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<!DOCTYPE html>
<html>
<head>
<title>ACED-DIFFERENTIATE: SciML Webinar Course</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta.2/css/bootstrap.min.css"
integrity="sha384-PsH8R72JQ3SOdhVi3uxftmaW6Vc51MKb0q5P2rRUpPvrszuE4W1povHYgTpBfshb"
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Research
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News
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Publications
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</div>
<h1 id="scientific-machine-learning-mini-course">Scientific Machine Learning Webinar Series</h1>
<h2 id="website-change" style="color:#ff0000"> The SciML webinar has moved to the
University of Michigan's MICDE. Find the website, calendar, Zoom link and talks at
<a href="https://micde.umich.edu/news-events/sciml-webinar-series/">
https://micde.umich.edu/news-events/sciml-webinar-series/ </a>. If you were on our
mailing list, look out for an email to consent being added to the new SciML webinar
mailing list.
</h2>
<br>
<p>This webinar series and panel events are organized by <a href="https://www.andrew.cmu.edu/user/mphuthi/">Keith Phuthi</a>, Varun Shankar and <a href="https://www.andrew.cmu.edu/user/venkatv/">Venkat Viswanathan</a> with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.</p>
<p>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.</p>
<p>Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time </p>
<iframe src="https://calendar.google.com/calendar/embed?height=600&wkst=1&bgcolor=%23ffffff&ctz=America%2FNew_York&src=Y19pajU0dHVtbmM2OWV0YW9pbWFwODNzZmxzY0Bncm91cC5jYWxlbmRhci5nb29nbGUuY29t&color=%23D50000" style="border:solid 1px #777" width="800" height="600" frameborder="0" scrolling="no"></iframe>
<!-- <h2 id="How To Join">How To Join</h2>
<p><a href="https://calendar.google.com/calendar/u/0/r?cid=c_ij54tumnc69etaoimap83sflsc@group.calendar.google.com">Add Webinar Calendar</a><br />
<a href="https://cmu.zoom.us/j/99244798052?pwd=dTlCYkpHK3kzdStEd3FuWWU5amJ4dz09">Zoom Link</a><br />
Webinar ID: 992 4479 8052<br />
Passcode: 919401</p> -->
<h4 id="How To Join">Suggest speakers: </h4>
<p><a href="https://docs.google.com/forms/d/e/1FAIpQLSdv6yyHQ2XSfOOqNlG0QZRlDA11BItCz1wBTZmr16an9qx2LQ/viewform?usp=sf_link"> Form </a></p>
<br/>
<h2 id="tentative-speakers-and-topics"> Past Webinars </h2>
<h4 id="ML-embed">Spring 2023:</h4>
<ul>
<li> March 23: <a href="https://colabfit.org/"> Colabfit </a> Team with Stefano Martiniani (NYU), Eric Fuemmeler (UMN) and Amit Gupta (UMN)
<br/> Tentative Title: ML Interatomic Potentials development within the OpenKIM/ColabFit frameworks.
