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
Processing Multispectral Satellite Imagery for Marine and Coastal Biodiversity Assessment Completed	Develop optimized processing of satellite imagery for oceanographic and coastal research. Collaborate with research staff to analyze and publish taxonomic occurrence, in situ oceanographic sensor, and gridded satellite imagery data at large scale. Support existing community efforts through code reviews, expanding test coverage, issue reporting, and documentation.	Tylar Murray University of South Florida New Port Richey, FL
Development of Algebraic Non-uniformity Correction Algorithm for Hexagonally Sampled Infrared Imagery Completed	Previously, Ratliff et al. [1-3] and Sakoglu et al. [4] developed algebraic non-uniformity correction (NUC) algorithms (the latter developed a matrix-based version with regularization capabilities) which mitigate fixed-pattern non-uniformity noise that is notoriously present in infrared image sequences/video, by utilizing of global motion of the scene or the imaging camera system. Infrared imagery have been traditionally sampled and acquired using a rectangular grid, therefore the developed NUC algorithms work on this traditional rectangular grid. On the other hand, hexagonal sampling better preserves information in the sampled data when compared to traditional rectangular sampling, and a hexagonal addressing scheme was recently developed by Rummelt et al. [5] In this project, we propose to develop an algebraic NUC algorithm for hexagonally-sampled infrared imagery, utilizing the addressing scheme developed in [5]. The proposed project involves simulation of infrared imagery with global motion, and testing the efficiency of the developed NUC algorithm on simulated infrared imagery and real infrared image videos. The matrix-based NUC algorithm requires large amounts of memory and computational power, therefore XSEDE resources will be crucial for testing the matrix-based NUC algorithm. [1] B. M. Ratliff, M. M. Hayat, and R. C. Hardie, An algebraic algorithm for nonuniformity correction in focal-plane arrays, Journal of the Optical Society of America A, Vol.19, pp.1737--1747, (2002). [2] B. M. Ratliff, M. M. Hayat, and J. S. Tyo, Radiometrically Accurate Scene-based Nonuniformity Correction for Array Sensors, Journal of the Optical Society of America A, Vol.20, pp.1890--1899 (2003). [3] B. M. Ratliff, M. M. Hayat, and J. S. Tyo, Generalized algebraic scene-based nonuniformity correction algorithm, Journal of the Optical Society of America A, Vol. 22, pp. 239--249, (2005). [4] U. Sakoglu, R. C. Hardie, M. M. Hayat, B. M. Ratliff and J. S. Tyo, An algebraic restoration method for estimating fixed pattern noise in infrared imagery from a video sequence, 49th Annual Meeting of the SPIE: Applications of Digital Image Processing XXVII, Denver, CO, SPIE Proc. Vol. 5558, pp. 69--79, August 2-6, 2004. [5] Nicholas I. Rummelt, Joseph N. Wilson, "Array set addressing: enabling technology for the efficient processing of hexagonally sampled imagery," Journal of Electronic Imaging, Vol. 20, Issue 2, pp. 023012-1--11, (2011).	Unal Zak Sakoglu University of Houston-Clear Lake Houston, TX
Molecular Dynamics Simulation of Alanine Racemase in Pathogen Pseudomonas Aeruginosa for Drug Design Purposes Completed	Pseudomonas aeruginosa is an opportunistic pathogen and associated with serious cross-infections in the hospitals and clinics; the D-alanine produced by alanine racemase (AlaR) is used for peptidoglycan biosynthesis in bacteria, so AlaR is a good drug design target on P. aeruginosa. Limited X-ray structure for the AlaR from P. aeruginosa (one available), so ten AlaR high-resolution structures from Geobacillus stearothermophilus would be studied and compared to Molecular Dynamics (MD) simulation trajectory of the AlaR from P. aeruginosa for drug design purposes.	Hung-Chung Huang Jackson State University Jackson, MS
Role of Spin in Pyridinic N Catalyzed O2 Dissociation on Graphene Defects Completed	We investigate various graphene defects as supports for single metal atoms and the role that spin plays in catalysis. Previous investigations have been on Pt, as well as earth abundant metals like Fe, V, Ta, and Mo. We would like to vary the number of pyridinic N dopants to identify the role of spin in catalytic reactions facilitated by these earth abundant transition metal adatoms stabilized by graphene defects.	Chloe Groome University of California-Irvine Irvine, CA
