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Presented by National Science Foundation (NSF) Mathematical Sciences Graduate Internship (MSGI) Program. NSF MSGI is funded by the Division of Mathematical Sciences.

Welcome to the NSF MSGI Summer Research Symposium

August 25th 10:00AM to 6:00PM ET
August 26th 10:00AM to 3:30PM ET 

This unique two day virtual event will showcase the research and learning experiences of MSGI doctoral student interns spending their summer at Department of Energy (DOE) and other federal national laboratories across the country. Presentations will highlight the diverse areas of applied mathematical sciences.

Scheduled topics will include research-focused traditional style presentations. In addition to featuring MSGI interns, this event will spotlight national laboratories engaging in advanced mathematical sciences research.

The following will be featured over the course of the event:
  • NSF Welcome and Plenary Session, 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.
  • Panel Discussion, "Preparing Students for Careers in the Mathematical Sciences." This session will feature distinguished panelists from national laboratories and highlight the role of mathematics and statistics graduate education in preparing students for careers in science and industry, and the distinctive contribution of internship programs such as MSGI.
  • Presentations, Research-focused traditional style presentations from mathematical sciences doctoral students. Research presentations will feature the following topics:
    • Fluids, Climate, and Atmospheric Science
    • Data Science and Machine Learning
    • Mathematical Biology
    • Numerical Methods
    • Physics
    • Quantum Computing
    • Stochastics and Statistics

Moderators

Dr. Yulia Gel, Program Director, Division of Mathematical Sciences, National Science Foundation

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Program Responsibilities include:

  • Algorithms for Modern Power Systems
  • Algorithms for Threat Detection
  • Computational and Data-Enabled Science and Engineering in Mathematical and Statistical Sciences
  • NSF Mathematical Sciences Graduate Internship
  • Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science
  • Statistics
  • Dr. Gel's Bio

    Dr. Gel is a Professor of Statistics at the University of Texas at Dallas. She is currently on a temporary assignment as an NSF Program Officer in the Division of Mathematical Sciences, Statistics Program. Her research agenda stems around  topological and geometric methods in statistics and machine learning, nonparametric statistics, inference and models for spatio-temporal processes and complex networks. The areas of applications include climate informatics, biosurveillance, and blockchain data analytics.

Dr. Jeremy Tyson, Program Director, Division of Mathematical Sciences, National Science Foundation

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Program Responsibilities include:

  • Analysis
  • Mathematical Sciences Postdoctoral Research Fellowships
  • NSF Mathematical Sciences Graduate Internship
  • Dr. Tyson's Bio

    Dr. Tyson is a Professor of Mathematics at the University of Illinois at Urbana-Champaign. 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 sub-Riemannian geometry. Dr. Tyson is particularly interested in notions of first-order 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

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Program Responsibilities include:

  • CAREER
  • Focused Research Groups in Mathematical Sciences
  • Geometric Analysis
  • Internships (MSGI, INTERN)
  • Mathematical Sciences Postdoctoral Fellowships
  • MPS-Ascend postdoctoral fellowships
  • Topology
  • Dr. Naik's Bio

    Dr. Naik’s research interests are low dimensional topology and knot theory. Since 1994 until 2019, she was a Professor of Mathematics at the University of Nevada, Reno. Dr. Naik joined NSF as a rotator in the Topology and Geometric Analysis programs in 2015 and was hired as a permanent program director in 2019. At NSF she has worked with multiple programs, including Graduate Research Fellowships, NSF Research Traineeships, Research Experience for Undergraduates, Research Training Groups, and Mathematical Science Infrastructure

Panelists

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Dr. Aditi Krishnapriyan, Applied Mathematician

  • Facility: Lawrence Berkeley National Laboratory
  • Faculty: University of California, Berkeley
  • Bio: https://a1k12.github.io

 

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Dr. E. Louise Loudermilk, Research Ecologist

 

Dr. Gabriel Perdue, Scientist

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Dr. Kevin R. Pilkiewicz, Research Chemist

Dr. Kevin R. Pilkiewicz

 

Dr. Stefan M. Wild, Deputy Division Director/Senior Computational Mathematician

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Schedule

All times are in Eastern Daylight Time  |  View the printable schedule (.pdf, 246 KB)

Symposium Moderators:
Yulia Gel, Program Director, National Science Foundation
Jeremy Tyson, Program Director, National Science Foundation
Swatee Naik, Program Director, National Science Foundation
Jennifer Burnette, Project Manager, Oak Ridge Institute for Science and Education

