Scientific Machine Learning Webinar Series
This webinar series and panel events are organized by Dilip Krishnamurthy and Venkat Viswanathan with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.
Webinar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions. Invited session chairs will guide the discussion along with offering their perspective on the field. The Q&A session is typically very interactive with a small group of enthusiastic audience.
Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time
How To Join
Add Webinar Calendar
Zoom Link
Webinar ID: 992 4479 8052
Passcode: 919401
Upcoming/Ongoing Events
Machine Learning in Fluid Dynamics:
- May 20: Filipe de Avila Belbute-Peres, Carnegie Mellon University
Session Chair: Karthik Kashinath, Berkeley Lab - May 27: Joseph Bakarji, University of Washington
Session Chair: Julia Ling, Google X - June 3: Pedro M. Milani, Exponent
Session Chair: Gavin Portwood, Lawrence Livermore NL - June 10: Zongyi Li, Caltech
Session Chair: Sanjay Choudhry, NVIDIA - June 17: Ameya D. Jagtap, Brown University
Session Chair: Rose Yu, UC San Diego - June 24: Dmitrii Kochkov, Google
Past Webinars
VC Panel on Quantum Computing (May 11th):
- Moderator: Sridhar Tayur, Carnegie Mellon University
- Carly Anderson, Prime Movers Lab
- Chris Boshuizen, DCVC
- Russ Wilcox, Pillar VC
Quantum Machine Learning:
- April 15: Hsin-Yuan (Robert) Huang , Caltech
Characterizing Quantum Advantage in Machine Learning
Session Chair: Kristan Temme, Institute for Quantum Information and Matter - April 22: Ian Convy
, UC Berkeley
Session Chair: Miles Stoudenmire, Flatiron Institute - April 29: Andrea Skolik, Volkswagen Data Lab and Leiden University
Session Chair: Maria Schuld, Xanadu and University of KwaZulu-Natal - May 6: Alba Cervera Lierta, University of Toronto
Session Chair: Glen Evenbly, Georgia Institute of Technology - May 13: Michael Broughton, Google
Session Chair: Max Radin, Zapata Computing
Deployment of ML in the Industry:
- Mar 11: Melanie Senn and Alina Negoita, Innovation Center California of Volkswagen Group of America
High-Throughput Screening Framework for Battery Materials Design
Session Chair: Shailendra Kaushik, General Motors - Mar 18: Austin Sendek, Aionics, Inc.
Aionics: Harnessing ML to supercharge battery discovery, design, and deployment in industry
Session Chair: Venkat Viswanathan, Carnegie Mellon University - Mar 25: Payel Das, IBM Thomas J
Watson Research Center
Trustworthiness in AI for Accelerating Discovery
Session Chair: Isidoros Doxas, Northrop Grumman Mission Systems - Apr 1: Aniruddha Mukhopadhyay, ANSYS, Inc.
Shifting Landscape in ML-Driven Product Engineering
Session Chair: Vivek Singh, NVIDIA - Apr 8: Keith Task, BASF
Data Science for Chemicals and Materials Development at BASF
Session Chair: Amra Peles, Pacific Northwest National Laboratory
Molecular ML for Drug Discovery:
- Feb 11: Wengong Jin, Massachusetts Institute of Technology
Graph Neural Networks and Generative Models for Drug Discovery
Session Chair: Alex Wiltschko, Google Research - Feb 18: Bharath Ramsundar and Seyone Chithrananda
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
Session Chair: Tom Miller, Caltech and Entos, Inc. - Feb 25: Dominik Lemm
Energy-Free Machine Learning Predictions of Ab Initio Structures - March 4: Mario Krenn
Robust Molecular String Representation for Molecular Machine Learning
Session Chair: Olexandr Isayev, Carnegie Mellon University
Panel Discussion on Open Challenges in ML:
This session is focused on discussing challenges and technological bottlenecks at the intersection of machine learning and science/engineering. Industry leaders at original equipment manufacturers (OEMs) and venture capitalists (VCs) will provide their perspective and directions for research and development. We anticipate that this session will facilitate effective TT & O (Tech. Transfer and Outreach).VC Panel (March 23rd):
