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Michigan Institute for Data Science (MIDAS) Seminars

Events for 2018

Information-Theoretic Approaches to Neural Network Compression, Clustering and Concept Learning

Lav Varshney, Assistant Professor , UIUC

Larger, Faster, Random(ized): Computing in the Era of Big Data

Ioana Dumitriu, PhD, Department of Mathematics University of Washington

Baseball Umpire Calls as a Naturally Occurring Data Source for Revealing Principles of Bias and Learning in Perceptual Judgments

Robert Goldstone, PhD, Chancellor's Professor of Psychological and Brain Sciences, Department of Psychological & Brain Sciences Indiana University, Bloomington

Geometries of Word Embeddings

Pramod Viswanath, Professor, Electrical Engineering and Computer Science, Univeristy of Illinois at Urbana-Champaign

Data Science at Columbia University

Jeannette Wing, PhD, Avanessians Director of the Data Science Institute, Professor of Computer Science, Columbia University

Building Scalable Machine Learning Solutions for Data Curation

Ihab F. Ilyas, Professor, Cheriton School of Computer Science, University of Waterloo

Data and Decision Science for Mobility Services

Pascal Van Hentenryck, PhD, Seth Bonder Collegiate Professor, Industiral Operations and Engineering, Computer Science and Engineering

Technology, The Science of Learning, and Transformation in Higher Education

Candace Thille, PhD, Stanford University

Increasing the Utility of Machine Learning in Clinical Care

Jenna Wiens, PhD, Assistant Professor, Electrical Engineering & Computer Science, The University of Michigan

Protecting Security and Privacy of Connected & Automated Cars

Frank Kargl, PhD, Director, Institute for Distributed Systems, The University of Ulm, Germany

Sparsity and Decomposition in Semidefinite Optimization

Lieven Vandenberghe, PhD, Electrical and Computer Engineering Dept of Mathematics University of California, Los Angeles

Parameter Bounds Under Misspecified Models and Some Perspectives for Data Science and Learning

Christ D. Richmond, PhD, School of Electrical, Computer and Energy Engineering Arizona State University

The power of bias and what to do about it

John P.A. Ioannidis, MD, DSC, Professor of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, Stanford University

Prediction Machines: The Simple Economics of Artificial Intelligence

Avi Goldfarb, PhD, Ellison Professor of Marketing, Rotman School of Management University of Toronto

The Enigmatic Kime: Time Complexity in Data Science

Ivo D. Dinov, Professor of Health Behavior and Biological Sciences, School of Nursing Professor of Computational Medicine and Bioinformatics, Medical School Associate Director of Education and Training, Michigan Institute for Data Science, University of Michigan

Statistical models for analyzing dynamic social network data

Kevin S. Xu, Assistant Professor, Electrical Engineering and Computer Science, University of Toledo

New methods for detecting natural selection in large samples of genetic data

Jonathan Terhorst, Assistant Professor of Statistics, University of Michigan

Determine the Number of States in Hidden Markov Models via Marginal Likelihood

Yang Chen, Assistant Professor of Statistics, University of Michigan

The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences

Chris Callison-Burch, PhD, Associate Professor, University of Pennsylvania

Two-Step Estimation and Inference with Possibly Many Included Covariates

Matias D. Cattaneo, Professor of Economics Professor of Statistics, University of Michigan

Reliable evidence from health care data: lessons from the Observational Health Data Sciences and Informatics (OHDSI) collaborative

Marc A. Suchard, Professor Department of Biostatistics Department of Biomathematics Department of Human Genetics, University of California, Los Angeles

Person Specific Temporal Networks: Accuracy, Dynamics, and Emojis 😕

Adriene Beltz, Assistant Professor of Psychology, University of Michigan

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