<br/> Session Chair:
</li>
<li> March 30: <a href="https://www.linkedin.com/in/xyxie"> Xiaoyu Xie</a> (Northwestern)
<br/> Tentative Title: Data-driven discovery of dimensionless numbers and governing laws from scarce measurements
<br/> Session Chair: Dr. Youngsoo Choi (Lawrence Livermore National Lab)
</li>
<li> April 6: <a href="https://www.linkedin.com/in/leon-gerard-41721a153/"> Leon Gerard </a> and <a href="https://scherbela.com/">Michael Scherbela </a> (University of Vienna)
<br/> Tentative Title: Neural Network Wavefunctions with Variational Monte Carlo
<br/> Session Chair: Weiluo Ren (Bytedance)
</li>
<li> April 13: <a href="https://engineering.purdue.edu/MSE/people/ptProfile?resource_id=23607"> Edwin Garcia </a> (Purdue)
<br/> Title: Machine Learning of Phase Diagrams
<br/> Session Chair: Ursula Kattner (NIST)
</li>
<li> April 20: <a href="https://lsj2408.github.io/"> Shengjie Luo </a>(Peking University)
<br/> Tentative Title: One Transformer Can Understand Both 2D & 3D Molecular Data
<br/> Session Chair: Payel Das (IBM)
</li>
<li> April 27: Misaki Ozawa (Université de Grenoble Alpes)
<br/> Tentative Title: Renormalization Group Approach for Machine Learning Probability Distributions
<br/> Session Chair: Bruno Loureiro (École Normale Supérieure)
</li>
<li> May 11: <a href="https://www.linkedin.com/in/saro-passaro/"> Saro Passaro </a> (Meta AI)
<br/> Tentative Title: Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
<br/> Session Chair: Josh Rackers (Genentech)
</li>
<li> May 18: <a href="https://www.crduan.com/"> Chenru Duan </a> (MIT)
<br/> Tentative Title: A transferable recommender approach for selecting the best density functional approximations in chemical discovery
<br/> Session Chair: Matthew Welborn (Entos)
</li>
<li> May 25: <a href=https://marylou-gabrie.github.io>Marylou Gabrié </a> (École Polytechnique)
<br/> Tentative Title: Adaptive Monte Carlo augmented with normalizing flows
<br/> Session Chair: Tony Lelièvre (CERMICS)
</li>
</ul>
<h4 id="ML-embed">Fall 2022:</h4>
<ul>
<li>September 22: <a href="https://www.linkedin.com/in/ilyes-batatia-725ab5197/"> Batatia Ilyes (ENS Paris Saclay)</a>
<br/> MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
<br/> Session Chair: Mario Geiger (Massachusetts Institute of Technology)
</li>
<li>September 29: <a href="https://github.com/Linux-cpp-lisp"> Alby Musaelian </a> and <a href="https://simonbatzner.github.io/"> Simon Batzner </a> (Harvard University)
<br/> Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
<br/> Session Chair: Bharath Ramsundar (Deep Forest Sciences)
</li>
<li>October 6: Yongji Wang (Princeton University)
<br/> Solving 3-D Euler via PINNs
<br/> Session Chair: Lu Lu (University of Pennsylvania)
</li>
<li>October 20: Jan Weinreich (University of Vienna)
<br/> Ab-initio machine learning of phase space averages
<br/> Session Chair: Stephan Heinen (University of Vienna)
</li>
<li>October 27: Darshil Doshi (University of Maryland College Park)
<br/> Critical Initialization of Deep Neural Networks using Jacobians
<br/> Session Chair: Tankut Can (Institute for Advanced Study)
</li>
<li>November 3: Gert-Jan Both (Université de Paris)
<br/> Fully Differentiable Model Discovery
<br/> Session Chair: Christian Mueller (Helmholtz Center Munich)
</li>
