Machine Learning in Science & Engineering

Data Science Institute, Columbia University

MLSE will highlight the latest research in artificial intelligence and machine learning that are advancing science &amp; engineering fields.

This conference will demonstrate how data-driven approaches can help solve emerging challenges, and will showcase innovative thinking from a diverse range of technological disciplines. Throughout this two day conference, representatives from academia, government, and industry will gather together to explore the future of science and engineering across ten dedicated tracks. MLSE is partially supported by an NSF TRIPODS+X award.Learn More: <a href="http://mlse2020.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">mlse2020.com</a>MLSE Tracks &amp; Track Chairs<ul><li>Astronomy, Astrophysics, and Physics: Szabolcs Marka, David Kipping, Zsuzsanna Marka, Melissa Ness</li><li>Biology: Itsik Pe'er, Maria Chikina</li><li>Health Sciences: Olena Mamykina, Adler Perotte</li><li>Chemistry, Chemical Engineering, and Materials Science: Sanat K. Kumar, Simon Billinge, Yevgeny Rakita</li><li>Computing Systems: Martha Kim, Sarah Bird</li><li>Earth and Environmental Sciences: Pierre Gentine, Laure Zanna</li><li>Mechanical Engineering, Engineering Mechanics, and Civil Engineering: Steve WaiChing Sun, Krishna Garikipati</li><li>Methods and Algorithms: John Paisley, John Wright, Alexandr Andoni</li><li>Neuroscience: Paul Sajda</li><li>Quantum: Andrew J. Millis, Hanhee Paik</li><li>Transportation: Sharon (Xuan) Di</li></ul>Keynote Speakers include:<ul><li>William Dally, Chief Scientist &amp; Senior VP of Research, Nvidia; Professor-Research, Computer Science &amp; Electrical Engineering, Stanford University</li><li>Barbara Engelhardt, Associate Professor, Department of Computer Science, Princeton University</li><li>David W. Hogg, Group Leader, Flatiron Institute; and Professor of Physics &amp; Data Science, Department of Physics, New York University</li><li>Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, and Statistical Science, Duke University </li></ul>

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Machine Learning in Science & Engineering

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