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
Coarse-grained Computational Modeling of Actin Polymerization Completed	A systematic understanding of the biochemical and biophysical mechanisms that govern actin organization is important for both medical applications as well as the development and design of advanced biomimetic self-organizing materials. Formins are importants proteins for cells with the ability to remain processively associatied with the actin filament end, adding profilin-actin subunits to the actin filament. The aim of this project is to develop computational models of how formins accelerate actin polymerization at the molecular level.	Dimitrios Vavylonis Lehigh University Bethlehem, PA
Creation of an Updated GalaxSee Interface Completed	GalaxSee is a program designed to teach issues in high performance computing surrounding the gravitational N-Body problem, and has been built in the past to run on Macs, PCs, Web Servers, in Java applets, on HPC clusters, and on the LittleFe parallel computing environment. We would like to update the current PC/Mac interface to run on the most recent OS versions, adding in the feature set parallelism that can be enabled for both multi-core through OpenMP and many-core if a suitable GPGPU is present. Additionally, we would like to add a steered mode to communicate with GalaxSee-HPC running on a remote XSEDE cluster, and update GalaxSee-HPC to make use of OpenMP and GPGPU based parallelism.	David Joiner Kean University Union, NJ
Electronic Structure Determination of Copper Nitrites Completed	Copper centers in enzymes facilitate the reduction of nitrite to nitric oxide as part of denitrification. The mechanism of this process is not well understood, so there is interest in studying smaller model systems that allow for possible mechanisms to be more easily evaluated. This initial study will evaluate isolated copper nitrite complexes and determine the electronic structures.	S. Chantal E. Stieber California State Polytechnic University-Pomona Pomona, CA
Learner(s) for Graphics and Visualization Completed	In this project students (Learners) will become familiar with the basic paradigms of high performance computing. Students will understand take a look at the differences between writing and debugging serial and parallel code, optimizing applications, and working with large scale data with a focus on graphics and visualization tasks.	Michael Shah Northeastern University Boston, MA
HPC Student Intern Opportunity Completed	This student intern position will initially learn various roles in the Oklahoma State University High Performance Computing Center, while also contributing to the team to support computational and data-intensive research. The student will learn about the research computing profession and get hands-on professional skills that will help with any future computational research or role she aspires to. Depending on interests and aspirations, the intern may focus and learn one aspect of HPC more intensively or work directly with the research community on domain projects (after learning more about it.)	Dana Brunson Oklahoma State University-Main Campus Stillwater, OK
Quantum Permutation Solver on HPC Completed	Explore Quantum Computing (QC) by modeling and simulating quantum bits with Hamiltonian energy operator on various HPC platforms. Study permutation problems and their computation complexity. Formulate and compare performance on permutation problem solving among Quantum-Simulation on HPC, Graphical Modeling, Cloud-based Quantum Annealing and parallel programming on various GPU/FPGA-accelerated HPC platforms.	Liwen Shih University of Houston-Clear Lake Houston, TX
Quantify Beauty with AI Completed	Technology progress into higher definition (HD) demands high-quality content strategy. This project aims in automating the creating and discerning aesthetic levels among visual contents of images, art, graphics, etc... (and can be extended later beyond visual content). Data will be extracted from measuring/filtering/transforming visual contents for AI machine learning analysis to uncover the mystery of what constitute absolute/biological/emotional beauty and harmony, thus enabling the synthesis, selection and creation of beautiful contents for our HD world.	Liwen Shih University of Houston-Clear Lake Houston, TX
Cognitive Intelligence with EEG Deep Learning on Spark Completed	We will investigate the performance and the scalability of deep learning using electroencephalogram data (EEG) on the Spark platform. This study will extend the scope of current EEG data processing and modeling and evaluate its impacts to cognitive intelligence.	Hong Lin University of Houston-Downtown Houston, TX