<li> December 1: <a href="https://yuuuxie.github.io/"> Yu Shelby Xie </a> (Harvard University)
<br/> FLARE: Many-body Bayesian force field and uncertainty-aware molecular dynamics from Bayesian active learning
<br/> Session Chair: Chris Paolucci (University of Virginia)
</li>
</ul>
<h4 id="ML-embed">Generative Models:</h4>
<ul>
<li>July 14: <a href="https://people.tamu.edu/~yzluo/">Youzhi Luo (Texas A&M)</a>
<br/> An Autoregressive Flow Model for 3D Molecular Geometry Generation
<br/> Session Chair: Jian Tang (Mila-Quebec AI Institute)
</li>
<li>July 28: <a href="https://people.tamu.edu/~yiliu/">Yi Liu</a> (Texas A&M)
<br/> Complete and Efficient Graph AI Techniques for Molecular Sciences
<br/> Session Chair: Wengong Jin (Broad Institute)
</li>
<li>August 4: Yuelin Wang and Kai Yi (Shanghai Jiao Tong University)
<br/> ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition
<br/> Session Chair: Jia Zhao (Utah State University)
</li>
<li>August 18: <a href="https://www.linkedin.com/in/niklas-gebauer-868101244/">Niklas Gebauer</a> (TU Berlin)
<br/> Inverse design of 3d molecular structures with conditional generative neural networks
<br/> Session Chair: Gregor Simm (Microsoft Research)
</li>
</ul>
<h4 id="ML-embed">Differentiable Physics:</h4>
<ul>
<li>February 24: Giuseppe Romano (MIT)
<br/> Differentiable Phonon Simulations to Optimize Thermal Transport in Nanostructures
<br/> Session Chair: Ján Drgoňa (PNNL)
</li>
<li>March 3: <a href="https://kidger.site/">Patrick Kidger</a> (University of Oxford)
<br/> On Neural Differential Equations
<br/> Session Chair: David Duvenaud (University of Toronto)
</li>
<li>March 10: <a href="https://jgreener64.github.io/">Joe Greener</a> (MRC Laboratory of Molecular Biology)
<br/> Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
<br/> Session Chair: Sam Schoenholz (Google Brain)
</li>
<li>March 17: <a href="https://dmse.mit.edu/people/rafael-gomez-bombarelli">Rafael Gomez-Bombarelli</a> (MIT)
<br/> Differentiable Uncertainty
<br/> Session Chair: Olexandr Isayev (CMU)
</li>
<li>March 24: <a href="https://www.linkedin.com/in/yaofeng-desmond-zhong-4443a843/">Desmond Zhong</a> (Siemens Technology)
<br/> Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
<br/> Session Chair: Rachel Kurchin (CMU)
</li>
<li>March 31: Taylor Howell & Simon Le Cleac'h (Stanford)
<br/> DOJO - differentiable rigid-body-dynamics
<br/> Session Chair: Vikas Sindhwani (Google Brain)
</li>
<li>April 21: <a href="https://www.akshayagrawal.com/">Akshay Agrawal</a>
<br/> Networks with Differentiable Contact Models
<br/> Session Chair: Zac Manchester (CMU)
</li>
</ul>
<h4 id="ML-embed">Symmetries, Physical Systems and Machine Learning:</h4>
<ul>
<li>October 14: Rui Wang (UCSD) and Robin Walters (Northeastern)
<br/> Incorporating Symmetry for Improved Generalization in Dynamics Prediction
<br/> Session Chair: Soledad Villar (Johns Hopkins University)
</li>
<li>October 28: <a href="https://www.jku.at/en/institute-for-machine-learning/about-us/team/ass-prof-dr-johannes-brandstetter/">Johannes Brandstetter</a> (Johannes Kepler University Linz)
<br/> Geometric and Physical Quantities Improve E(3) Equivariant Message Passing
<br/> Session Chair: Stephan Günnemann (Technical University of Munich)
</li>
<li>November 4: <a href="https://wangyu9.github.io/">Yu Wang</a> (MIT)
