Welcome to the 2022 NSF MSGI Summer Research Symposium

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

  View daily schedule

  Welcome and Schedule of Events (vimeo.com video)	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 (vimeo.com video)	10:30AM
  Acceleration of Kernel Methods with Nystrom Approximation Zezheng Song, Lawrence Berkeley National Laboratory The Askey-Rahman-Suslov Nonsymmetric Poisson Kernel for Askey-Wilson Polynomials and its Special Values Raymond Centner, National Institute of Standards and Technology
  Mathematical Biology (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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
  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 Mathematical approaches for effective meso-micro coupling Jithin George, National Renewable Energy Laboratory Discovering reduced-order equations of motion for firebrand transport Alexander Mendez, United States Department of Agriculture Forest Service
  Physics (vimeo.com video)	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

  View daily schedule

  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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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 (vimeo.com video)	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