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  October 29November 5, 3 pm CT Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress in Predictive Inference Reconstructing SARS-COV-2 Response Pathways Ziv Bar-Joseph, FORE Systems Professor of Computer Science, Carnegie Mellon Emmanuel Candès, Barnum-Simons Chair in Mathematics and Statistics, and Professor, by courtesy, of Electrical Engineering, Stanford University REGISTER FOR ZOOM WEBINAR 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). SARS-CoV-2 is known to primarily impact cells via two viral entry factors, ACE2 and TMPRSS2. However, much less is currently known about virus activity within cells. We used computational methods based on probabilistic graphical models to integrate several recent SARS-CoV-2 interaction and expression datasets with general protein-protein and protein-DNA interaction datasets. The reconstructed models display the pathways viral proteins use to drive expression in human cells and the pathways the cell uses to respond to the infection. Intersecting key proteins on these pathways with expression data from underlying conditions shown to increase mortality from SARS-CoV-2, and with knockout and phosphorylation data, identifies a few potential targets for treating cells to reduce viral loads. Ziv Bar-Joseph is the FORE Systems Professor of Computational Biology and Machine Learning at Carnegie Mellon University. His work focuses on the development of machine learning methods for the analysis, modeling, and visualization of time series high throughput biological data. Dr. Bar-Joseph is the recipient of the Overton Prize, an NSF CAREER Award, and several conference Best Paper awards. He is currently leading the Computational Tools Center for the National Institutes of Health Human BioMolecular Atlas Program (HuBMAP). He has served on the advisory board of several national efforts including the National Institute for Allergy and Infectious Diseases Systems Biology Program. Software tools developed by his group are widely used for the analysis of genomics dataEmmanuel 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 2014.

  
  

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