Machine Learning in
Science & Engineering
Virtual Conference: December 14 - 15, 2020
MLSE 2020 will feature the latest research in artificial intelligence and machine learning that are advancing science, engineering, and technology fields at large.
Through keynotes, conversations, demonstrations, and networking, this two day virtual conference will explore how data-driven approaches can help solve emerging challenges. MLSE 2020 will convene 11 tracks to highlight new research and innovation from a diverse range of disciplines. Learn More →
Register
Browse registration rates for professionals and students.
Sponsor
Consider supporting MLSE 2020 to unlock special offerings.
Experience
Learn how attendees will engage virtually throughout the conference.
Keynote Speakers
Tracks &
Track Chairs
Click into each track to learn more about the session programs. Registered attendees will have access to the Attendee Portal on MLSE2020.com to view session URLs, discussion boards, affiliated research, and more.
To email the track chairs, click on their names.
MLSE 2020 Sponsors & DSI Industry Affiliates
MLSE 2020 is brought to you by The Data Science Institute at Columbia University. Thank you to DSI Industry Affiliates.
The MLSE 2020 Neuroscience Track is sponsored by the IEEE Brain Initiative: brain.ieee.org
MLSE 2020 is supported by an NSF TRIPODS+X award from the National Science Foundation.
The MLSE 2020 Chemistry Track is sponsored by the Department of Chemical Engineering, Northeastern University: che.northeastern.edu
Calico Life Sciences joins as an MLSE 2020 sponsor: calicolabs.com
Receive brand recognition, introduction services, and recruiting capabilities to 1000+ attendees: Explore sponsorships.
MLSE 2020 Conference Chairs:
Jeannette M. Wing, Avanessians Director of the Data Science Institute and Professor of Computer Science, Columbia University
Qiang Du, Fu Foundation Professor of Applied Mathematics, Department of Applied Physics and Applied Mathematics, Columbia Engineering (SEAS)
Thank you to prior MLSE Conference Chairs:
Dana Randall, Co-Executive Director of the Institute for Data Engineering and Science, Georgia Tech
Newell Washburn, Associate Professor of Chemistry and Biomedical Engineering, Carnegie Mellon University