<br/> Geometric Operators for Shape Analysis
<br/> Session Chair: Rana Hanocka (University of Chicago)
</li>
<li>November 18: <a href="https://pimdehaan.com/">Pim de Haan</a> (Qualcomm)
<br/> Natural Message Passing
</li>
<li>December 2: <a href="https://research.google/people/CarlosEsteves/">Carlos Esteves</a> (Google)
<br/> Towards Efficient Spherical NNs
<br/> Session Chair: Manzil Zaheer (Google Research)
</li>
<li>January 6: <a href="https://kindxiaoming.github.io/">Ziming Liu</a> (MIT)
<br/> Machine Learning Symmetries for Conservation Laws
<br/> Session Chair: Sam Vinko (University of Oxford)
</li>
<li>January 13: <a href="https://zrqiao.github.io/">Zhuoran Qiao</a> (Caltech)
<br/> Geometric Learning for Quantum-Chemistry-Informed Representations
<br/> Session Chair: Peetak Mitra (PARC)
</li>
<li>January 20: <a href="https://www.mit.edu/~alet//">Ferran Alet</a> (MIT)
<br/> Learning to Encode and Discover Physics-Based Inductive Biases
<br/> Session Chair: Bharath Ramsundar (Deep Forest Sciences)
</li>
</ul>
<h4 id="ML-embed">Machine Learning Potentials and Force Fields for Materials Chemistry:</h4>
<ul>
<li>August 19: <a href="https://go.umd.edu/tiwarylab">Pratyush Tiwary</a>, University of Maryland
<br/> From Atoms to Mechanisms with State Predictive Information Bottleneck and Denoising Diffusion Proabilistic Models
<br/> Session Chair: Matthias Rupp, University of Konstanz
</li>
<li>August 26: <a href="https://samschoenholz.wordpress.com/">Sam Schoenholz</a>
, Google Brain
<br/> JAX-MD: A Framework for Differentiable Molecular Dynamics
<br/> Session Chair: Michael Brenner, Harvard University
</li>
<li>September 2: <a href="https://github.com/deepmodeling/deepmd-kit">Linfeng Zhang</a>, Princeton University
<br/> Learning-Assisted Molecular Modeling: From Methodology Development ot Engineering Effort
<br/> Session Chair: Tess Smidt, Massachusetts Institute of Technology
</li>
<li>September 9: <a href="https://linkedin.com/in/simonbatzner">Simon Batzner</a>, Harvard University
<br/> Neural Equivariant Interatomic Potentials
<br/> Session Chair: Matti Hellström, Software for Chemistry and Materials
</li>
<li>September 16: <a href="https://www.linkedin.com/in/alice-allen-037484112">Alice Allen</a> and
<a href="https://www.linkedin.com/in/d%C3%A1vid-p%C3%A9ter-kov%C3%A1cs-9b8465104/">Dávid Péter Kóvacs</a>,
University of Cambridge
<br/> Linear Body-Ordered Molecular Force Fields
<br/> Session Chair: Albert Bartók-Pártay, University of Warwick
</li>
<li>September 23: <a href="https://scholar.google.com/citations?view_op=list_works&hl=it&hl=it&user=ulwBTzgAAAAJ">Andrea Grisafi</a>,
École Polytechnique Fédérale de Lausanne
<br/> Symmetry-Adapted and Long-Range Representations in Atomic-scale ML
<br/> Session Chair: Kieron Burke, University of California Irvine
</li>
<li>September 30: Muhammad Firmansyah Kasim and Sam Vinko,
University of Oxford
<br/> Differentiable Quantum Chemistry
<br/> Session Chair: Ekin Dogus Cubuk, Google Brain
</li>
</ul>
<h4 id="ML-embed">Machine Learning meets Information Theory and Statistical Mechanics:</h4>
<ul>
<li>July 8: <a href="https://www.alexalemi.com/">Alex Alemi</a>, Google Research
<br/> Machine Learning and Thermodynamics
<br/> Session Chair: Max Welling, University of Amsterdam
</li>
