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
Performing Computational Study of High Entropy Alloys Completed	In this project, the student will help improving the parallel performance of KKR-CPA method on supercomputers and apply the method to the computational study of high entropy alloys. The student will also explore the data analytics tools (e.g., Materials Project, AFLOW, OQMD) for the study of high entropy alloys, and compare the results from different computational approaches.	Yang Wang Carnegie Mellon University Carnegie, PA
Computational Analysis of Copper Intermediates in Reduction of Nitrous Oxide Completed	An intern will conduct quantum mechanical calculations of copper enzyme intermediates and calculate X-ray emission spectra. These calculated spectra will be compared with calculated spectra of model complexes to determine whether different coordination modes of nitrous oxide and reduced species can be distinguished by XES to identify enzyme intermediates.	S. Chantal E. Stieber California State Polytechnic University-Pomona Pomona, CA
An Exploration of Parallel Computation in Physics and Finance Using LittleFe Completed	A hands-on exploration of parallel computing concepts using LittleFe. Topics to be explored include MPI, OpenMP, Parallel R, knowledge of the Unix command line, and data analytics and visualization. These tools and technologies will be applied to basic algorithms within computational physics and computational finance.	Beau Christ Wofford College Spartanburg, SC
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
High-throughput DFT Study of Functionalized Polycyclic Aromatic Hydrocarbons (PAHs) Completed	Students will utilize DFT in order to understand the electronic properties of functionalized polycyclic aromatic hydrocarbons. The focus will be placed on understanding the role of functionality and the HOMO-LUMO gap, band gap, band dispersion, and charge carrier mobility.	Bohdan Schatschneider California State Polytechnic University-Pomona Pomona, CA
Novel Randomized Algorithms for Large-Scale Matrix Completion Completed	This project targets one of the most challenging problems in big data analytics, called large-scale matrix completion. In particular, we will design scalable and efficient randomized algorithms to enable matrix completion to handle large matrices and will implement the proposed algorithms on modern parallel/distributed computing systems. The key goal of this project is to support the training of undergraduate students.	Hao Ji California State Polytechnic University-Pomona Pomona, CA
Code Samples for Promoting Excellence in Machine Learning Education Completed	This position will look for students at the Apprentice level to help prepare high-quality code samples for machine learning teaching and training. These code samples aim to cover the most widely used machine learning techniques, including regression, support vector machines, k-means and graph-based clustering, linear and nonlinear dimensionality reduction techniques, recommender systems, and deep learning. The code samples will be used to help students understand the basics of machine learning and how to deploy them on XSEDE resources. Based on the code samples, the students will be able to quickly design and train their machine learning models and apply them to real-life problems such as computer vision, natural language processing, and robotics.	Hao Ji California State Polytechnic University-Pomona Pomona, CA