The last decade has seen both machine learning and biology transformed: the former by the ability to train complex predictors on massive labelled data sets; the latter by the ability to perturb and measure biological systems with staggering throughput, breadth, and resolution. However, fundamentally new ideas in machine learning are needed to translate biomedical data at scale into a mechanistic understanding of biology and disease at a level of abstraction beyond single genes. This challenge has the potential to drive the next decade of creativity in machine learning as the field grapples with how to move beyond prediction to a regime that broadly catalyzes and accelerates scientific discovery.
To seize this opportunity, we will bring together current and future leaders within each field to introduce the next generation of machine learning specialists to the next generation of biological problems. Our full-day workshop will start a deeper dialogue with the goal of Learning Meaningful Representations of Life (LMRL), emphasizing interpretable representation learning of structure and principles. The workshop will address this challenge at five layers of biological abstraction (genome, molecule, cell, system, phenome) through interactive breakout sessions led by a diverse team of experimentalists and computational scientists to facilitate substantive discussion.
We are calling for short abstracts from computer scientists and biological scientists. Submission deadline is Friday, September 20. Significant travel support is also available. Details here:
Elizabeth Wood (Broad Institute)
Elizabeth Wood co-founded and co-runs JURA Bio, Inc., an early-stage therapeutics start up focusing on developing and delivering cell-based therapies for the treatment of autoimmune and immune-related neurodegenerative disease. Before founding JURA, Wood was a post-doc in the lab of Adam Cohen at Harvard, after completing her PhD studies with Angela Belcher and Markus Buehler at MIT, and Claus Helix-Neilsen at The Technical University of Denmark. She has also worked at the University of Copenhagen’s Biocenter with Kresten Lindorff-Larsen, integrating computational methods with experimental studies to understand how the ability of proteins to change their shape help modulate their function. Elizabeth Wood is a visiting scientist at the Broad Institute, where she serves on the steering committee of the Machine Inference Algorithm’s Initiative.
Yakir Reshef (Harvard University)
Jon Bloom (Broad Institute of MIT and Harvard)
Jasper Snoek (University of Toronto)
Barbara Engelhardt (Princeton University)
Scott Linderman (Stanford University)
Suchi Saria (Johns Hopkins University)
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’.
Alexander Wiltschko (Google Brain)
Casey Greene (University of Pennsylvania)
Chang Liu (UC Irvine)
Professor Liu’s research is in the fields of synthetic biology, chemical biology, and directed evolution. He is particularly interested in engineering specialized genetic systems for rapid mutation and evolution of genes in vivo. These systems can then be widely applied for the engineering, discovery, and understanding of biological function.
Kresten Lindorff-Larsen (University of Copenhagen)
Kresten Lindorff-Larsen trained as a biochemist at the University of Copenhagen and Carlsberg Laboratory, and completed his Ph.D. at the University of Cambridge in 2004. He then moved on to become an assistant professor in Copenhagen before joining D. E. Shaw Research in New York in 2007. He returned to Copenhagen in 2011, where he now serves as a Professor of Computational Protein Biophysics. He received the Danish Independent Research Councils’ Young Researchers’ Award in 2006, was a co-recipient of the 2009 Gordon Bell Prize, and has received several prestigious grants including a Hallas-Møller stipend (2011), a Sapere Aude grant (2012), and most recently a Novo Nordisk Foundation challenge programme grant (2019). His current research interests include developing and applying computational methods for integrative structural biology, and the integration of biophysics and genomics research.
Debora Marks (Harvard University)
Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. During her PhD, she quantified the pan-genomic scope of microRNA targeting - the combinatorial regulation of protein expression and co-discovered the first microRNA in a virus. As a postdoc she made a breakthrough in the classic, unsolved problem of ab initio 3D structure prediction of proteins using undirected graphical probability models for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her new lab at Harvard is interested in developing methods in deep learning to address a wide range of biological challenges including designing drug affinity libraries for large numbers of human genes, predicting epistasis in antibiotic resistance, the effects of genetic variation on human disease etiology and drug response and sequence design for biosynthetic applications.
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