Scaling Rank-Revealing Randomized Singular Value Decomposition Algorithms Using Spark Completed	Constructing a low-rank matrix approximation with a suitable rank is critical to many data analytic applications, but for big data, its scalable distributed implementations have not been investigated much. In this project, we plan to design, analyze, and implement scalable rank-revealing randomized singular value decomposition algorithms by bringing together recent advances in randomized algorithms and Spark's big data processing. We will benchmark their performance on a variety of matrix data from the SuiteSparse Matrix Collection as well as the datasets for large-scale recommender systems.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
Computational Research in Number Theory: Initial and Middle Sums & FLT Completed	Analyzing initial and middle sums of sequences dates back to antiquity--including examples from Leonardo Fibonacci. For about a decade, we have developed a series of related number theoretic research questions that led to well-received conference presentations--four regional, two national and one at an international venue. This internship extends the research by applying naturally parallelizable algorithms for our Mathematica programs and dynamic Excel spreadsheets that have yielded insight into FLT via modulo m analysis of initial and middle sums of sequences based on Stirling numbers of the second kind.	Stephen Harnish Bluffton University Bluffton, OH
Sequential Monte Carlo (SMC) Methods for Data Assimilation Completed	The student will continue to work on implementing sequential monte carlo (SMC) methods using XSEDE resources for data assimilation purposes. SMC methods will be implemented for simulating real-time applications (building occupancy estimation). The student will work to parallelize the SMC methods and produce some significant results.	Sanish Rai Longwood University Farmville, VA
Unveiling Dynamics of Membrane Proteins in Multi-component Lipid Bilayers Completed	1. Setting up and running molecular dynamic (MD) simulation for membrane proteins in large and complex lipid bilayers 2. Analyzing the data generated by MD and working with large scale data sets to gain information about the conformations and the secondary structures of the proteins embedded in the bilayers	Afra Panahi California State University-San Marcos San Marcos, CA
Unveiling Dynamics of Membrane Proteins in Multi-component Lipid Bilayers Completed	1. Setting up and running molecular dynamic (MD) simulation for membrane proteins in large and complex lipid bilayers 2. Analyzing the data generated by MD and working with large scale data sets to gain information about the conformations and the secondary structures of the proteins embedded in the bilayers	Afra Panahi California State University-San Marcos San Marcos, CA
Large Scale Clothing Retrieval Using Deep Fashion Landmarks Completed	Recent advances in machine learning, especially in deep learning, have made a big leap forward, leading to state-of-the-art fashion recommender systems with both high accuracy and fast speed. In this project, we plan to build a large-scale recommender system for retrieving fashion items of interest by taking advantage of deep fashion landmarks. We will compare with the approaches based on handcrafted vision features and/or deep CNN features in terms of accuracy and speed.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
3D Human Model Animation System Using Deep Pose Retargeting Completed	Recent progress in deep learning research has catalyzed the development of high-quality pose estimation and 3D object reconstruction. In this project, we plan to leverage deep learning techniques to build a 3d human model animation system, which consists of the following tasks: (1) estimating deep 3D human pose from a sequence of images; (2) creation of personalized 3D avatars; and (3) retargeting deep pose to constructed 3D avatars. The proposed framework will have the great potential to be applied to many domains, including but not limited to, fashion, fitness, education, and entertainment.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
A Study for Implementation of Parallel Algorithms for High Fidelity Mixtures of Biofilm and Solvent Completed	We are developing a massively parallel 3D code to simulate high fidelity mixtures of biofilm and solvent. Biofilm growth has important industrial and medical ramifications, including corrosion in oil and gas pipelines and possible causal links to a host of health issues. Despite this, there have been few high fidelity 3D simulations of biofilms in the research literature. One reason is that a high fidelity simulation of the partial differential equations that govern biofilms requires the use of parallel computing (e.g., supercomputers), which necessitates more sophisticated computational techniques. Such techniques include message passing between processors, parallel linear solvers, and domain decomposition preconditioning methods	Jung-Han Kimn South Dakota State University Brookings, SD
