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---
layout:
---
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<title>ACED-DIFFERENTIATE: SciML Webinar Course</title>
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<h1 id="scientific-machine-learning-mini-course">Scientific Machine Learning Webinar Series</h1>
<p>This webinar series and panel events are organized by <a href="https://dilipkrishnamurthy.github.io/">Dilip Krishnamurthy</a> 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>
<h2 id="tentative-speakers-and-topics">Upcoming/Ongoing Events</h2>
<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/> Session Chair: Karthik Kashinath, Berkeley Lab
</li>
<li>May 27: <a href="https://www.philenosis.com/">Joseph Bakarji</a>, University of Washington
</li>
<li>June 3: <a href="https://www.linkedin.com/in/pmmilani/">Pedro M. Milani</a>, Exponent
</li>
<li>June 10: <a href="https://zongyi-li.github.io/">Zongyi Li</a>, Caltech
</li>
<li>June 17: <a href="https://sites.google.com/view/ameyadjagtap">Ameya D. Jagtap</a>, Brown University
</li>
<li>June 24: <a href="https://www.linkedin.com/in/dmitrii-kochkov/">Dmitrii Kochkov</a>, Google
</li>
<li>Invited Session Chairs: Rose Yu (UCSD), Julia Ling (Citrine), Sanjay Choudhry (NVIDIA), Steve Brunton (UW), Jim Stone (IAS)
</ul>
<h4 id="ML-embed">VC Panel on Quantum Computing (Tentatively on 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>
Ask a Question: If you have a question that you would like to ask our panelists, we want to provide the opportunity to share with us beforehand (these questions will be given priority over live questions). Please add your question(s) before noon (Pacific) of May 11th in this <a href="https://github.us2.list-manage.com/track/click?u=d1019e4ff5e4e2b256b478eb1&id=e7c195fe3c&e=07bec0f196">spreadsheet</a> or send over email to dkrishn1@andrew.cmu.edu and/or stayur@cmu.edu.
</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>
<h2 id="tentative-speakers-and-topics">Past Webinars</h2>
<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 dkrishn1[at]andrew.cmu.edu and venkvis[at]cmu.edu</p>
<h2 id="Video Recordings">Video Recordings</h2>
Video recordings are available (login details available on request)<a href="pp/recordings.html"> here</a>
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