Welcome to the 2023 NSF MSGI Summer Research Symposium
This unique twoday virtual event showcased the research and learning experiences of 2023 MSGI doctoral student interns spending their summer at Department of Energy (DOE) and other federal national laboratories across the country. Presentations highlighted the diverse areas of applied mathematics, pure mathematics, and statistics.
Scheduled topics included researchfocused traditional style presentations. This event also featured two panel discussions involving current mentors and former MSGI interns.
Highlights of the event:
 NSF Welcome, An overview of the NSF MSGI program and its initiative to strengthen the next generation of mathematical scientists by applying advanced mathematical and statistical techniques to “real world” problems.
 Mentor Panel, Distinguished panelists from national laboratories will highlight the role of mathematics and statistics graduate education in preparing students for careers in science and industry, and the distinctive contribution of the MSGI internship.
 Alumni Panel, Former participants will share how the NSFMSGI program has impacted their career development.
 Presentations, Current participants will present the results of their research for the Division of Mathematical Sciences.
Schedule
All times are in Eastern Daylight Time

August 22 ^{nd}, 2023
Time (EST)
Presenter
Laboratory
Presentation Title
Mentor
10:0010:05 am
David Manderscheid
National Science Foundation
Introductory Remarks
10:0510:15 am
Kenneth Oswald
Oak Ridge Institute for Science Education
Overview of the NSFMSGI Program
10:1510:30 am
Jiyoung Park
National Institute of Standards and Technology
Uncertainty Quantification in UNet Image Segmentation with Geometric Encoding
Gunay Dogan
10:3010:45 am
Yinxi Pan
Argonne National Laboratory
Machine Learning Based Algorithms for Bayesian Inverse Problem (video)
Vishwas Rao
10:4511:00 am
Shuo Yan
Argonne National Laboratory
Constrained Reinforcement Learning by Lagrangian Primaldual Optimization (video, starts at 13:45)
Yixuan Sun
11:0011:15 am
Irem Altiner
Oak Ridge National Laboratory
Reinforcement Learning in Optimizing the Electric Vehicle Battery System Coupling with Driving Behaviors (video, starts at 25:27)
Shiqi (Shawn) Ou
11:1511:30 am
Eleanor Belkin
Oak Ridge National Laboratory
Communicationefficient Federated Learning Algorithm through the Use of Matrix Differential Equations (video, starts at 38:12)
Paul Laiu
11:3011:45 am
Killian Wood
National Renewable Energy Laboratory
Achieving Nash Equilibrium with Decisiondependent Data in Renewable Energy Markets (video, starts at 49:21)
Ahmed Zamzam
11:4512:00 pm
Karen Medlin
Argonne National Laboratory
Imbalanced Data Classification  an Introduction (video, starts at 1:00:51)
Krishnan Raghavan
12:0012:15 pm
Break
12:1512:30 pm
Madhumita Roy
Oak Ridge National Laboratory
Exploring Numerical Errors in Simulations of the Wave Equation with Large Grid Spacings (video)
James J Nutaro
12:3012:45 pm
Chris Vales
Lawrence Livermore National Laboratory
Energy Conserving Quadrature Based Dimensionality Reduction for Nonlinear Hydrodynamics Problems (video, starts at 11:26)
