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Michigan Institute for Data Science (MIDAS) Seminars

Events for 2019

How to Make Causal Inferences Using Texts

Justin Grimmer, Professor, Department of Political Science Stanford University

Information Extraction from Online Text from Opinions to Arguments to Persuasion

Claire Cardie, PhD, Professor, Department of Computer Science Department of Information Science, Cornell University

Quantify Systematics from Mislabeled Tables in Supervised Learning

Chris Miller, PhD, Associate Professor of Astronomy Associate Professor of Physics, College of Literature, Science, and the Arts University of Michigan

What Can Tweets Tell Us About Public Opinions? Uncovering the Data Generating Process by Linking Twitter Data with Surveys

Josh Pasek, PhD, Assistant Professor, Communication Studies, University of Michigan

Leveraging Information Theory to Practical Machine Learning: Minimum Description Length Regularization for Online Learning

Gil Shamir, PhD, Google AI

Infusing Structure into Machine Learning Algorithms

Animashree Anandkumar, PhD, Professor, Computing + Mathematical Sciences, California Institute of Technology

Scalable Bayesian Inference With Hamilton Monte Carlo

Michael Betancourt, PhD, Principal Research Scientist, Symplectomorphic, LLC

Making Connections: Data Science Approaches to Understanding Mood and Cognition in the Modern Er

Alex Leow, MD, PhD, Associate Professor, Departments of Psychiatry, Bioengineering, and Computer Science University of Illinois at Chicago

Data Science at The New York Times

Chris Wiggins, PhD, Associate Professor, Columbia University

statistical Methods for Flexible Differential Analysis of Cross-Sample Single-Cell RNA-Seq Datasets

Mark Robinson, PhD, Associate Professor of Statistical Genomics Institute of Molecular Sciences, University ofn Zurich

The Strength of Long-Range Ties

Patrick Park PhD, MIDAS Fellow

Predictive Analytics in Healthcare: From Anomaly Detection to Development of Clinical Decision Systems

Elyas Sabeti, PhD, MIDAS Fellow, University of Michigan

Benchmarking at Scale: Comparing Analysis Workflows for Single-Cell Genomic Data

Jun Li, Professor of Human Genetics, University of Michigan

Are you a researcher looking for genetic and clinic data? Do you need assistance in data analysis?

Erin O’Brein Kaleba, MPH Director, Data Office for Clinic and Translational Research; Cinzia Villanucci Smothers, M. Bioethics Project Manager

PCS Framework, Interpretable Machine Learning, and Deep Neural Networks

Bin Yu, PHD, Chancellor’s Professor, Electrical Engineering and Computer Science University of California, Berkeley

Learning & Exploiting Low-Dimensional Structure in High-Dimentional Data

David Dunson, PhD , Arts & Sciences Distinguished Professor of Statistical Science & Mathematics, Duke University

Constructing Tumor-Specific Gene Regulatory Networks Based on Samples with Tumor Purity Heterogeneity

Pei Wang, PhD – Icahn School of Medicine, Mount Sinai, Professor, Department of Genetics and Geomic Sciences

How Powerful Are Graph Neural Networks?

Jure Leskovec, Associate Professor, Stanford University

Quantifying Cell Type-Specific Changes in Transcriptional State and Gene Co-Regulation Across Multiple Datasets Using SCRANA-SEQ

Gerald Quon, Assistant Professor, University of California

Bias in Search and Recommenders Systems

Ricardo Baeza-Yates, CTO, NTENT Director, Data Science Programs,, Northeastern University, Silicon Valley Campus

Word Embeddings: What Works, What Doesn’t, and How to Tell the Difference for Applied Research

Arthur Spirling, Deputy Director,, Center for Data Science, New York University

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