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
Structure Determination and Analysis of Mixed Palladium Silicon Clusters, Si(n)Pd(m) (n > 12; m = 1-3) Completed	This position will study strongly bound doped metalloid atomic clusters as models for real world systems in heterogeneous catalysts and electronics. Specifically, we will explore palladium doped silicon clusters here, Si(n)Pd(m) (n > 12; m = 1-3). searching cluster potential energy surfaces for local and global minima and exploring the relationship of cluster size and dopant concentration on different cluster properties. The results from this investigation will then be compared with related cluster systems.	Jonathan Lyon Murray State University Murray, KY
Structure Determination and Analysis of Mixed Palladium Silicon Clusters, Si(n)Pd(m) (n < 13; m = 1-3) Completed	This position will study strongly bound doped metalloid atomic clusters as models for real world systems in heterogeneous catalysts and electronics. Specifically, we will explore palladium doped silicon clusters here, Si(n)Pd(m) (n < 13; m = 1-3). searching cluster potential energy surfaces for local and global minima and exploring the relationship of cluster size and dopant concentration on different cluster properties. The results from this investigation will then be compared with related cluster systems.	Jonathan Lyon Murray State University Murray, KY
Testing Supervised Molecular Dynamics for Curved Access Channels in p450 Completed	There are only a few hypotheses of how the drugs actually enter the enzyme in order to be metabolized. This question has implications for drug-drug interactions (effects when you need to take multiple medications) as well as drug design and discovery. This project will develop scripts to run Supervised Molecular Dynamics (SuMD) for the ingress of atorvastatin lactone (ARVL), and take preliminary data.	Edward Ackad Southern Illinois University-Edwardsville Edwardsville, IL
Modeling the Extracellular Matrix using Cellular Particle Dynamics (Apprentice Position) Completed	We are developing an in-house code for modeling the extracellular matrix. This matrix has complex structure composed of several components with different size and shape. Distinct type of cells are present and interact with the components. We are aiming to model these dynamic interactions.	Mahsa Dabagh University of Wisonsin-Milwaukee Milwaukee, WI
Development of Computational Labs for a New Chemistry Course Completed	In this position, the student will work with me to develop and test computational labs for a proposed computational chemistry course at USM. Since no such course is taught at USM, I envision have lab components for each lecture similar to the workshop.	Matthew Donahue University of Southern Mississippi Hattiesburg, MS
Computational Studies on Physical and Chemical Properties with Supercomputers Completed	Identify the requirements of machine learning and apply machine learning in the computational studies on Physical and Chemical properties	Yigui Wang Southern Connecticut State University New Haven, CT
Quantifying Binding Strength in Bacterial Nanowires through Non-Equilibrium Simulations Completed	Some bacteria use heme-rich protein assemblies called nanowires to move electron excitations long distances. We aim to probe these nanowire components through molecular simulation, building on current simulations ongoing in the laboratory to calculate the binding affinity between individual monomers within the nanowires.	Josh Vermaas Michigan State University East Lansing, MI
Developing Data Visualization Tools for Cryo-EM Completed	This project will develop data visualization Jupyter notebooks, web pages, or other digital tools to enable easy assessment of cryo-electron microscopy (cryo-EM) analysis routines. As part of the COSMIC2 (cosmic-cryoem.org) project, data visualization tools will enable COSMIC2 users to understand and interpret their jobs with clean, simple, and informative data visualization tools.	Michael Cianfrocco University of Michigan-Ann Arbor Ann Arbor, MI
Multiplex Network Analysis for Multimodal Neuroimaging Completed	We are developing a high-throughput data pipeline for generating multiplex networks from T1W, diffusion-weighted MRI, and fMRI imaging modalities. A multiplex network represents these diverse modes of MRI data as one graph-theoretic structure. Students will assist in the deployment of this data pipeline to the HPC context. This includes configuration of complex computing environments, code performance evaluation, and developing an understanding of image processing concepts.	Konasale Prasad University of Pittsburgh-Pittsburgh Campus Pittsburgh, PA
GPU-accelerated Sub-grid Models for Multi-phase Fluid Dynamics Completed	Sub-grid models for flowing dispersions are essential for the high-fidelity simulations of cough droplets leaving your mouth, bubble clouds cavitating near ship propellers, and more. Still, these sub-grid models are complex with high arithmetic intensity, dominating the cost of their associated simulations. We will develop a GPU-accelerated implementation of these models to enable next-generation multi-phase flow simulation.	Spencer Bryngelson Georgia Institute of Technology-Main Campus Atlanta, GA