Youngsoo Choi
12:451:00 pm
Yotam Yaniv
Lawrence Berkeley National Laboratory
Empirical Study of Graph Partitioning Methods (video, starts at 22:59)
Xiaoye (Sherry) Li
1:001:15 pm
Caroline Hammond
Lawrence Berkeley National Laboratory
Solving the Triangle Counting Problem on Networks Using TimeDomain Computing (video, starts at 35:44)
Patricia GonzalezGuerrero and George Michelogiannakis
1:151:30 pm
Weichen Xie
Fermi National Accelerator Laboratory
Control Optimization Landscape Analysis of a TwoQubit System (video, starts at 48:35)
A. Baris Ozguler and Benjamin Lienhard
1:301:45 pm
Paul Simanjuntak
Fermi National Accelerator Laboratory
Quantuminspired Optimization Algorithm (video, starts at 57:56)
Prasanth Shyamsundar and Gabriel Perdue
1:452:00 pm
Noah Corbett
U.S. Army Corps of Engineers, Engineer Research and Development Center
Predicting State Switches in Chaotic Dynamical Systems (video, starts at 1:10:57)
Kevin Pilkiewicz and Michael Mayo
2:002:10 pm
Break
2:102:55 pm
Emily Casleton
BriMathias Hodge
Gabriel Perdue
Panos Stinis
LANL
NREL
FNAL
PNNL
Mentor Panel Discussion
NSFMSGI and Careers in the Mathematical and Statistical Sciences (video)
2:553:00 pm
Break
3:003:15 pm
Damien Beecroft
Pacific Northwest National Laboratory
Multifidelity Finite Basis Physics Informed Neural Networks (video)
Amanda Howard and Panos Stinis
3:153:30 pm
Yuan Luo
Lawrence Berkeley National Laboratory
Topological Optimization with Distance to Measure (video, starts at 10:55)
Arnur Nigmetov
3:303:45 pm
Zhifeng Wei
Oak Ridge National Laboratory
Recovering Cluster Structure using Graph Embedding Methods in Perturbed Networks (video, starts at 19:52)
Pablo Moriano
3:454:00 pm
Tobias Timofeyev
National Renewable Energy Laboratory
KuramotoSakaguchi OscillatorInspired Fixed Point Algorithm for the AC Power Flow Problem (video, starts at 32:02)
Bai Cui
4:004:15 pm
Ziyu Lu
Oak Ridge National Laboratory
Interpreting Deep Learning Models of Neuronal Dynamics through the Lens of Connectome (video, starts at 47:02)
SeungHwan Lim
4:154:30 pm
Break
4:304:45 pm
Yao Yao
Oak Ridge National Laboratory
An Approximate Gradient Descent Algorithm for Sparse Recovery of Dynamic Systems (video)
Tran Hoang and Guannan Zhang
4:455:00 pm
Anthony Sulak
National Energy Technology Laboratory
Pairing Neural Networks and Physicsbased Equations to Optimize Gas Drilling (video, starts at 11:21)
Wu Zhang
5:005:15 pm
Sudheer Shukla
Oak Ridge National Laboratory
Impacts of Shopping Access, Race, and Transportation Availability on Food Security and Nutrition in LowIncome Households of US Cities (video, starts at 22:24)
David McCollum
5:155:30 pm
Yixin Lin
Argonne National Laboratory
Attributed Graph Matching via Optimal Transport (video, starts at 35:52)
Hongwei Jin
5:305:45 pm
Shen Huang
National Renewable Energy Laboratory
Comparison of Wind Turbine Wakes and Loads across Varying Fidelity of Turbine Models
Ashesh Sharma and Ganesh Vijayakumar
5:456:00 pm
Mark Walth
National Renewable Energy Laboratory
Frequency Synchronization in the Power Grid (video, starts at 46:11)
BriMathias Hodge

