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.
Hybrid organic-inorganic lead halide perovskites are of great interest in the scientific community due to their impressive power conversion efficiency for solar cell applications. However, despite numerous studies on their 3D structural properties, relatively little progress has been made in our understanding of their various properties, such as the role of hydrogen bonding in orthorhombic phases in the organic lead(halides). Theoretical approaches have been found to be very powerful predictive tools for exploring and "tailoring" new perovskites with desired properties. In this project, we'll use ab-initio density functional theory (DFT) and molecular dynamics (MD) methods to predict and study such properties extensively using high-performance computing and compare it with experimental results found at the Center for Advanced Photovoltaics at South Dakota State University, and elsewhere.
Small conductance calcium-activated potassium (SK) channels play critical roles in cardiac excitability. The goals of the project is to understand the atomistic regulation of SK channels by calmodulin and PIP2. The overall thrust of the project is to design a comprehensive computational model to address successively the functional activation and regulation of SK channels in the heart.
This project will investigate high-resolution Micro-CT image segmentation and visualizations of additively manufactured (3D printed) polymer fiber composites. A key challenge in using the X-ray CT imaging technique is to accurately segment material phases in contact so as to faithfully visualize the microstructure morphology, which provides the basis for image-based finite element modeling and analysis.
Membrane proteins are attached to or associated with membranes, and they perform a wide range of functions from signal transduction to membrane fusion that are vital to cell growth, replication, and movement. Here, we develop and apply highly accurate and efficient methods for multiscale simulations of critical membrane proteins in order to gain insights into their acting mechanisms and underlying dynamics.
Hai Lin
University of Colorado Denver/Anschutz Medical Campus
In this project we are developing a technology for controlling cell behavior in living cultures based on real time images acquired from high resolution microscopy. We are currently focusing on developing parallelized image processing and controls algorithms for directing cell behavior in real time based on the microscopy observations.
Construction and Support of the Anvil Cluster. This person would get familiar with how to build (storage, nodes, sys and scheduling) tasks in building the Anvil Cluster.
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, 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 (with PDB ID: 6a2f) for drug design purposes.
For this position, we plan to extend our study on strongly bound doped metalloid atomic clusters as models for the active sites in heterogenous catalysis. Specifically, we intend to explore iridium doped silicon cluster with different cluster sizes and dopant concentrations. The results from this study will be compared to previous studies on related systems.
Dynamic Functional Connectivity of Functional Magnetic Resonance Imaging (fMRI) data has been developed in the last decade, with the seminal methods paper by Sakoglu et al. which was applied to schizophrenia fMRI data [1-4]. A MATLAB-based software toolbox named DynaConn was developed by the PI and his former research assistant student [5]. The DynaConn has been made freely available by the PI for the neuromainging community for research [6]; however; due to lack of funding and other resources, further software development and maintainence has not been possible and the toolbox's use by the has been limited. Specifically, the toolbox was developed for dynamic time-series analysis of independent component analysis (ICA) results of fMRI data; the toolbox needs to be developed for region/atlas-based analysis, which will be nicely complementary to ICA-based analysis. The neuroimaging community will greatly benefit from such toolbox which will be offered freely by the PI. Its further development will also facilitate region-based dynamic functional connectivity analyses of some existing fMRI data by the PI and the student assisting the PI in this project.
Reference:
[1] Sakoglu U, Calhoun VD, "Temporal Dynamics of Functional Network Connectivity at Rest: A Comparison of Schizophrenia Patients and Healthy Controls," Proc. 15th Annual Meeting of the Organization for Human Brain Mapping, Vol. 47, Suppl. 1, pp. S169, June 2009, San Francisco, CA.
[2] Sakoglu U, Michael AM, Calhoun VD, "Classification of schizophrenia patients vs healthy controls with dynamic functional network connectivity," Proc. 15th Annual Meeting of the Organization for Human Brain Mapping, Vol. 47, Suppl. 1, pp. S57, June 2009, San Francisco, CA.
[3] Sakoglu U, Calhoun VD, "Dynamic windowing reveals task-modulation of functional connectivity in schizophrenia patients vs healthy controls," Proc. 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine, #3676, April 2009, Honolulu, HI.
[4] Sakoglu U, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD, "A Method for Evaluating Dynamic Functional Network Connectivity and Task-Modulation: Application to Schizophrenia," Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA), Special Issue on MR Imaging of Brain Networks, Vol. 23, pp. 351-366 (2010).