<li>July 15: <a href="https://pratikac.github.io/">Pratik Chaudhri</a>, University of Pennsylvania
<br/> (Towards the) Foundations of Small Data
<br/> Session Chair: Karthik Duraisamy, University of Michigan
</li>
<li>July 22: <a href="https://sites.google.com/site/shoyaida/home">Sho Yaida</a>, Facebook
<br/> Model Complexity from Macroscopic Perspective
<br/> Session Chair: Jascha Sohl Dickstein, Google Brain
</li>
<li>July 29: <a href="https://yasamanb.github.io/">Yasaman Bahri</a>, Google Brain
<br/> Dynamics and Phase Transitions in Wide, Deep Neural Networks
<br/> Session Chair: Surya Ganguli, Stanford
</li>
<li>Aug 5: <a href="https://elenaagliari.weebly.com/">Elena Agliari</a>, Sapienza University of Rome
<br/> Learning From Storing
<br/> Session Chair: Jascha Shol-Dickstein, Google Brain
</li>
</ul>
<h4 id="ML-embed">Machine Learning in Fluid Dynamics:</h4>
<ul>
<li>May 20: <a href="https://filipeabperes.github.io/">Filipe de Avila Belbute-Peres</a>, Carnegie Mellon University
<br/> Combining PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
<br/> Session Chair: Karthik Kashinath, Berkeley Lab
</li>
<li>May 27: <a href="https://www.philenosis.com/">Joseph Bakarji</a>, University of Washington
<br/> Data-driven Discovery of Differential Equations for Complex Models in Fluids
<br/> Session Chair: Julia Ling, Alphabet
</li>
<li>June 3: <a href="https://www.linkedin.com/in/pmmilani/">Pedro M. Milani</a>, Exponent
<br/> ML Approaches to Learning Turbulent Mixing in Film Cooling Flows
<br/> Session Chair: Gavin Portwood, LLNL
</li>
<li>June 10: <a href="https://zongyi-li.github.io/">Zongyi Li</a>, Caltech
<br/> Neural Operator: Learning Maps Between Function Spaces
<br/> Session Chair: Sanjay Choudhry, NVIDIA
</li>
<li>June 17: <a href="https://sites.google.com/view/ameyadjagtap">Ameya D. Jagtap</a>, Brown University
<br/> A Generalized Space-Time Domain based Extended PINN for PDEs: Method and Implementation
<br/> Session Chair: Rose Yu, UCSD
</li>
<li>June 24: <a href="https://www.linkedin.com/in/dmitrii-kochkov/">Dmitrii Kochkov</a>, Google
<br/> Machine Learning Accelerated Computational Fluid Dynamics
<br/> Session Chair: Themistoklis Sapsis, MIT
</li>
<li>July 1: <a href="https://neuralconcept.com/">Pierre Baque</a>, Neural Concept
<br/> Session Chair: Andrea Panizza, Baker Hughes
</li>
</ul>
<h4 id="ML-embed">VC Panel on Quantum Computing (May 11th, Tuesday at 2 pm Pacific Time):</h4>
<ul>
<!-- <li><a href="https://www.linkedin.com/in/anders-g-fr%C3%B8seth-a333438b/">Anders Frøseth</a>, Propagator ventures
</li> -->
<li>Moderator: <a href="https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/tayur-sridhar.html">Sridhar Tayur</a>, Carnegie Mellon University
</li>
<!-- <li><a href="https://www.linkedin.com/in/jordanjacobs1/?originalSubdomain=ca">Jordan Jacobs</a>, Radical Ventures
</li> -->
<li><a href="https://www.linkedin.com/in/carly-e-anderson/">Carly Anderson</a>, Prime Movers Lab
</li>
<li><a href="https://www.dcvc.com/bio/core/dr-chris-boshuizen.html">Chris Boshuizen</a>, DCVC
</li>
<li><a href="https://www.linkedin.com/in/russ-wilcox-2005">Russ Wilcox</a>, Pillar VC
</li>
</ul>
<h4 id="ML-embed">Quantum Machine Learning:</h4>
The organizers would like to thank Jarrod McLean (Google) and Zlatko K. Minev (IBM) for suggestions of speakers and session chairs.