August 23rd, 2023
Time (EST)
Presenter
Laboratory
Presentation Title
Mentor
10:0010:15 am
Jinghao Chen
Los Alamos National Laboratory
William Hlavacek
10:1510:30 am
Jongwon Kim
U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory
Stochastic Modeling of Soil Microbial Data (video, starts at 13:21)
Robyn Barbato
10:3010:45 am
Isaac Tate
U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory
Dark Noise: Modelling an Electrical Signal Propagating through Melanized Fungal Tissue (video, starts at 28:58)
Robert Jones
10:4511:00 am
Zachariah Malik
Argonne National Laboratory
Generative Models Learn Potentials (video, starts at 35:28)
Romit Maulik
11:0011:15 am
Sarah Murphy
Pacific Northwest National Laboratory
Stacking PINNs for Dynamical Systems (video, starts at 45:58)
Amanda Howard and Panos Stinis
11:1511:30 am
Isaac Ray
Los Alamos National Laboratory
Bayesian Nonparametric Deep Clustering of Remote Sensing Scenes through Transductive Transfer Learning
Alexei Skurikhin and Emily Casleton
11:3011:45 am
Jeannie Schrieber
National Institute of Standards and Technology
Topological Shape and Data Analysis (video, starts at 58:21)
Zachary Grey
11:4512:00 pm
Break
12:0012:15 pm
Qipeng Qian
Argonne National Laboratory
Tanwi Mallick
12:1512:30 pm
Jeremy Lilly
Los Alamos National Laboratory
Methods for Efficient TimeStepping in Shallow Water Models (video, starts at 9:10)
Mark Petersen
12:3012:45 pm
Edward Terrell
Sandia National Laboratories
Semiimplicit Time Integration for Spatiallylocalized Projectionbased Reduced Order Models (video, starts at 21:11)
John Tencer, Francesco Rizzi, and Patrick Blonigan
12:451:00 pm
Julie Sherman
U.S. Army Corps of Engineers, Cold Regions Research and Engineering Laboratory
Statistical Analyses of PFAS Sorption Experiments
Amanda Barker
1:001:15 pm
Chun Yin Siu
Lawrence Berkeley National Laboratory
Exploration in Topological Statistics of Crystal and Cosmological Data (video, starts at 35:12)
Dmitriy Morozov
1:151:30 pm
Ariel Goodwin
National Renewable Energy Laboratory
A Geometric Approach to Optimal Power Flow and Constrained Optimization on Manifolds (video, starts at 46:14)
Jonathan Maack and Devon Sigler
1:301:45 pm
Tyler Fara
National Renewable Energy Laboratory
Comparing the Effect of Equation of State Choice on Supercritical CO _{2} Simulation (video, starts at 58:24)
Michael Martin
1:452:00 pm
Break
2:002:10 pm
Break
2:102:55 pm
Kristine Gierz
Anthony Gruber
Ratna Khatri
Chul Moon
Matea Santiago
NIST
SNL
US NRL
Southern Methodist U.
U. of Arizona
Alumni Panel Discussion
NSFMSGI and Career Development (video)
2:553:00 pm
Break
3:003:15 pm
Ying Liu
Argonne National Laboratory
Advancing Graph Neural Diffusion with SemiImplicit Neural ODEs (video)
Hong Zhang
3:153:30 pm
Geigh Zollicoffer
Los Alamos National Laboratory
Scalable Matrix Diagonalization with Graph Isomorphism Networks in the AlphaZero Framework (video, starts at 08:37)
Manish Bhattarai and Phil Romero
3:303:45 pm
Dipak Dulal
U.S. Department of Agriculture, Forest Service Northern Research Station
Exploring and Analyzing Wildland Fire Data via Machine Learning Techniques (video, starts at 22:44)
Nicholas Skowronski, Michael Gallagher, and Joseph Charney
3:454:00 pm
Alokesh Manna
U.S. Department of Agriculture, Forest Service Southern Research Station
Development of a Statistical Predictive Model for Daily Water Table Depth and Elevation and Important Variables Selection for Inference (video, starts at 36:03)
Amatya Devendra and Sushant Mehan
4:004:15 pm
Jimi Kim
Oak Ridge National Laboratory
PrivacyPreserving Development of Dynamic Treatment Regimes for COVID19 (video, starts at 50:44)
Robert Bridges and Vandy Tombs
4:154:20 pm
Break
4:204:35 pm
Luis Picon Nunez
Oak Ridge National Laboratory
JeanLuc Fattebert
4:354:50 pm
Matthew Kearney
U.S. Army Corps of Engineers, Geospatial Research Laboratory
Point Set Registration with Applications to Lidar (video, starts at 12:04)
Weston Baines and Carter Sturm
4:505:05 pm
Ilyas Mustapha
Oak Ridge National Laboratory
Fourier Collocation Method for Nonlocal Problems
Olena Burkovska
5:055:20 pm
Joseph Hunter
Oak Ridge National Laboratory
RealizabilityPreserving DGIMEX Method for TwoMoment Model of Special Relativistic Transport (video, starts at 25:02)
Eirik Endeve
5:205:35 pm
Michael Kielstra
Lawrence Berkeley National Laboratory
Tensor Butterfly Factorization (In Parallel!) (video, starts at 39:23)
Yang Liu
5:355:50 pm
Nian Liu
National Institute of Standards and Technology
Edge Detection in Gap Analysis (video, starts at 50:53
John Lu
5:506:00 pm
Jeremy Tyson, Swatee Naik, and Timothy Hodges
National Science Foundation
Closing Remarks
Moderators
Dr. Tim Hodges, Program Officer, Algebra and Number Theory Program, National Science Foundation