Applying Hybrid Functional to Calculate H2O2 Synthesis Pathways on Physical Hole Defects in Graphene Completed	Perform density functional theory calculations to determine transition states for the synthesis of hydrogen peroxide over physical hole defects in graphene using LC-wPBE functinsl. 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 hybrid functional should provide more accurate barriers for already calculated pathways with PBE.	Michael Groves California State University-Fullerton Fullerton, CA
Calculating Reaction Barriers for Keto-enol Tautomerization of Acetophenone on Pt(111) Completed	Perform density functional theory (DFT) calculations to determine transition states for the keto-enol tautomerization reaction of acetophenone on Pt(111). In the literature, there are conflicting reports of the mechanism and DFT calculations should provide some insight. The results will be influential with understanding asymmetric hydrogenation reactions on non-chiral surfaces.	Michael Groves California State University-Fullerton Fullerton, CA
Structural and Electronic Properties of Borophene and B2C over H-terminated Diamond (100) Heterostructures Completed	Diamond is an ultra-wide bandgap (UWBG) semiconductor with the highest breakdown field and carrier mobility, making it an attractive material of choice for next-generation high-speed and highpower electronic and RF device applications. Recent studies suggest using a thin layer of 2D materials as the gate dielectric layer to mitigate the limitations of oxide-based acceptor layers. This work will explore the electronic properties of borophene and B2C 2D materials as an acceptor layer	Michael Groves California State University-Fullerton Fullerton, CA
Computational Study of Electronic Properties of 2D Materials Completed	In this project, we investigate the structural, electronic and optical properties of 2D materials based on Density Functional Theory (DFT). The electronic and optical properties can be extended using many body approaches such as GW method. In addition, we will study the effect of layer thickness, single layer properties. Both non-magnetic such as transitional metal dichalcogenides (TMDs) and magnetic layered materials such as transition metal halides will be studied. The cleavage energy will be calculated that will indicate the cleavability to single layer or few layers using mechanical exfoliation technique similar to graphene or other two-dimensional materials. The detailed atomic, electronic and optical properties will be studied.	Santosh KC San Jose State University San Jose, CA
Mathematical and Machine Learning-based Modeling for Brain Signal Analysis Completed	This project is the continuation of the XSEDE Empower project "Computing and Mathematical Foundations for Brain Signal Analysis", which is being carried out during Summer 2021. The project aims to develop mathematical and machine learning-based models for brain signal analysis. Its focus is to analyze EEG brain signals for patterns that represent intended communication of non-verbal and/or paralyzed subjects. This project will include training on Machine Learning techniques at intermediate and advanced level for undergraduate students, who participated on the previous project.	Luis Cueva Parra University of North Georgia Dahlonega, GA
3D Visualization, Identification, and Quantification of Biological Cells using Virtual Reality Completed	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 limitation in 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.	Matthew Niepielko Kean University Union, NJ
Molecular Dynamics of the Viral Life Cycle Completed	Student will learn to perform all-atom molecular dynamics simulations and participate in the development of computational methods to investigate processes of the viral life cycle.	Jodi Hadden-Perilla University of Delaware Newark, DE
Effect of Nanoconfinement and Polymer-Nanoparticle Interactions on the Behavior of Polymer Blends in Dense Nanoparticle Packings Completed	This project aims to support the work of the mentor involving field theoretic study of the thermodynamics of polymer blends in dense nanoparticle packings, which is supported by the mentor's NSF GRFP grant and NSF CBET-1933704. The effect of nanoconfinement and polymer-nanoparticle interactions on the phase behavior of polymer blends in highly-filled nanocomposites is poorly understand, in part because the experimental preparation of these systems proved difficult prior to the development of the capillary rise infiltration (CaRI) method in the lab of the mentor’s co-advisor. Molecular dynamics and theoretically informed Langevin dynamics (TILD) simulations in LAMMPS are alternate methods to field theoretic simulations that can shed light on the equilibrium structures and dynamics of these composites, and will compose the bulk of the work of the student.	Anastasia Neuman University of Pennsylvania Philadelphia, PA
Predictive Analytics for Extreme-Scale Scientific Computing Integrity using Provenance Data (Intern Position) Completed	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.	Nitin Sukhija Slippery Rock University of Pennsylvania Slippery Rock, PA
Large Scale Genetic Programming for Scientific Image Understanding (Learner Position) 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. The search space involved in this research is extremely large and requires massive computing resources. This research 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