Modeling and Simulation: Capstone Projects Completed	Students should have basics modeling and programming skills. The students will work on projects of different topics. The objective is to build and test a model and use the model to derive insights into the system behavior with respect to a set of proposed problems. The students will use Python and will learn OpenMP . It is expected that the students work on two projects and one of the projects should involve HPC.	Ana Gonzalez University of Puerto Rico-Mayaguez Mayaguez, PR
Development of Autonomous Unmanned Aerial Vehicle for Object Tracking and Following System Completed	The main focus of this project is to perform research, develop, implement, and evaluate solutions for Unmanned Aerial Vehicle (UAV) based object tracking and facial expression recognition (FER) application. In this project, we will use image/video processing techniques for moving object tracking and use deep learning for facial expression recognition. The students will also evaluate the tracking algorithms and FER in HPC. The implementation for the object tracking will lead to the study of an autonomous object following and human face expression recognition on UAV. The success of this system will lead to the applications of video processing to a wide range of robot control and perception problems.	Jiang Lu University of Houston-Clear Lake Houston, TX
Statistical Description of Turbulent Wall-bounded Flows Completed	Turbulence is mainly characterized by randomness, chaos and a disparate range of turbulent scales and mixing. Applications can be found in drag reduction, heat transfer enhancement, aeroacoustic noise control and mixing enhancement. In recent years, the discipline of fluid dynamics has been reliant to high-performance computational simulations as a means of predicting flow behavior and understanding the thin zone around a solid immersed in a viscous fluid flow, the so called "boundary layer." Furthermore, turbulent boundary layers that evolve along the flow direction are ubiquitous. Computationally speaking, this type of boundary layer (i.e., spatially-developing boundary layer) poses an enormous challenge, due to the need for accurate and time dependent in flow turbulence information. Direct Numerical Simulation (DNS) is a numerical tool that resolves all turbulence scales; thus, it aims to provide high spatial/temporal flow data. In this project, we will employ a large dataset of direct simulations of turbulent boundary layers in order to perform a structural analysis of boundary layer parameters based on high order statistics of velocity, pressure and temperature fluctuations, such as quadrant analysis, skewness, flatness, probability density function (PDF) and power spectra. The major objectives of the proposed study are two-fold: (i) to understand the effect of different external conditions such as streamwise pressure gradient, Reynolds number dependency and compressibility, on the boundary layer structure, (ii) to evaluate the analogy between the velocity and thermal field.	Guillermo Araya University of Puerto Rico-Mayaguez Mayaguez, PR
Regional University Learners Programming Cohort Completed	Faculty at a regional university will mentor Learners for the XSEDE EMPOWER Learners program. Faculty will guide the students through multiple modules from the SHODOR site. Faculty will also coordinate meetings with other regional and national leaders in HPC education.	Jeremy Evert Southwestern Oklahoma State University Weatherford, OK
The Effect of Convex Wall Curvature on Hypersonic Turbulent Boundary Layers Completed	Turbulent boundary layers that evolve along the flow direction are ubiquitous. Computationally speaking, this type of boundary layer poses an enormous challenge, due to the need for accurate inflow turbulence information. Moreover, accounting for the effects of wall-curvature driven pressure gradient and flow compressibility adds significant complexity to the problem. Consequently, hypersonic spatially-developing turbulent boundary layers (SDTBL) over curved walls are of crucial importance in aerospace applications, such as unmanned high-speed vehicles, scramjets and advanced space aircrafts. This project seeks to evaluate the effect of convex surface curvature on spatially evolving turbulent boundary layers at hypersonic speeds (Mach number ~ 5). The main idea is to perform Reynolds Averaged Navier-Stokes (RANS) simulations and turbulence model assessment by means of a commercial Computational Fluid Dynamics (CFD) software, such as STAR-CCM+ or ANSYS-FLUENT, in moderate and strong Favorable Pressure Gradient (FPG) flows induced by wall curvature. Validation of numerical results will be done by comparison with a previously obtained Direct Numerical Simulation (DNS) database and wind tunnel experiments from the literature. This is a continuation Fall-2018 EMPOWER internship project.	Guillermo Araya University of Puerto Rico-Mayaguez Mayaguez, PR