Program Responsibilities include:
 Algebra and Number Theory
 Secure and Trustworthy Cyberspace
 NSF Mathematical Sciences Graduate Internship
Dr. Hodges is Professor Emeritus of Mathematics at the University of Cincinnati. He is currently on a temporary assignment as an NSF Program Officer in the Algebra and Number Theory Program. His areas of research interest include noncommutative algebra and geometry, quantum symmetry and cryptography.
Dr. Jeremy Tyson, Program Director, Division of Mathematical Sciences, National Science Foundation

Program Responsibilities include:
 Analysis
 Division of Mathematical Sciences International Collaborations
 NSF Mathematical Sciences Graduate Internship (MSGI, INTERN)
 Quantum Information Science and Engineering Working Group
Dr. Tyson is a Professor of Mathematics at the University of Illinois at UrbanaChampaign. Dr. Tyson is currently on a temporary assignment as an NSF Program Officer in the Analysis Program. His areas of research interest include analysis in metric spaces, geometric mapping theory, geometric measure theory, fractal geometry, and subRiemannian geometry. Dr. Tyson is particularly interested in notions of firstorder differential calculus and differential geometry in metric measure spaces lacking any a priori Euclidean or Riemannian structure.
Dr. Swatee Naik, Program Director, Division of Mathematical Sciences, National Science Foundation

Program Responsibilities include:
 Conferences and Workshops in the Mathematical Sciences
 Focused Research Groups in Mathematical Sciences
 Geometric Analysis
 Mathematical Sciences Graduate Internship
 Mathematical Sciences Postdoctoral Fellowships
 MPSAscend Postdoctoral Fellowships
 Research Training Groups
 Topology
Dr. Naik’s research interests are in low dimensional topology. She is a permanent program officer at NSF in the disciplinary programs Topology and Geometric Analysis. Her experience at NSF has included management teams for the Graduate Research Fellowships, NSF Research Traineeships, Research Experience for Undergraduates, Research Training Groups, and Mathematical Science Infrastructure program. Dr. Naik is also an Emeritus Professor at the University of Nevada, Reno.
Dr. Sushant Mehan, Assistant Professor of Water Resources Engineering in the Department of Agricultural and Biosystems Engineering at South Dakota State University

Sushant Mehan earned his bachelor’s degree (2011) and master’s degree (2014) in agricultural engineering from Punjab Agricultural University, India. At Purdue University in 2018, he earned a Ph.D. in agricultural and biological engineering, and he currently is an Assistant Professor of Water Resources Engineering in the Department of Agricultural and Biosystems Engineering at South Dakota State University, Brookings, SD. His research plan entails advancing the field of water resources management through geospatial analysis, hydrologic modeling, remote sensing products, wireless sensor networks, data analytics, integrated watershed modeling, and the development of decision support systems. He has published 19 peerreviewed journal articles and four book chapters and received an international patent for a soybased filtration system. He was named Early Career Engineer of the Year in 2022 by the Association of Agricultural, Biological, and Food Engineers of Indian Origin. Promoting Diversity, Equity, Inclusion, and Belonging is fundamental to Mehan’s personal and professional philosophy.
Dr. Ahmed Zamzam, Senior Research Scientist at the National Renewable Energy Laboratory(NREL)

