- Daphne Koller - Insitro (USA) "Machine learning: A new approach to drug discovery"
Bio: Daphne Koller is the CEO and Founder of insitro, a startup company that aims to rethink drug development using machine learning. Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years. She was the co-founder, co-CEO and President of Coursera for 5 years, and the Chief Computing Officer of Calico, an Alphabet company in the healthcare space. She is the author of over 200 refereed publications appearing in venues such as Science, Cell, and Nature Genetics. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012. She received the MacArthur Foundation Fellowship in 2004 and the ACM Prize in Computing in 2008. She was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Academy of Arts and Sciences in 2014 and of the International Society of Computational Biology in 2017.
- Jennifer Listgarten - UC Berkeley (USA) "Machine learning for protein engineering"
Abstract: With the advent of more and more high-throughput technologies to measure protein properties of interest such as binding, expression, fluorescence, the time for machine learning to act synergistically with protein design is here. I will describe our work on accelerating the design/optimization of proteins (and small molecules) with machine learning approaches--- a sort of in silico approach to the method of Directed Evolution, which won the 2018 Nobel prize in Chemistry.
Bio: Since Jan. 2018, Jennifer Listgarten is a Professor in the Department of Electrical Engineering and Computer Science, and Center for Computational Biology, at the University of California, Berkeley. She is also a member of the steering committee for the Berkeley AI Research (BAIR) Lab, and a Chan Zuckerberg investigator. From 2007 to 2017 she was at Microsoft Research, through Cambridge, MA (2014-2017), Los Angeles (2008-2014), and Redmond, WA (2007-2008). She completed her Ph.D. in the machine learning group in the Department of Computer Science at the University of Toronto, located in her home town. She has two undergraduate degrees, one in Physics and one in Computer Science, from Queen's University in Kingston, Ontario. Jennifer's research interests are broadly at the intersection of machine learning, applied statistics, molecular biology and science.
- Quaid Morris - University of Toronto (Canada) "How to be a machine learning biologist"
- William Stafford Noble - University of Washington (USA) "Machine learning methods for making sense of big genomic and proteomic data"
Bio: William Stafford Noble is a Professor in the Department of Genome Sciences and in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. He received the Ph.D. in computer science and cognitive science from University of California, San Diego in 1998. Dr. Noble's research applies statistical and machine learning methods to the analysis of complex biological data sets. He is the author of more than 250 peer reviewed publications and has advised 29 postdoctoral fellows and 18 PhD students. William is the recipient of the International Society for Computational Biology Innovator award, an NSF CAREER award, is a Sloan Research Fellow, is on the Clarivate Analytics list of “Highly cited researchers,” and is a Fellow and former member of the Board of Directors of the ISCB.