Invited Speakers

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.


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.


Bio: Quaid Morris is a full member of the Computation and Systems Biology program at the Sloan Kettering Institute at the Memorial Sloan Kettering Cancer Center. Until last month, he was a full professor at the University of Toronto in the Donnelly Centre with cross-appointments in Molecular Genetics and Computer Science. Quaid is a faculty member at the Vector Institute for Artificial Intelligence (AI) in Toronto, where he holds a Canada CIFAR AI chair. He pursued graduate training and research in machine learning at the Gatsby Unit with Peter Dayan and Geoffrey Hinton at the University College London and obtained his PhD in Computational Neuroscience from Massachusetts Institute of Technology. Quaid's B.Sc. is in computer science, and his PDF is in computational biology with Brendan Frey and Timothy Hughes, both at the University of Toronto.

Morris lab (http://www.morrislab.ca/) uses machine learning and artificial intelligence to do biomedical research, focusing on cancer evolution, post-transcriptional regulation, and gene function prediction. The lab has published more than 100 papers in both high impact journals (Nature, Science, Cell), focused field-specific journals (Nature Methods, Genome Biology, Bioinformatics), and computer science and machine learning conferences (NeurIPS). For the past two years, he has been a Clarivate highly cited researcher.


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.