Frontiers in Machine Learning for the Physical Sciences

October 26, 2020 - Virtual Symposium



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[ This event occurred in the past ]

Please note all times below are PDT (U.S. Pacific Daylight Time)

8:30 AM

Pramod Khargonekar
Vice Chancellor for Research, UCI


8:45 AM

Keynote Address
Stanley Osher
Innovations in Mean-Field Game Theory for Scalable Computation and Diverse Applications

9:30 AM

Kipton Barros
Automated discovery of a robust interatomic potential for aluminum

10:00 AM

Kieron Burke
Machine learning for electronic structure calculations and a new approach to warm dense matter simulations

10:30 AM

Lighting Talks (Video Presentation)

 [ 15 Minute Break ]

11:15 AM

Poster Session

Poster Session by Topic and Breakout Room

Breakout Room 1

Extreme Physics, Fundamental Physics/Astronomy and Astrophysics

  • Irina Espejo - Excursion - Active Learning and Gaussian Processes for black box inference
  • Jessica Howard - Foundations of a Fast, Data-Driven, Machine-Learned Simulator
  • Evan Jones - Tests of Catastrophic Outlier Prediction in Empirical Photometric Redshift Estimation with a Support Vector Machine
  • Bernadette Bosco - Machine Learning in Astronomy: Galaxies ML
  • Anne-Katherine Burns - A Machine Learning Solution to Computationally Intensive Problems in Big Bang Nucleosynthesis (BBN)

Breakout Room 2

Methods, Hardware/Software/Algorithms/ Mathematics

  • Eric Montoya - Spin torque oscillators for spintronic neuromorphic computing
  • Tess Smidt -  e3nn: A modular PyTorch framework for 3D Euclidean neural networks
  • Truong Nguyen - An Expectation-Maximization accelerator for unsupervised learning of adaptive Gaussian Mixture models
  • Jiayi Li - Tropical Geometry for Understanding Expressivity of Neural Networks
  • Yonatan Dukler - Optimization Theory for ReLU Neural Networks Trained with Normalization Layers

Breakout Room 3


  • Cameron Movassaghi - Machine Learning Applications for Multiplexed Neurotransmitter Detection
  • Zixiao Zong - Topological Models of Amyloid Fibril Formation, and Identification of Fibril Topologies from Fibrillization Kinetics
  • Elizabeth - Diessner: Mapping the Mutational Landscape of the SARS-CoV-2 Main Protease: Molecular Modeling and Comparative Analysis
  • Eric Medwedeff - Towards Scalable Simulation of Dynamical Graph Grammar Biological Models, a Natural Arena for ML Model Reduction
  • Cory Scott - Efficient Learning of Cytoskeletal Dynamics with Multiscale Machine Learning and Optimized Projection Operators

Breakout Room 4

Many-body Physics

  • Mathieu Bauchy - End-to-End Differentiability and TPU Computing to Accelerate Materials’ Inverse Design
  • Yu Song - Deciphering the viscosity of nuclear waste immobilization glasses by deep learning
  • Azmain Abrawr Hossain - Extracting Exciton Binding Energy using Regression Techniques
  • David Rosenberger -  Evaluating diffusion and the thermodynamic factor for binary ionic mixtures

Breakout Room 5

Weather and Climate; Science Image Analysis and Bio-image Analysis

  • Matthew Laffin - Physics-Constrained Neural Networks for Large-Scale Inference of Subglacial Topography under Greenland and Antarctica
  • Griffin Mooers - Generative Modeling of Atmospheric Convection
  • Nadia Ahmed - Remote Sensing for Severe Weather Detection
  • Silvia Miramontes - Accelerating Cell Counting with Quantitative Microscopy Based on U-Net

 [ 15 Minute Break ]


12:30 PM

Brian Spears
Cognitive Simulation: Combining simulation and experiment with artificial intelligence

1:00 PM

Stephan Mandt
Machine Learning and Physics: Bridging the Gap

 [ 15 Minute Break ]

1:45 PM

Gowri Srinivasan
Combining Graph Theory and Machine Learning to Characterize Fractured Systems

2:15 PM

Eric Mjolsness
AI approaches to graph dynamics for multiscale computational science

2:45 PM

John Sarrao
Deputy Director, Science, Technology, and Engineering, LANL
Closing Remarks