Photorealistic Synthetic Data Generation for Deep Learning Training Completed	Using synthetic data in deep learning training helps to reduce manual annotation, but often fails to produce satisfactory predictions on real data. One of the primary factors is the lack of visual realism the presence of objects in training images. Recent progress in image synthesis and adversarial generative networks have catalyzed the rapid generation of photorealistic images with limited user assistance. We plan to study an efficient approach to generating photorealistic, synthetic data and analyze the reality gap of synthetic data in deep learning.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
Towards Sustainable Agriculture: Elucidating Ammonium Transporter Mep2 Completed	We are seeking a passionate apprentice about computational biology. You can employ molecular dynamics simulations, machine learning, and data science approaches to understand the underlying working mechanism of an ammonium transporter, Mep2. Mep2 proteins transport ammonium from external environment, a crucial determining factor for plant growth. Understanding how Mep2 absorbs ammonium will help us understand how plants take up nutrient. This will potentially provide new insights about engineering plant proteins to achieve better crop yield.	Jiangyan Feng University of Illinois Urbana-Champaign Urbana-Champaign, IL
Performance Assertions for High Performance Applications 2.0 Completed	Student(s) will be working on building tools to monitor performance sensitive applications. We will be building infrastructure to support writing 'performance assertions' that monitor the performance of high performance applications (games, graphics, volume visualizations, etc.) running with C/C++ code. Typically these applications are also highly parallelized (using frameworks like OpenMP or CILK), and can be difficult to track using regular profiling tools. Thus, we will be implementing a tool framework for monitoring the performance of these applications using the LLVM compiler framework.	Michael Shah Northeastern University Boston, MA
A Study for Implementation of Parallel Algorithms for Engineering Applications Completed	We are developing parallel codes to simulate biofilm and optimal power flow. Our biofilm model based on modified Cahn-Hillard equations to simulate the growth of bacteria in water. 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. Simulations for optimal power flow based in electrical engineering as a way to minimize the cost associated with power production in a network. We will develop parallel codes using parallel network data structure to apply to large size power grid simulations.	Jung-Han Kimn South Dakota State University Brookings, SD
Use of ML for Identification of Electronic Properties Trends in Organic Molecular Crystals Completed	Generate Python computer code that will sort organic molecular crystals according to functionalities associated with the molecular components. Then use density functional theory to calculate the electronic properties of said crystals. Then use ML algorithms to establish quatitative structure properties trends in these materials.	Bohdan Schatschneider California State Polytechnic University-Pomona Pomona, CA
Structures and Properties of Strongly Bound Doped Metalloid Atomic Clusters Completed	We intend to study strongly bound doped metalloid atomic clusters, which are potentially useful as models for the active sites in heterogeneous catalysis and for components in the electronics industry. Specifically, we intend to continue our investigations of understanding how the properties of silicon clusters change when different doping concentrations in the silicon clusters are utilized.	Jonathan Lyon Murray State University Murray, KY
Parallel Numerical Simulation of Energy Transport and Conversion in 2D Nano Materials Completed	This undergraduate student experience will involve developing parallel numerical codes for the simulation of energy transport and conversion in nanostructured semiconductor materials and devices. We will then proceed to study nanostructured thermoelectric devices for energy conversion with the goal of improving energy conversion efficiency and energy performance in nanoelectronics.	Zlatan Aksamija University of Massachusetts-Amherst Amherst, MA
Continuation of Electronic Structure Determination of Copper Hyponitrites Completed	Copper hyponitrites are proposed as intermediates in NO reduction, however mechanisms are not well understood. This intern will use density functional theory to calculate X-ray absorption spectra for a series of copper hyponitrites that have been crystallographically characterized.	S. Chantal E. Stieber California State Polytechnic University-Pomona Pomona, CA
