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.

The project aims at developing new optimization techniques and implementations of cryptography tools in a CPU+GPU environment. We will focus on those expensive cryptography primitives including zero-knowledge proof and secure multi-party computation.

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 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.

In this project, underrepresented and minority students at Lane College will be trained in high-performance computing techniques to be used in optimizing computational algorithms. The project will port sequential protein structure and function prediction applications to the GPU using OpenACC and with the Nvidia Visual Profiler, optimize the ported application for improved efficiency. The optimized application will be tested on a benchmark dataset to ensure it produces results with a comparable accuracy.

Channel proteins are membrane proteins that facilitate the moving of ions across membranes, and they perform a wide range of functions that are vital to cell growth, replication, and movement. Here, we develop and apply highly accurate and efficient QM/MM methods for multiscale simulations of critical channel proteins in order to gain insights into their acting mechanisms and underlying dynamics.

Hai Lin

University of Colorado Denver/Anschutz Medical Campus

Perform density functional theory calculations to determine transition states for the synthesis of hydrogen peroxide over physical hole defects in graphene. According to our calculations the size of the physical hole defect changes the acidity of adjacent OH-groups and this project seeks to explore that change in acidity as a function of the size of the physical hole defect for the production of H2O2 from O2.

The open-source TARDIS supernova simulation code is written in Python and accelerated with the Numba just-in-time (JIT) compiler framework. The project for the summer will be to profile the current Numba code and add GPU support where applicable. The student will gain insights into code performance analysis, GPU programming, and open-source science code development (using modern practices such as version control, continuous integration, code review).

Cyclostationary signals are a special class of signals whose statistical properties do change with time in a periodic or close-to-periodic way. (https://cyclostationary.blog/) There is a lot of research that needs to be done to learn about satellite data mainly about cyclostationary signals (Gardner, 1994) and research on analyzing these data for further processing. Normally, these data needs to be manually analyzed and interesting features needs to be plotted and extracted. In this work, we want to use machine learning on the available data to extract these interesting features automatically. Unsupervised machine learning models help to analyze data and categorize data automatically based on their features. It will help group the data into various plots based on their common features. After that more work can be done including application of various other machine learning models to analyze the data such as learning about cyclostationary data, and their extraction methods.
A preliminary work has already been going on this Spring with two students. Students have completed basic literature review to understand the scope and problem which includes learning about satellite data analysis, cyclostationary signal properties and machine learning process. Basic analysis of a satellite data using data analysis has been completed to visualize various properties has been done. K-means clustering and neutral network algorithms have been implemented to analyze pulsar properties (Lorimer, Kramer, 2012). Currently python is used with its data analysis and machine learning libraries. The experiment results show that the process is computationally complex and taking significant resource and time to execute even for basic algorithms.
In this project, we will continue the research by adding more data sets and implementing other data analysis and machine learning algorithms. The experiment will require high performance computing resource which will be obtained from XSEDE resources.
References:
Gardner, W. A. (1994). An introduction to cyclostationary signals. In Cyclostationarity in communications and signal processing (pp. 1-90). New York: IEEE press.
Lorimer, D. R., & Kramer, M. (2012). Handbook of pulsar astronomy. Handbook of Pulsar Astronomy.

Proton beam therapy is an attractive option compared to traditional X-ray beam therapy when treating deep-seated tumors because the depth in the body at which protons deposit most of their energy can be controlled, while X-ray absorption falls off sharply with depth. Monte Carlo simulations and analytical methods are used to predict energy deposition patterns, as (for example) Coulombic scattering and energy loss make the analysis of even a simple pencil beam highly complex. This project will train students in these methods in the context of optimizing coverage of simulated tumors.

The undergraduate student will work with the mentor to investigate the machine learning models for compressible turbulent mixing simulations to predict the fluctuations between Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES). With the power of today's HPC systems, resolving all turbulence length scales are handled by DNS. In LES, the unresolved smaller scale motions are modeled by subgrid scale model. The student will conduct compressible multi fluid simulations to build up a database to train the neural network, and apply machine learning techniques to explore and prepare data for an optimized neural network model.

This project aims to provide basic computational and mathematical background to undergraduate students, who will eventually work in a research project involving the analysis of brain signals. It includes three main areas of preparation: 1) Mathematical foundation, 2) Computing foundation and 3) Electroencephalography (EEG) signals foundations.

This project will involve two undergraduate students developing a novel computational tool to advance our capabilities to visualize and quantify biological cells in 3D using virtual reality. Biological data such as images of cells developing in a tissue are stored as layered 2D images gathered on instruments such as confocal microscope; this data acquisition creates pitfalls when attempting to quickly identify and quantify 3D objects such as cells that are condensed within a finite space during development or tumorigenesis. To circumvent these issues, our goal is to create an open source tool using the Unity Visualization Toolkit (UVT) that can 1) render 2D layered imaging data as distinct 3D objects in virtual reality (VR), 2) identify individual cells within a condensed space, and 3) automatically quantify cell populations.

The goal of this project is to develop a conceptual framework that encompasses scalable provenance data analysis tools, predictive models using machine learning and optimization techniques to investigate causes and outcomes pertaining to loss of scientific computing integrity.