Pathfinding with Assistance of UAV for Multiple Targets (Apprentice Position) Completed	This is follow-up research of Unmanned Aerial Vehicle (UAV) for pathfinding. With the help of HPC, more UAV based applications can be possible. In this project, we will continue using computer vision techniques for multiple targets optimal pathfinding with the assistance of UAV. UAV can capture wider view than humans. Thus, it provides better solution for a path from source to destination. This optimization problem has many real applications such as helping rescue in flooding, fire or earthquake. The students will be asked to evaluate the pathfinding algorithms in XSEDE HPC and to implement with UAV that has been built with previous funding support.	Jiang Lu University of Houston-Clear Lake Houston, TX
Adsorption of Amino Acids and Nucleic Acids at Nanomaterials Completed	The interaction of biomolecules with nanomaterials is present in a large range of applications, including, nanomedicine, biosensing, nanobioelectronics, and bioelectrochemistry. To refine such technologies, it is important to gain a deeper molecular comprehension of the properties of nano-bio interfaces. With this goal in mind, the apprentice will study the adsorption of selected amino acids, DNA, and RNA fragments onto gold nanoclusters and single-walled carbon nanotubes.	Luiz Oliveira Mount Vernon Nazarene University Mount Vernon, OH
Computational Determination of Electronic Structures and Spectroscopic Properties of N-heterocyclic Carbene Nickel Nitrosyl Complexes Completed	The intern will conduct quantum mechanical calculations of nickel complexes with bidentate N-heterocyclic carbene and nitrosyl ligands. These complexes are synthesized in the PI's lab and this computational work will investigate electronic structures and mechanisms related to reduction of NO. This will complement experimental work in the area NO reduction for applications in sustainable chemistry.	S. Chantal E. Stieber California State Polytechnic University-Pomona Pomona, CA
Investigation of Metal Binding in Psoriasin using Molecular Dynamics Simulations Completed	This study aims to understand the structural variations of psoriasin by providing a comparative analysis of the chemical and biophysical properties for the two structures of psoriasin in apo and metalated form at a molecular level by running molecular dynamics simulations using NAMD.	Deepika Das Emory University Atlanta, GA
Mixing Efficiency in Microfluidic Systems with Non-Newtonian Fluids (Apprentice Position) Completed	Microfluidic devices play crucial roles in a wide range of applications but efficient mixing stands as a significant hurdle. This circumstance is problematic as efficient microfluidic mixing is critical for creating homogeneous fluid environments necessary for chemical and biological applications. This project will investigate the mixing efficiency of suppressed Reynolds number flows using computational fluid dynamics, through the Ansys Fluids suite (ANSYS), to establish new foundational knowledge and approaches for designing passive microfluidic mixers to integrate within fluidic systems, such as bioprinters.	STEVEN SANTANA Harvey Mudd College Claremont, CA
Local Conductivity Mapping using Kelvin Probe Force Microscopy Impedance Modeling Completed	In Kelvin probe force microscopy (KPFM), 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 electrical properties can be challenging. During a summer XSEDE EMPOWER project, simulations combining tip-sample electrostatic modeling with a Langragian impedance model revealed new experimental conditions that best probe specific sample electrical properties such as conductivity and dielectric constant. To test these predictions, the student will write Python code to perform and analyze KPFM and/or electrochemistry experiments; these new experiments and analyses will be shared with other researchers via open-source Python code and a web interface for those without programming experience.	Ryan Dwyer University of Mount Union Alliance, OH
Assessing the Knotting and Unknotting Mechanism of Proteins Completed	Identified in approximately 1% of the Protein Data Bank (PDB) entries, knotted proteins have been connected to diseases, such as Alzheimer's disease and Parkinson's disease. Therefore, understanding how the primary structure of proteins folds and knots into a unique configuration has been a challenge in the biophysical community. As a first step towards the goal of better understanding the knotting/unknotting mechanism in proteins, the student will learn basic computational molecular science techniques.	Luiz Oliveira Mount Vernon Nazarene University Mount Vernon, OH
Evaluation of Inference Pipeline for TrackML (Learner Position) Completed	The main goal of this project is to develop and evaluate a deep learning pipeline for reconstructing 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 work with deep learning models including graph neural networks (GNNs) and multilayer perceptron (MLP) and compare them in terms of accuracy and computing time.	Alina Lazar Youngstown State University Youngstown, OH