Dr. Ahmed Zamzam is a senior research scientist at the National Renewable Energy Laboratory, where he has been part of the energy systems control and optimization group since 2019. Before that, he obtained his Ph.D. in electrical engineering from the university of Minnesota. He has obtained his MSc and BSc from Nile University and Cairo University in 2015 and 2013, respectively. His research interests span machine learning and optimization from managing distributed energy resources in the energy systems, tensor decompositions, and datadriven protection and control of power systems.
Dr. Benjamin Lienhard, SNSF Postdoctoral Research Fellow at Princeton University

Dr. Benjamin Lienhard is an SNSF postdoctoral research fellow at Princeton University, where he focuses on efficient quantum processor calibration and control. He received his PhD from the Electrical Engineering and Computer Science Department at MIT and an MS and BS from the Department of Information Technology and Electrical Engineering at ETH Zurich. While at MIT, Ben worked on logical qubits using solidstate quantum emitters and readout and control of superconducting qubits. He was an executive member of MIT's Interdisciplinary Quantum Information Science and Engineering program and part of the graduate student advisory group to MIT's dean of engineering.
Dr. John Lu, Mathematical Statistician at National Institute of Standards and Technology (NIST)

Dr. John Lu is a mathematical statistician at National Institute of Standards and Technology (NIST), where he collaborates with scientists, developing innovative statistical solutions to physical and chemical measurement problems, including statistical methods for imaging and spectraltype data. His interdisciplinary research interests in the past have carried over from chaos in physics to numerical weather prediction, and from time series to hydrology runoff prediction. In recent years he mostly worked on statistical issues related to standard developments in medical imaging including CT and optical imaging. He is also getting interested in some challenging inferential issues facing data sciences and AI, and forensic sciences.
Mentor Panel
Dr. Emily Casleton, Deputy Group Leader of the Statistical Sciences Group, Los Alamos National Laboratory

Emily Casleton is currently the deputy group leader of the statistical sciences group, but was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a post doc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015, Emily has routinely collaborated with seismologists, nuclear engineers, physicists, geologists, chemists, and computer scientists on a wide variety of cool datadriven projects. Most recently, she has been the PI of a data analytics project under the NA22 venture MINOS; coorganizer of the invited CCS6 seminar series; and cochair of CoDA, the conference that brought her here a decade ago.
Dr. BriMathias Hodge, Associate Professor, Department of Electrical, Computer and Energy Engineering and an Associate Director and Fellow of the Renewable and Sustainable Energy Institute (RASEI) at the University of Colorado Boulder

BriMathias Hodge is an Associate Professor in the Department of Electrical, Computer and Energy Engineering and an Associate Director and Fellow of the Renewable and Sustainable Energy Institute (RASEI) at the University of Colorado Boulder. He is also a Chief Scientist and a Distinguished Member of the Research Staff in the Power Systems Design & Planning Group at the National Renewable Energy Laboratory (NREL). His research focuses on the modeling and simulation of power and energy systems, with an emphasis on the operational and planning challenges posed by the integration of renewable energy sources, such as wind and solar power. He is an author on over 100 journal articles in this area and has received five best paper awards at the IEEE Power & Energy Society General Meeting. At NREL he has received Outstanding Mentor Awards on five occasions, the NREL President’s Award in 2016, the NREL Outstanding Performance Award and Director's Publication Impact Award in 2019, and the NREL Chairman's Award in 2020. Dr. Hodge also received a Fulbright Fellowship in 2016 for a sabbatical at VTT in Finland.
Dr. Gabriel Perdue, Quantum Simulation for Physics, Fermi National Accelerator Laboratory