- Joel Moxley, Breakthrough Energy Ventures
- James Hardiman, DCVC (Data Collective)
- Mark Cupta, Prelude Ventures
- Bilal Zuberi, Lux Capital
Physics-Regularized ML:
- Oct 1: Dilip Krishnamurthy, Carnegie Mellon University PhD
Physics-Informed Neural Networks
Session Chair: Bharath Ramsundar, DeepChem - Oct 8: Xiaowei Jia, Asst. Prof. @ University of Pittsburgh & University of Minnesota PhD
Physics-Guided Machine Learning for Scientific Discovery
Session Chair: Jason Koeller, Citrine Informatics
ML Obeying Physical Symmetries:
- Oct 15 (note schedule change): Bingqing Cheng, Trinity College Cambridge & EPFL PhD
Machine-Learning Potentials: What Works and What Doesn’t
Session Chair: Prof. Frank Noe, Freie Universität Berlin - Oct 22: Bharath Ramsundar, Creator of DeepChem & Stanford CS PhD
Physical Theories and Differentiable Programs
Session Chair: Prof. Venkat Viswanathan, Carnegie Mellon University - Oct 29 (note schedule change): Jan Hermann
, Freie Universität Berlin & Humboldt University of Berlin Physics PhD
Deep neural network solution of the electronic Schrödinger equation
Session Chair: Prof. Giuseppe Carleo, École polytechnique fédérale de Lausanne (EPFL) - Nov 5 (note schedule change): Tess Smidt and Mario Geiger, Lawrence Berkeley Laboratory & École polytechnique fédérale de Lausanne, respectively
Neural Networks With Euclidean Symmetry for Physical Sciences and e3nn: A Modular PyTorch Framework for Euclidean Neural Networks
Session Chair: Prof. Risi Kondor, University of Chicago
ML-Embedded Physical Models:
- Nov 12: Li Li, Google Accelerated Science & UC Irvine PhD
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Session Chair: Prof. Vikram Gavini, University of Michigan - Nov 19: Christopher Rackauckas and Alec Bills, Massachusetts Institute of Technology & Pumas-AI, and Carnegie Mellon University, respectively
Universal Ordinary Differential Equations and Its Application to an Engineering Challenge
Session Chair: Prof. Srinivas (Sai) Ravela, Massachusetts Institute of Technology - Dec 3: Alok Warey, General Motors & University of Texas at Austin PhD
Deep Learning for Vehicle Systems
Session Chair: Aniruddha Mukhopadhyay, ANSYS, Inc. - Dec 10: Jesse Bettencourt
, University of Toronto PhD
Neural Ordinary Differential Equations
Session Chair: Prof. Zico Kolter, Carnegie Mellon University - Jan 14: Miles Cranmer, Princeton Astrophysics PhD
Time Symmetries and Neurosymbolic Learning for Dynamical Systems
Session Chair: Prof. Phiala Shanahan, Massachusetts Institute of Technology - Jan 21: Shaojie Bai, Carnegie Mellon University PhD
Deep Equilibrium Models
Session Chair: Stephan Hoyer, Google Research - Jan 28: Gurtej Kanwar, Massachusetts Institute of Technology PhD
Gauge Equivariant Normalizing Flows for Lattice Field Theory
Session Chair: Lena Funcke, Perimeter Institute for Theoretical Physics - Feb 4: Evan Feinberg, Genesis Therapeutics, Inc.
Machine Learning and Molecular Simulation Based Methods for Therapeutics
Session Chair: Amir Barati Farimani, Carnegie Mellon University
Resources
The seminar series is supported by the ARPA-E DIFFERENTIATE program and the Carnegie Mellon Presidential Fellowship.
Conception
A panel discussion on the topic Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization crystallized important considerations while applying machine learning methods to limited-data engineering applications. Often it's useful to synergistally stack the ML models to the extent possible with the known physics of the problem for effective learning even in low-data regimes.
Questions?
Email the organizers at dkrishn1[at]andrew.cmu.edu and venkvis[at]cmu.edu