Simulation of Proposed Bio-based Asphalt Replacement Materials using Molecular Dynamics Completed	Collaborators have devised (experimentally) a sustainable bio-sourced alternative material that can reduce needs in pavement for petroleum-based asphalt. We have estimated model compositions (via Reverse Monte Carlo computations) that can represent this bio-based multicomponent system in molecular simulations. In the project, fully atomistic molecular dynamics simulations will be conducted and interpreted to infer how the presence and activity of different molecule types impacts the predicted mechanical properties.	Michael Greenfield University of Rhode Island Kingston, RI
Geospatial Inspired Community Characterization with High Dimensional Data (Learner Position) Completed	We have been studying the geospatial clustering of high-dimensional data for health risk management. In particular, we are interested to find out the impact of various social determinants on residents' overall mental health for a community. We approach this problem from three directions: 1) geostatistics, 2) graph learning, 3) visualization	Xinlian Liu Hood College Frederick, MD
HPC Performance Evaluation Completed	This position will be assistant to the PPerfLab at Portland State. Specifically, the student will do test runs and test out portions of the Crux workflow critical path tool and other research efforts. This involves runs on the Coeus Cluster on campus, and learning performance measurement tools.	Karen L. Karavanic Portland State University Portland, OR
Phase Stability and Intercalant Ordering in V2O5 Cathodes Completed	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.	Hartwin Peelaers University of Kansas Lawrence, KS
Computational Investigation of Knee Joint Replacement Completed	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. Realistic representations will be obtained from 3D reconstructed X-ray CT images and quantified for mechanical analysis.	Jun Li University of Massachusetts-Dartmouth North Dartmouth, MA
Using Machine Learning for Categorizing Satellite Data Completed	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	Sanish Rai Longwood University Farmville, VA
RNA Comparative Analysis of Covid-19 Strains Completed	This is a continuation on the Spring 2021 Covid analytics project. The objective is to assess the fine-grained similarities of the 70,000 samples and to derive a means to expand that to the more than 200,000 samples now available. The key question is whether the fine-grained dissimilarities can be clustered and create a mutation tree that could be mapped across the world.	Stephen Wheat Oral Roberts University Tulsa, OK
Predictive Analytics for Extreme-Scale Scientific Computing Integrity using Provenance Data (Apprentice Position) Completed	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.	Nitin Sukhija Slippery Rock University of Pennsylvania Slippery Rock, PA
Pathfinding with Assistance of UAV for Multiple Targets (Learner Position) Completed	This is follow-up research of Unmanned Aerial Vehicle (UAV) for pathfinding. With the help of HPC, more UAV based applications can be possible. In this project, we will continue using computer vision techniques for multiple targets optimal pathfinding with the assistance of UAV. UAV can capture wider view than humans. Thus, it provides better solution for a path from source to destination. This optimization problem has many real applications such as helping rescue in flooding, fire or earthquake. The students will be asked to evaluate the pathfinding algorithms in XSEDE HPC and to implement with UAV that has been built with previous funding support.	Jiang Lu University of Houston-Clear Lake Houston, TX
Mixing Efficiency in Microfluidic Systems with Non-Newtonian Fluids (Learner Position) Completed	Microfluidic devices play crucial roles in a wide range of applications but efficient mixing stands as a significant hurdle. This circumstance is problematic as efficient microfluidic mixing is critical for creating homogeneous fluid environments necessary for chemical and biological applications. This project will investigate the mixing efficiency of suppressed Reynolds number flows using computational fluid dynamics, through the Ansys Fluids suite (ANSYS), to establish new foundational knowledge and approaches for designing passive microfluidic mixers to integrate within fluidic systems, such as bioprinters.	STEVEN SANTANA Harvey Mudd College Claremont, CA
Evaluation of Inference Pipeline for TrackML (Apprentice Position) Completed	The main goal of this project is to develop and evaluate a deep learning pipeline for reconstructing 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 work with deep learning models including graph neural networks (GNNs) and multilayer perceptron (MLP) and compare them in terms of accuracy and computing time.	Alina Lazar Youngstown State University Youngstown, OH
Geospatial Inspired Community Characterization with High Dimensional Data (Intern Position) Completed	We have been studying the geospatial clustering of high-dimensional data for health risk management. In particular, we are interested to find out the impact of various social determinants on residents' overall mental health for a community. We approach this problem from three directions: 1) geostatistics, 2) graph learning, 3) visualization	Xinlian Liu Hood College Frederick, MD