are large protein channels that span the membranes of two neighboring cells, connecting their cytosols. We aim to use GPU-accelerated MD simulations to understand the molecular requirements underlying solute permeation through these intercellular channels	Steve Reichow Portland State University Portland, OR
Growing Artificial Tissues using Artificial Intelligence-based Controls Completed	In this project we are developing a technology for controlling cell behavior in living cultures based on real time images acquired from high resolution microscopy. During the first year of the project we have published the automated platform and also developed parallelized stabilization codes for the very large microscopy images. The next phase of the project is to develop a parallelized controls algorithm for directing the cell behavior in real time based on the high speed large memory microscopy observations.	Roman Voronov New Jersey Institute of Technology Newark, NJ
Hybrid Rocket Modeling Completed	Improve upon a simple model of hybrid rocket trajectory. This model will be used by a student team to design and build a rocket for the Spaceport America Cup.	Jeffrey Santner California State University-Los Angeles Los Angeles, CA
Performance Evaluation of Calorimeter Clustering Algorithms Completed	We develop optimized python clustering algorithms to group similar particle trajectories. These algorithms can then be used by the scientists at CERN to predict the traits of the particles in the Large Hadron Collider based on their trajectories. With the ever-growing data from scientific experiments, it is imperative to have automatic ways to analyze that data. Specifically, we analyze clustering algorithms such as DBSCAN, HDBSCAN and CLUE and compare them in terms of accuracy and computing time.	Alina Lazar Youngstown State University Youngstown, OH
Medical Imaging Data Compression Using Adaptive Space Filling Curves Completed	Magnetic Resonance Imaging (MRI) is one of the most widely used medical imaging data/modalities in neuroimaging. MRI data are stored as 3 dimensional (3D) tomographical data. Functional magnetic resonance imaging (fMRI) data, which has 3D plus a 1D time dimension, usually result in computation of 3D brain spatial activation maps. These activation maps can identify different brain networks involved in processing a task, stimulus, etc. For fMRI analyses, 3D spatial data of the brain are usually mapped to 1D for further analysis, and sometimes smoothened and/or compressed using 3D kernels. These operations have been done traditionally by using pre-defined linear ordering schemes or kernels. Linear ordering is far from optimal in the sense that it does not retain the structure of the 3D brain. Finding an optimal mapping which uses an optimal space-filling curve could retain the structure of the brain much better [1]. In this project, we propose finding a data-adaptive space filling curve, heuristics of which were defined in [1], and apply it to compression of fMRI brain activation maps from a schizophrenia study, and evaluate its compression performance. Due to the large size of 3D neuroimaging data, finding an optimal space-filling curve and applying it to compression requires computational power. Success of this project's outcome would improve any subsequent analyses that neuroimaging researchers perform, which includes brain's network functional connectivity analysis, brain disease/disorder classification, and pseudo computed tomography image generation from MRI data [2]. References: [1] Sakoglu et al. "In Search of Optimal Space-Filling Curves for 3-D to 1-D Mapping: Application to 3-D Brain MRI Data," Proceedings of ISCA 6th International Conference on Bioinformatics and Computational Biology (BICOB), pp. 61-66, March 2014, Las Vegas, NV. [2] Kuljus et al. "Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images," Communications in Statistics: Case Studies, Data Analysis and Applications 4, no. 1 (2018): 46-55.	Unal Zak Sakoglu University of Houston-Clear Lake Houston, TX
Molecular Dynamics for Photovoltaics and Acoustically-Controlled Defect Transitions Completed	Students assist mathematics, computer science and physics faculty in molecular dynamics simulations, visualizations and analyses. The two nano-scale applications are tracking acoustically-controlled defect transitions and simulating thin-film materials to aid photovoltaic technologies. Students adapt code developed by previous student and faculty researchers, and test them on a local Linux cluster before large-scale production runs on OSC's Owens cluster.	Stephen Harnish Bluffton University Bluffton, OH
Molecular Dynamics for Photovoltaics and Acoustically-Controlled Defect Transitions Completed	Students assist mathematics, computer science and physics faculty in molecular dynamics simulations, visualizations and analyses. The two nano-scale applications are tracking acoustically-controlled defect transitions and simulating thin-film materials to aid photovoltaic technologies. Students adapt code developed by previous student and faculty researchers, and test them on a local Linux cluster before large-scale production runs on OSC's Owens cluster.	Stephen Harnish Bluffton University Bluffton, OH