Announcements

• Colloquium on Digital Transformation Science

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  October 2229, 3 pm CT Machine Learning-based Design of Proteins, Small Molecules, and Beyond Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference Emmanuel Candès, Barnum-Simons Chair in Mathematics and Statistics, and Professor, by courtesy, of Electrical Engineering, Stanford UniversityJennifer Listgarten, Professor of Electrical Engineering and Computer Sciences, University of California, Berkeley REGISTER FOR ZOOM WEBINAR Data-driven design is making headway into a number of application areas, including protein, small-molecule, and materials engineering. The design goal is to construct an object with desired properties, such as a protein therapeutic that binds tightly to its target. To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates. The aim then is to discover designs that are better than the best design in the observed data. This goal puts machine learning-based design in a much more difficult spot than traditional applications of predictive modelling, since successful design requires, by definition, some degree of extrapolation -- a pushing of the predictive models to its unknown limits, in parts of the design space that are a priori unknown. This talk will anchor the overall problem in protein engineering and discuss emerging approaches to tackle it. Recent progress in machine learning provides us with many potentially effective tools to learn from datasets of ever increasing sizes and make useful predictions. How do we know that these tools can be trusted in critical and high-sensitivity systems? If a learning algorithm predicts the GPA of a prospective college applicant, what guarantees do I have concerning the accuracy of this prediction? How do we know that it is not biased against certain groups of applicants? This talk introduces statistical ideas to ensure that the learned models satisfy some crucial properties, especially reliability and fairness (in the sense that the models need to apply to individuals in an equitable manner). To achieve these important objectives, we shall not “open up the black box” and try understanding its underpinnings. Rather, we discuss broad methodologies that can be wrapped around any black box to produce results that can be trusted and are equitable. We also show how our ideas can inform causal inference predictive. For instance, we will answer counterfactual predictive problems (i.e., predict what the outcome would have been if a patient had not been treated). Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a Professor of Electrical Engineering (by courtesy), and a member of the Institute of Computational and Mathematical Engineering, all at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing, and mathematical optimization with applications to the imaging sciences, scientific computing, and inverse problems. Candès has given over 60 plenary lectures in mathematics and statistics, biomedical imaging, and solid-state physics. Candès was awarded the Alan T. Waterman Award from the National Science Foundation and was elected to the National Academy of Sciences and the American Academy of Arts and Sciences in 2014Jennifer Listgarten is a Professor in the Department of Electrical Engineering and Computer Sciences and the 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 in 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 hometown. 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.

  
  

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