  • August 25th, 2022

    Welcome and Schedule of Events
    10:00AM
    Welcome from the Division Director
    • Dr. David Manderscheid, Division Director, Division of Mathematical Sciences, National Science Foundation
    Overview of the NSF Mathematical Sciences Graduate Internship and Schedule of Events
    • Jennifer Burnette, Project Manager, Oak Ridge Institute of Science and Education
    Research Presentations
    These sessions will feature 15-minute research-focused traditional style presentations from mathematical sciences doctoral students. Research presentations will feature the following topics: 1) Data Science and Machine Learning, 2) Fluids, Climate, and Atmospheric Science, 3) Mathematical Biology, 4) Numerical Methods, 5) Physics, 6) Quantum Computing, and 7) Stochastics and Statistics.
    Numerical Methods 10:30AM
    The Askey-Rahman-Suslov Nonsymmetric Poisson Kernel for Askey-Wilson Polynomials and its Special Values
    • Raymond Centner, National Institute of Standards and Technology
    Acceleration of Kernel Methods with Nystrom Approximation
    • Zezheng Song, Lawrence Berkeley National Laboratory
    Mathematical Biology
    11:00AM
    Simulating African Swine Fever Movement with SIR Models
    • Abigail D'Ovidio Long, United States Department of Agriculture: Animal and Plant Health Inspection Service
    Topological data analysis on LiDAR scans of the forest
    • Alvis Zhao, United States Department of Agriculture Forest Service
    Data Science and Machine Learning 11:30AM
    Causal inference and discovery with dynamic intervention for policy-making
    • Jimi Kim, Oak Ridge National Laboratory: Oak Ridge Leadership Computing Facility
    Geometric Scattering Priors and Differentiable Solvers for Inverse Problems
    • Oluwadamilola Fasina, Lawrence Berkeley National Laboratory
    How Robust are the Communities in Temporal Networks? A Comparative Analysis Using Community Detection Algorithms
    • Moyi Tian, Oak Ridge National Laboratory
    Generative Modeling and Parameter Identification of SDEs via Optimal Transport and Normalizing Flows
    • Jonah Botvinick-Greenhouse, Argonne National Laboratory, MCS Division
    Scalable hyperparameter optimization for neural networks
    • Madhu Gupta, Oak Ridge National Laboratory
    Variational Deep Learning for Image Segmentation
    • Liangchen Liu, National Institute of Standards and Technology
    Stochastics and Statistics
    1:00PM
    Techniques of Design of Experiments and Space Filling Designs
    • Manisha Garg, Argonne National Laboratory
    Predicting a continuous causal variable given ordinal outcomes and structural zeroes with application to submersed aquatic vegetation biomass
    • Julie Sherman, United States Geological Survey: Upper Midwest Environmental Sciences Center
    Stein-Variational Gradient Descent in Higher Dimensional Bayesian Inference
    • Muhammad Rao, Los Alamos National Laboratory
    Theoretical and Empirical Investigation of Gradient Estimators in Zeroth-order Optimization
    • Manushi Welandawe, Argonne National Laboratory
    Mathematical Biology 2:00PM
    Decoding Animal Behaviors: Using Information Theory to Explore Behavioral Dynamics in Golden Shiners
    • Katherine Daftari, United States Army Corps of Engineers: Engineer Research and Development Center
    Inference of dynamical states in behavioral recordings of socially interacting animals
    • Wai Ho Chak, Lawrence Berkeley National Laboratory
    Numerical Methods 2:30PM
    An exploration of multidimensional numerical integration techniques in PAGANI and m-CUBES
    • Madison Phelps, Fermi National Accelerator Laboratory
    Break 2:45PM
    Data Science and Machine Learning 3:00PM
    Transfer learning techniques for building damage assessment
    • Yandi Wu, United States Army Corps of Engineers: Engineer Research and Development Center: Geospatial Research Laboratory
    Fluids, Climate, and Atmospheric Science 3:15PM
    Pressure-dependant rheological stress model of continuum granular flows
    • Eunji Yoo, Lawrence Berkeley National Laboratory
    Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2
    • Nian Liu, Los Alamos National Laboratory
    Break 3:45PM
    Fluids, Climate, and Atmospheric Science Cont’d 4:00PM
    Mathematical approaches for effective meso-micro coupling
    • Jithin George, National Renewable Energy Laboratory
    High Amplitude Acoustic Propagation in Porous Media
    • Ryan McConnell, United States Army Corps of Engineers: Engineer Research and Development Center: Cold Regions Research and Engineering Laboratory
    Discovering reduced-order equations of motion for firebrand transport
    • Alexander Mendez, United States Department of Agriculture Forest Service
    Physics 4:45PM
    Machine Learning of Peridynamic Models
    • Biraj Dahal, Oak Ridge National Laboratory
    Preconditioning for hyper-reduction in reduced order models
    • Minji Kim, Lawrence Livermore National Laboratory
    A Discrete Curvature Approach to the Drill String Bending Problem
    • Arthur Mills, National Energy Technology Laboratory
    Intragranular bubble detection in crystalline solids through image processing and graph convolutional neural networks
    • Irving Martinez, Sandia National Laboratory
    Closing Remarks 5:45PM
  • August 26th, 2022