Gabriel Perdue received a PhD in physics from the University of Chicago in 2008, studying matterantimatter asymmetries in rate neutral meson decays. He subsequently worked as a Postdoctoral Fellow at the University of Rochester on MINERvA, working to measure neutrino cross sections. He joined Fermilab in 2013 to continue work on neutrino cross sections at MINERvA and as part of the GENIE event generator collaboration. He subsequently shifted into applied machine learning and from there into quantum computing. Today he works primarily in quantum simulation for physics and on the application of machine learning to the control and optimization of quantum computers.
Dr. Panos Stinis, Lead, Computational Mathematics Group, Pacific Northwest National Laboratory

Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems, multiscale modeling, uncertainty quantification, and machine learning. Stinis studied aeronautical engineering at the Technical University of Athens, Greece. He earned his PhD in applied mathematics in 2003, from Columbia University in New York, in the area of model reduction. He began his career at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research, where he worked on applying model reduction methods to hyperbolic systems and in developing techniques for locating and tracking singularities of partial differential equations. In 2008, he became a faculty member at the Mathematics Department at the University of Minnesota, where he worked on renormalization, mesh refinement, particle filtering and optimization. He moved to PNNL in 2014, where he is currently leading the Computational Mathematics group. He serves as the codirector of the PhysicsInformed Learning Machines (PhILMs) multiinstitution collaboration and leads the Digital Twin Component Development thrust of the Energy Storing Materials Initiative.
Alumni Panel
Dr. Kristine Gierz

Kristine Gierz completed her undergraduate education at the University of California, Los Angeles as a Biology major and was inspired by an environmental conservation internship in Fiji to pursue graduate studies in Biostatistics. She participated in the National Science Foundation’s Mathematical Sciences Graduate Internship in the summer of 2018 at the National Institute of Standards and Technology, and went on to be awarded the Department of Defense’s SMART Scholarship for Service program. She completed her PhD in December of 2020 and fulfilled her service at the Secretary of the Air Force’s Studies, Analyses, and Assessments division in the Pentagon, and then returned to NIST as a Mathematical Statistician. She lives in College Park with her husband and three dogs, enjoys all types of outdoor activities (including volunteering at a horse therapy site for special needs children), is a reluctant distance runner, and an extremely amateur ukulele player.
Dr. Anthony Gruber

Anthony Gruber is the John von Neumann fellow in computational science at Sandia National Laboratories in Albuquerque, New Mexico, where he develops structureinformed surrogate models of largescale dynamical systems. He received a Ph.D. in Mathematics from Texas Tech University in 2019, after completing an NSFMSGI internship at Oak Ridge National Laboratory in 2018. His research interests are relatively broad, as reflected by publications in subjects ranging from pure and applied mathematics to various areas of computer science, physics, and engineering.
Dr. Ratna Khatri

Ratna Khatri is a Research Scientist in the Optical Sciences Division at the U.S. Naval Research Laboratory (NRL), and currently serving as the Vice President of the SIAM WashingtonBaltimore Section. She joined NRL as the Jerome and Isabella Karle Distinguished Scholar Fellow in 2021, after getting her PhD in Mathematics from George Mason University in 2020. As a graduate student, she spent two summers at Argonne National Laboratory as an NSF MSGI Fellow in 2017, and Givens Associate Research Intern in 2018. Her area of research is the intersection of PDEconstrained optimization, deep learning, and inverse problems in imaging science.
Dr. Chul Moon

Chul Moon is an assistant professor of the Department of Statistics and Data Science at Southern Methodist University. He received his Ph.D. in Statistics from the University of Georgia in 2018. In 2017, he participated in the NSFMSGI internship at Sandia National Laboratories in Albuquerque, New Mexico.
Dr. Matea Santiago

Matea Santiago is a Postdoctoral Research Associate at the University of Arizona. She graduated with her PhD from the University of California, Merced in 2021. Her research focuses on computational fluid dynamics applied to biological organisms.