<ul>
<li>April 15: <a href="https://momohuang.github.io/">Hsin-Yuan (Robert) Huang</a> , Caltech
<br/> Characterizing Quantum Advantage in Machine Learning
<br/> Session Chair: Kristan Temme, Institute for Quantum Information and Matter
</li>
<li>April 22: <a href="https://www.cchem.berkeley.edu/kbwgrp/index.php/People/IanConvy">Ian Convy
</a>, UC Berkeley
<br /> Session Chair: Miles Stoudenmire, Flatiron Institute
</li>
<li>April 29: <a href="https://www.linkedin.com/in/andrea-skolik-b64a3215a/?originalSubdomain=de">Andrea Skolik</a>, Volkswagen Data Lab and Leiden University
<br /> Session Chair: Maria Schuld, Xanadu and University of KwaZulu-Natal
</li>
<li>May 6: <a href="https://albacl.github.io/">Alba Cervera Lierta</a>, University of Toronto
<br /> Session Chair: Glen Evenbly, Georgia Institute of Technology
</li>
<li>May 13: <a href="https://deepai.org/profile/michael-broughton">Michael Broughton</a>, Google
<br /> Session Chair: Max Radin, Zapata Computing
</li>
</ul>
<h4 id="ML-embed">Deployment of ML in the Industry:</h4>
<ul>
<li>Mar 11: <a href="https://www.linkedin.com/in/melanie-senn-8a7628110/">Melanie Senn</a> and <a href="https://www.linkedin.com/in/gianina-alina-negoita/">Alina Negoita</a>, Innovation Center California of Volkswagen Group of America
<br />High-Throughput Screening Framework for Battery Materials Design
<br /> Session Chair: Shailendra Kaushik, General Motors
</li>
<li>Mar 18: <a href="https://www.linkedin.com/in/austin-sendek">Austin Sendek</a>, Aionics, Inc.
<br /> Aionics: Harnessing ML to supercharge battery discovery, design, and deployment in industry
<br /> Session Chair: Venkat Viswanathan, Carnegie Mellon University
<li>Mar 25: <a href="https://researcher.watson.ibm.com/researcher/view.php?person=us-daspa">Payel Das</a>, IBM Thomas J
Watson Research Center
<br /> Trustworthiness in AI for Accelerating Discovery
<br /> Session Chair: Isidoros Doxas, Northrop Grumman Mission Systems
</li>
</li>
<li>Apr 1: <a href="https://www.linkedin.com/in/anirudhm/">Aniruddha Mukhopadhyay</a>, ANSYS, Inc.
<br/>Shifting Landscape in ML-Driven Product Engineering
<br/>Session Chair: Vivek Singh, NVIDIA
</li>
<li>Apr 8: <a href="https://www.linkedin.com/in/keith-task-8030aa1b">Keith Task</a>, BASF
<br /> Data Science for Chemicals and Materials Development at BASF
<br /> Session Chair: Amra Peles, Pacific Northwest National Laboratory
</li>
</ul>
<h4 id="ML-embed">Molecular ML for Drug Discovery:</h4>
<ul>
<li>Feb 11: <a href="https://people.csail.mit.edu/wengong/">Wengong Jin</a>, Massachusetts Institute of Technology
<br />Graph Neural Networks and Generative Models for Drug Discovery
<br /> Session Chair: Alex Wiltschko, Google Research
</li>
<li>Feb 18: <a href="https://people.csail.mit.edu/wengong/">Bharath Ramsundar</a> and <a href="https://seyonechithrananda.com/about">Seyone Chithrananda</a>
<br />ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
<br /> Session Chair: Tom Miller, Caltech and Entos, Inc.
<li>Feb 25: <a href="https://www.linkedin.com/in/dominik-lemm-22866b161/en-us">Dominik Lemm</a>
<br />Energy-Free Machine Learning Predictions of Ab Initio Structures
<!-- <br /> Session Chair: Tom Miller, Caltech and Entos, Inc. -->
<li>March 4: <a href="https://mariokrenn.wordpress.com/">Mario Krenn</a>
<br /> Robust Molecular String Representation for Molecular Machine Learning
<br /> Session Chair: Olexandr Isayev, Carnegie Mellon University
</li>
<!-- <li>Feb 4: <a href="https://www.linkedin.com/in/evanfeinberg">Evan Feinberg</a>, Genesis Therapeutics, Inc.<br />Gauge Equivariant Normalizing Flows for Lattice Field Theory
<li>TBD: <a href="https://juliacomputing.com/about-us">Dhairya Gandhi</a>, Julia Computing<br /><a href="https://github.com/SciML/DiffEqFlux.jl">Scientific Machine Learning Methods and Tools (DiffEqFlux.jl)</a></li>
<li>TBD: Varun Shankar, Carnegie Mellon University<br />Physics-Constrained Machine Learning for Fluid Flow Fields</li>
<li>TBD: <a href="https://rkurchin.github.io/">Rachel Kurchin</a>, Carnegie Mellon University & Massachusetts Institute of Technology PhD<br />Physics-Guided Convolutional Neural Networks</li>
-->
</ul>
<h4 id="Panel">Panel Discussion on Open Challenges in ML:</h4>
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).