Regional University Learners Programming Cohort Completed	Faculty at a regional university will mentor Learners for the XSEDE EMPOWER Learners program to enable them to compete in the state HPC competition. Through weekly meetings, faculty will guide the students through multiple modules from the SHODOR site and work with multiple HPC systems. Faculty will also coordinate meetings with other regional and national leaders in HPC education.	Jeremy Evert Southwestern Oklahoma State University Weatherford, OK
Dynamics of Gap Junction Intercellular Communication 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, enabling direct cell-to-cell communication of chemical signals. We aim to use GPU-accelerated MD simulations to understand the molecular principles underlying solute permeation and selectivity of these intercellular communication pathways.	Steve Reichow Portland State University Portland, OR
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
Real-Time Adaptive Control of Cell Behavior in Artificial 3D Cultures using Machine Learning Completed	Current approaches to tissue engineering fail because there is no central nervous system to orchestrate cell behavior in artificial cultures. During the Fall 2019 phase of the XSEDE EMPOWER project we submitted for a publication a novel automated microfluidics technology that integrates tissue engineering scaffolds with vascular-like channels. These channels are then used for analyzing and modulating cell behavior using a computer, which effectively acts like a brain for the artificial tissue. The analysis of the cell behavior occurs in real time and at different locations in the culture, which in turn requires the use of distributed computing in order to handle the work load. However, several problems still remain unsolved: 1) The calculation flow process requires continuous 3D imaging at a very high resolution. But when the individually-acquired tiles are stitched into a panorama, the data size becomes too large to fit into RAM (which slows down performance). Therefore, we have also been working on parallelized Matlab algorithm that would perform the image stabilization algorithm by analyzing the individual tiles (before the stitching). Currently, we have finished this code, as well as a control "naive" implementation of the stabilization for comparison. We have also generated 20 scenarios of different types of disturbances and drifts that the microscopy acquisition can experience, in order to make sure that our algorithm outperforms the "naive" implementation in call cases. With the remainder of the Fall 2019 we intend to complete the testing, and begin drafting up the manuscript that will share the results of our work with the rest of the community. After this bottleneck is resolved, we will be able to analyze the incoming stream of images in real-time, by sending them to the supercomputer. This will allow our device to respond to the imaged cell behavior, with the ultimate goal of controlling it. To that end, the Spring 2020 phase of the project will focus on developing a Model Predictive Controller (MPC) that will use a Recurrent Neural Network (RNN) in order to learn from empirical data and modulate the cell action based on real-time non-destructive feedback from 3D Lattice Light Sheet Microscopy (a Nobel Laureate technology available in the PI's lab) and chemical assaying. There will be many MPCs working collaboratoratively, with each one being responsible for a different location in the culture. Therefore, their performance will also be parallelized. The ultimate goal is to achieve the ability to culture reproducible tissue patterns, specified by the user prior to the at the beginning of the experiment. This technology will benefit patients in need of life-saving organ and tissue transplants.	Roman Voronov New Jersey Institute of Technology Newark, NJ
Parallel Techniques and Data Structure for Compressive Genomics Completed	We have recently introduced a scalable hybrid parallel algorithm, called phyNGSC, which allows fast compression as well as the decompression of big FASTQ datasets using distributed and shared memory programming models via MPI and OpenMP. This project will present the design and implementation of a novel parallel data structure which lessens the dependency on decompression and facilitates the handling of DNA sequences in their compressed state using fine-grained decompression in a technique that is identified as in compresso data processing. Our proposed structure and methodology will facilitate the enrichment of compressive genomics and sublinear analysis of big NGS datasets.	Sandino Vargas-Perez Kalamazoo College Kalamazoo, MI