    Welcome Back
    Research Presentations
    These sessions will feature 15-minute research-focused traditional style presentations from mathematical sciences doctoral students. Research presentations will feature the following topics: 1) Data Science and Machine Learning, 2) Fluids, Climate, and Atmospheric Science, 3) Mathematical Biology, 4) Numerical Methods, 5) Physics, 6) Quantum Computing, and 7) Stochastics and Statistics.
    Stochastics and Statistics 10:00AM
    Statistical metrics for evaluating the compressibility of scientific datasets through lossy compressors
    • Arkaprabha Ganguli, Argonne National Laboratory
    Bayesian Nonlocal Operator Regression (BNOR): A Data-Driven Learning Framework of Nonlocal Models with Uncertainty Quantification
    • Yiming Fan, Sandia National Laboratory
    Statistical Emulators for Stochastic Computer Simulators
    • Hwanwoo Kim, Argonne National Laboratory
    Fluids, Climate and Atmospheric Science 10:50AM
    Predict Soil Moisture Content Using Physics-Informed Neural Networks
    • Jiajing Guan, United States Army Corps of Engineers, Cold Regions Research and Engineering Laboratory
    Quantum Computing 11:05AM
    A statistically-inspired method for enhancing error mitigation in quantum computing
    • Wern Yeen Yeong, Fermi National Accelerator Laboratory
    Fluids, Climate, and Atmospheric Science 11:20AM
    A Higher Order, Stable Partitioned Scheme for Fluid-Structure Interaction Problems
    • Kyle Schwiebert, Los Alamos National Laboratory
    Exploring numerical errors in simulations of the wave equation with large grid spacings
    • Madhumita Roy, Oak Ridge National Laboratory
    Changing Space-Time Covariance of Drought Index Under Changing Climates using Gaussian Processes
    • Tiffany Christian, Argonne National Laboratory
    Physics
    12:05PM
    Building Hierarchical Graphs to Describe Neutrino Interactions
    • Alaittin Kirtisoglu, Fermi National Accelerator Laboratory
    Reconstruction of 4d Diffraction Patterns in Scanning Transmission Electron Microscopy via Compressed Sensing and Neural Network
    • Zhaiming Shen, Oak Ridge National Laboratory
    Efficient Gaussian process-based surrogate model for chance-constrained optimal power flow on the large scaled power grid
    • Hanmo Li, Lawrence Livermore National Laboratory
    Break 12:50PM
    Panel Discussion: Preparing Students for Careers in Mathematical Sciences 1:00PM
    This session will feature distinguished panelists from national laboratories and highlight the role of mathematics and statistics graduate education in preparing students for careers in science and industry, and the distinctive contribution of internship programs such as MSGI.
    Moderator
    • Dr. Jeremy Tyson, Program Director, Division of Mathematical Sciences, National Science Foundation
    Panelists
    • Dr. Aditi Krishnapriyan, Applied Mathematician, Lawrence Berkeley National Laboratory
    • Dr. E. Louise Loudermilk, Research Ecologist, U.S. Department of Agriculture, Forest Service, Southern Research Station
    • Dr. Gabriel Perdue, Scientist, Fermi National Accelerator Laboratory (Fermilab)
    • Dr. Kevin R. Pilkiewicz, Research Chemist, U.S. Army Corps of Engineers, Engineer Research and Development Center
    • Dr. Stefan M. Wild, Deputy Division Director/Senior Computational Mathematician, Argonne National Laboratory
    Quantum Computing 2:00PM
    Simulating Quantum Circuits Using the Yang-Baxter Equation
    • Andrey Khesin, Fermi National Accelerator Laboratory
    Topological Quantum Error Detection/Correction
    • Tushar Pandey, Oak Ridge National Laboratory
    Data Science and Machine Learning 2:30PM
    To Correctly Classify Imbalanced Data, Find the Best Data
    • K Medlin, Argonne National Laboratory
    An Explainable Convolutional Neural Network Model for Predicting the Southern Annular Mode
    • Austin Eide, Los Alamos National Laboratory
    The Constituents of Hierarchical Temporal Memory
    • DJ Passey, Lawrence Berkeley National Laboratory
    Learning on top of the reference dynamical system
    • Tianhao Zhang, Lawrence Berkeley National Laboratory
    Closing Remarks 3:25PM