Machine Learning in Computational Biology (MLCB) 2023: deadline Oct 4
David A Knowles
We are excited to be holding the 18th Machine Learning in Computational Biology (MLCB) meeting as a two-day hybrid conference on November 30 and December 1, 9am-5pm PST, with the in-person component at the University of Washington, Seattle, USA. The event will be streamed live through YouTube and Zoom. Please feel free to forward this call for papers/participation to anyone who may be interested.
From its inception in 2004 to 2017, MLCB was an official NeurIPS workshop. Given the growth and maturity of the field, MLCB became an independent conference in 2019. For 2020-2022, MLCB was held virtually. The virtual conference format led to a record number of participants, which included 1000 registered participants via Zoom and > 3000 views on the YouTube live stream. Our sponsors include Recursion, Deep Genomics, and Amazon.
We will consider papers describing research on novel learning approaches or application areas in computational biology in two formats: 1) 8-page papers that are eligible to (optionally) be published online in an MLCB section of the Proceedings of Machine Learning Research. 2) 2-page abstracts that can describe work in progress (not eligible for publication).
Paper submission deadline: October 4, 2023.
More details: https://mlcb.github.io/
(Free!) registration: https://forms.gle/rEHdj682deUXqKN86
Invited Speakers
Elham Azizi - Columbia University
Alex Rives - Meta AI and New York University
Bianca Dumitrascu - Columbia University
Organizers
Sara Mostafavi - University of Washington (USA)
David A Knowles - Columbia & New York Genome Center (USA)
Su-In Lee - University of Washington (USA)
Gerald Quon - UC Davis (USA)
Anshul Kundaje - Stanford (USA)
James Zou - Stanford (USA)
David A. Knowles (he/him/his), PhD.
Assistant Professor, Computer Science, Columbia University.
Interdisciplinary Appointee, Systems Biology, Columbia University.
Affiliate Member, Data Science Institute, Columbia University.
Core Faculty Member, New York Genome Center.