[5] Esquivel J, Mete M, Sakoglu U, "DynaConn: A Software for Analyzing Brain's Dynamic Functional Connectivity from fMRI Data," Midsouth Computational Biology and Bioinformatics Society Conference (MCBIOS), March 2014, Stillwater, OK.
[6] http://www.drsakoglu.com/p/dynaconndfctoolbox.html
Using density functional theory, as implemented in the VASP code, and python scripts based on the Atomic Simulation Environment (ASE), we will investigate if V2O5, a layered material, can be used as a sensor material for simple molecules, such as NH3, CO, etc. Some experiments reveal that that should be possible, and in this project we will unravel the mechanisms at an atomic level.
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 pore-forming membrane channels that span the membranes of two neighboring cells, connecting their cytosols to facilitate cell-to-cell communication. We aim to use GPU-accelerated MD simulations to understand the molecular requirements underlying solute permeation through these intercellular channels.
Organic photoredox catalysts provide access, under mild conditions, to reactive intermediates that are otherwise difficult or impossible to produce; and in recent years these catalysts have demonstrated their utility in numerous C-C bond forming reactions that are of interest to the synthetic practitioner. While most photochemistry occurs through triplet excited states there is experimental evidence to suggest that some photoredox catalysts can participate in productive photochemistry through their first excited singlet states which engenders greater oxidizing/reducing power than would be available through the corresponding first triplet excited states. As photoredox catalysis has gained traction, DFT has emerged as a powerful predictive tool for the investigation and design of these molecules. This project endeavors to use DFT calculations to explore structure-activity relationships for heavier core-chalcogen substituted derivatives of phenoxazine catalysts and the student involved will carry out calculations to characterize how the nature of the ring-chalcogen atom influences thermodynamic and spectral properties in effort to understand how this influences intersystem crossing and population of the available excited states for this class of catalysts.
Organic photoredox catalysts provide access, under mild conditions, to reactive intermediates that are otherwise difficult or impossible to produce; and in recent years these catalysts have demonstrated their utility in numerous C-C bond forming reactions that are of interest to the synthetic practitioner. Moderately reducing acridinium salts have demonstrated their utility but their widespread use is hindered by their inherent instability and the photochemical lifetimes could benefit from optimization; our previous studies with related neutral radicals demonstrate that common ionic liquid anions can have a marked influence on these particular properties so the goal of this project is to investigate the influence of including different anions that are common in ionic liquids into catalyst structures based on this molecular framework. DFT has emerged as a powerful predictive tool for the investigation and design of photredox catalysis and this project endeavors to use DFT calculations, complemented by experimental studies carried out by the PI, to explore structure-activity relationships; the student involved will carry out calculations to characterize thermodynamic and spectral properties in effort to understand how structural modifications influence stability and lifetime of the photochemically active excited states of acridinium-based photoredox catalysts.
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.
Integrating multiple types of biological data for cancer samples shows potential in identifying new biomarkers to predict patient outcome. The relationship between them has been speculated but needs further work to confirm. We focused on the mRNA, proteomics, and tissue slide images of four different types of cancer: BRCA, OV, READ, and COAD. We will preprocess the data by selecting qualified data samples and extracting morphological features from tissue slides. Then we will perform pair-wise correlation analysis on genes and images. We will present the results side by side to detect patterns of correlations. We will investigate how mRNA and proteomics correlate with the morphological features. Finally, we will examine our results with clinical data to study the feasibility of using image features as markers of cancer prognosis.
Students will use Artificial Intelligence, Machine Learning, Deep Learning technologies, and Molecular Modeling applied to nuclear and radiochemistry challenges. The students will learn to apply data science and molecular modeling to develop efficient computational protocols to optimize binding to rare earth elements (REEs), lanthanides, and actinides in nuclear and radiochemical applications. The students will focus on nuclear materials, actinides used for radiotherapy for cancer treatment (such as actinium), and binding to REEs and actinides for separations (such as selective binding to REEs needed in critical materials, and uranium and plutonium in selective complexation).
Inorganic carbon in the form of bi-carbonate is transported into the cytoplasm of cyanobacteria through bicarbonate transporters and allows for carbon fixation through photosynthesis. Among these transporters, the low-affinity, high-flux bicarbonate transporter BicA is a well-studied bicarbonate transporter in the Synechocystis cyanobacteria species. Using MD simulations, this study aims to elucidate the mechanism of bicarbonate transport in BicA and identify possible mutations to enhance bicarbonate uptake.
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, 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 (with PDB ID: 6a2f) for drug design purposes.