<h5 id="VC">VC Panel (March 23rd):</h5>
<ul>
<li><a href="https://www.breakthroughenergy.org/">Joel Moxley</a>, Breakthrough Energy Ventures
</li>
<li><a href="https://www.linkedin.com/in/hardimanjames/">James Hardiman</a>, DCVC (Data Collective)
</li>
<li><a href="https://www.linkedin.com/in/mark-cupta/">Mark Cupta</a>, Prelude Ventures
</li>
<li><a href="https://www.linkedin.com/in/bzuberi/">Bilal Zuberi</a>, Lux Capital
</li>
</ul>
<h4 id="physics-reg ML">Physics-Regularized ML:</h4>
<ul>
<li>Oct 1: <a href="https://dilipkrishnamurthy.github.io/">Dilip Krishnamurthy</a>, Carnegie Mellon University PhD<br /><a href="https://www.sciencedirect.com/science/article/pii/S0021999118307125?casa_token=Wt1UjlNtYqsAAAAA:0nr37aEEjRdnvuzKV7_WBiRg_XTLXjx1ekICV4XmTgrM3QGQ5B5KdLfqXjUA_4qoupxwtjCFqws">Physics-Informed Neural Networks</a>
<br /> Session Chair: Bharath Ramsundar, DeepChem
</li>
<li>Oct 8: <a href="https://www.pitt.edu/~xiaowei/">Xiaowei Jia</a>, Asst. Prof. @ University of Pittsburgh & University of Minnesota PhD<br /><a href="https://arxiv.org/abs/2001.11086">Physics-Guided Machine Learning for Scientific Discovery</a>
<br /> Session Chair: Jason Koeller, Citrine Informatics
</li>
</ul>
<h4 id="ML symmetries">ML Obeying Physical Symmetries:</h4>
<ul>
<li>Oct 15 (note schedule change): <a href="https://sites.google.com/site/tonicbq/">Bingqing Cheng</a>, Trinity College Cambridge & EPFL PhD<br /><a href="https://www.nature.com/articles/s41586-020-2677-y">Machine-Learning Potentials: What Works and What Doesn’t</a>
<br /> Session Chair: Prof. <a href="https://scholar.google.com/citations?user=QGiLc_cAAAAJ&hl=en">Frank Noe</a>, Freie Universität Berlin
</li>
<li>Oct 22: <a href="https://rbharath.github.io/about/">Bharath Ramsundar</a>, Creator of <a href="https://deepchem.io/">DeepChem</a> & Stanford CS PhD<br />Physical Theories and Differentiable Programs
<br /> Session Chair: Prof. <a href="https://www.andrew.cmu.edu/user/venkatv/index.html">Venkat Viswanathan</a>, Carnegie Mellon University
</li>
<li>Oct 29 (note schedule change): <a href="https://jan.hermann.name/">Jan Hermann
</a>, Freie Universität Berlin & Humboldt University of Berlin Physics PhD<br />Deep neural network solution of the electronic Schrödinger equation
<br /> Session Chair: Prof. <a href="https://people.epfl.ch/giuseppe.carleo">Giuseppe Carleo</a>, École polytechnique fédérale de Lausanne (EPFL)
</li>
<li>Nov 5 (note schedule change): <a href="https://blondegeek.github.io/">Tess Smidt</a> and <a href="https://mariogeiger.ch/">Mario Geiger</a>, Lawrence Berkeley Laboratory & École polytechnique fédérale de Lausanne, respectively
<br /><a href="https://e3nn.org/">Neural Networks With Euclidean Symmetry for Physical Sciences and e3nn: A Modular PyTorch Framework for Euclidean Neural Networks</a>
<br /> Session Chair: Prof. <a href="https://people.cs.uchicago.edu/~risi/">Risi Kondor</a>, University of Chicago
</li>
</ul>
<h4 id="ML-embed">ML-Embedded Physical Models:</h4>
<ul>
<li>Nov 12: <a href="https://research.google/people/LiLi/">Li Li</a>, Google Accelerated Science & UC Irvine PhD<br />