In force microscopy, magnetic or electrostatic forces between a nanometer-scale tip and a sample surface can produce nanometer-resolution images; however, linking the raw experimental data to specific sample properties can be challenging. Using our recently developed Lagrangian impedance model, we will perform numerical simulations for a broad range of possible tips and samples and apply several image reconstruction methods to the simulated data. The student will determine which image reconstruction method best captures relevant sample electrical or magnetic properties and share these results with other force microscopists via open-source Python code and a web interface for those without programming experience.

We will use cluster expansions, constructed based on density functional theory (DFT) calculations, to investigate the phase diagram upon intercalation of V2O5, a potential cathode material for non-Li-ion batteries, i.e., batteries that use ions such as Na, Mg, Ca, etc. Cluster expansions are required to be able to run large-scale Monte Carlo simulations. These will be used to introduce temperature to the DFT results, so that phase diagrams for different polymorphs and intercalant concentrations can be obtained. This will also allow for voltage profiles to be calculated.

One site on p450 3A4 has been shown to be a periphery site and may also be an allosteric site. This work will used supervised MD to probe what other site are periphery sites, leading to the testing if they are also allosteric sites.

The intern will conduct quantum mechanical calculations of nickel complexes with bidentate N-heterocyclic carbene ligands. These complexes are synthesized in the PI's lab and this computational work will investigate electronic structures and mechanisms related to oxidative addition reactions of alkyl fluorides. This will complement experimental work in the area of C-F activation for applications in pharmaceutical chemistry.

The intern will assist mathematics, computer science and physics faculty and graduate students in large-scale, density functional theory (DFT) calculations and molecular dynamics (MD) simulations and visualizations. The general goal is to analyze properties of Si-based and related semiconductors to aid ongoing research in photovoltaic (PV) technologies. An important role for this apprentice/intern will be to develop bash scripts to create directories for the large parameter space and associated VASP input files and implement parallel jobs to analyze VASP output files. A specific focus will be to fit VASP output for various Si-based semiconductors to the Birch-Murnaghan equation of state, yielding four physical quantities - bulk modulus, ground state energy, volume, and the first derivative of bulk modulus. These will be compared to known values, when available for that semiconductor. Scripts and parallel code will automate this analysis of the VASP output files.

In this project, we will use the density functional theory (DFT) approach to formulate and thoroughly investigate a series of novel inorganic solid electrolytes for the fast diffusion of Li+ ions. Specifically, we will perform systematic modeling on the structural stability and ion transport characteristics by fine-tuning the lattice chemistry of lithium-rich double anti-perovskite structures. This computational study will also lead to identifying effective ways to reduce the diffusion barriers for Li+ ions in double anti-perovskite phases via the suitable designation of lattice occupancy, which will be done by alloying on the chalcogen lattice site (A) and alternative occupancy of the halogen site (X). The high-performance computing facility will be used to run the simulations. Subsequently, the electrolytes for lithium-ion batteries will be synthesized at the Center for Advanced Photovoltaics at South Dakota State University to match with the theoretical results.

Knee-joint replacement is a procedure of replacing an injured joint with an artificial one, or prosthesis to mimic the function of a knee, taking into consideration of the patient's age, weight, activity level, and overall health.
This project aims to develop accurate and efficient simulations that reproduce the artificial-knee tibiofemoral kinematics, which involves:
1. Develop finite element (FE) simulators for the knee dynamic and the knee extension tests.
2. Develop neural networks (NN) based on self-organizing models and nature-inspired algorithms such as genetic programming (GP) that learn from FE simulations of the artificial-tibiofemoral kinematics.

Cyclostationary signals are a special class of signals whose statistical properties do change with time in a periodic or close-to-periodic way. (https://cyclostationary.blog/) There is a lot of research that needs to be done to learn about satellite data mainly about cyclostationary signals (Gardner, 1994) and research on analyzing these data for further processing. Normally, these data needs to be manually analyzed and interesting features needs to be plotted and extracted. In this work, we want to use machine learning on the available data to extract these interesting features automatically. Unsupervised machine learning models help to analyze data and categorize data automatically based on their features. It will help group the data into various plots based on their common features. After that more work can be done including application of various other machine learning models to analyze the data such as learning about cyclostationary data, and their extraction methods.
A preliminary work has already been going on this Spring with two students. Students have completed basic literature review to understand the scope and problem which includes learning about satellite data analysis, cyclostationary signal properties and machine learning process. Basic analysis of a satellite data using data analysis has been completed to visualize various properties has been done. K-means clustering and neutral network algorithms have been implemented to analyze pulsar properties (Lorimer, Kramer, 2012). Currently python is used with its data analysis and machine learning libraries. The experiment results show that the process is computationally complex and taking significant resource and time to execute even for basic algorithms.
In this project, we will continue the research by adding more data sets and implementing other data analysis and machine learning algorithms. The experiment will require high performance computing resource which will be obtained from XSEDE resources.
References:
Gardner, W. A. (1994). An introduction to cyclostationary signals. In Cyclostationarity in communications and signal processing (pp. 1-90). New York: IEEE press.
Lorimer, D. R., & Kramer, M. (2012). Handbook of pulsar astronomy. Handbook of Pulsar Astronomy.

Geo-spatial analysis is challenging because samples are not independent, but rather under influence by others in neighboring regions. Statistics-based methods of geospatial analysis rely on assumptions of adjacent interference, which is hard to characterize. We will explore using Graph Learning to detect geospatial clusters.

The objective of this project is to leverage GPU processing power to accelerate multi-physics simulations of complex real-world systems. Performance metrics would be predictive capability and time to solution.