<a href="https://arxiv.org/pdf/2009.08551.pdf">Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics</a>
<br /> Session Chair: Prof. Vikram Gavini, University of Michigan
</li>
<li>Nov 19: <a href="https://chrisrackauckas.com/">Christopher Rackauckas</a> and <a href="https://www.linkedin.com/in/alecbills/">Alec Bills</a>, Massachusetts Institute of Technology & Pumas-AI, and Carnegie Mellon University, respectively<br /><a href="https://arxiv.org/abs/2008.01527">Universal Ordinary Differential Equations and Its Application to an Engineering Challenge</a>
<br /> Session Chair: Prof. <a href="https://eapsweb.mit.edu/people/ravela">Srinivas (Sai) Ravela</a>, Massachusetts Institute of Technology
</li>
<li>Dec 3: <a href="https://www.linkedin.com/in/alok-warey-88ab1b4/">Alok Warey</a>, General Motors & University of Texas at Austin PhD<br />Deep Learning for Vehicle Systems
<br /> Session Chair: <a href="https://www.linkedin.com/in/anirudhm/">Aniruddha Mukhopadhyay</a>, ANSYS, Inc.
</li>
<li>Dec 10: <a href="https://www.jessebett.com/">Jesse Bettencourt
</a>, University of Toronto PhD<br />Neural Ordinary Differential Equations
<br /> Session Chair: Prof. <a href="https://zicokolter.com/">Zico Kolter</a>, Carnegie Mellon University
</li>
<li>Jan 14: <a href="https://astroautomata.com/">Miles Cranmer</a>, Princeton Astrophysics PhD<br />
Time Symmetries and Neurosymbolic Learning for Dynamical Systems<br />
Session Chair: Prof. Phiala Shanahan, Massachusetts Institute of Technology
</li>
<li>Jan 21: <a href="https://jerrybai1995.github.io/">Shaojie Bai</a>, Carnegie Mellon University PhD<br />
<a href="https://papers.nips.cc/paper/2019/hash/01386bd6d8e091c2ab4c7c7de644d37b-Abstract.html">Deep Equilibrium Models</a><br/>
Session Chair: Stephan Hoyer, Google Research
</li>
<li>Jan 28: <a href="https://www.linkedin.com/in/gurtej-kanwar-9a4b5b56/">Gurtej Kanwar</a>, Massachusetts Institute of Technology PhD<br />Gauge Equivariant Normalizing Flows for Lattice Field Theory
<br /> Session Chair: Lena Funcke, Perimeter Institute for Theoretical Physics
</li>
<li>Feb 4: <a href="https://www.linkedin.com/in/evanfeinberg">Evan Feinberg</a>, Genesis Therapeutics, Inc.<br />Machine Learning and Molecular Simulation Based Methods for Therapeutics
<br /> Session Chair: Amir Barati Farimani, Carnegie Mellon University
</li>
</ul>
<h2 id="Resources">Resources</h2>
<p>The seminar series is supported by the <a href="https://arpa-e.energy.gov/technologies/programs/differentiate">ARPA-E DIFFERENTIATE</a> program and the Carnegie Mellon Presidential Fellowship.</p>
<h2 id="conception">Conception</h2>
<p>A panel discussion on the topic <a href="https://pubs.acs.org/doi/full/10.1021/acsenergylett.8b02278">Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization</a> 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.</p>
<h2 id="questions">Questions?</h2>
<p>Email the organizers at mkphuthi[at]cmu.edu and venkvis[at]cmu.edu</p>
<h2 id="Video Recordings">Video Recordings</h2>
A Google Drive link to the Video recordings are available on request on the slack or by emailing the organizers.
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