crowdThe MIDAS Seminar Series features leading data scientists from around the world and across the U-M campuses addressing a variety of topics in data science.

MIDAS gratefully acknowledges Wacker Chemie AG for its generous support of the MIDAS Seminar Series.

Seminars are live-streamed at

Upcoming Seminars

September 23, 2019
Weiser Hall, 10th Floor
500 Church St.

Jun Li, PhD – University of Michigan

Benchmarking at scale: comparing analysis workflows for single-cell genomic data

September 30, 2019
Hussey Room, Michigan League

Precision Health – University of Michigan

The Precision Health Analytics Platform: data, analytics, and resources for your research.

October 4, 2019
Palmer Commons, Forum Hall
100 Washtenaw Ave.

Smita Krishnasw, PhD – Yale University


October 7, 2019
Weiser Hall, 10th Floor

500 Church St.

Luonan Chen, PhD – Chinese Academy of Sciences


October 14, 2019
Weiser Hall, 10th Floor
500 Church St.

Bin Yu, PhD – University of California, Berkeley


October 21, 2019

David Dunson, PhD – Duke University


October 28, 2019

Pei Wang, PhD – Icahn School of Medicine at Mount Sinai


November 4, 2019
Weiser Hall, 10th Floor
500 Church St.

Sean Taylor, PhD – Lyft


Previous Seminars

  • Goncalo Abecasis
    University of Michigan
    Sequencing 10,000's of Human Genomes: Early Results, Opportunities and Challenges
  • Jacob Abernethy
    University of Michigan
    Statistical and Algorithmic Tools to Aid Recovery in Flint
  • Animashree Anandkumar
    California Institute of Technology
    Infusing Structure into Machine Learning Algorithms
  • Laura Balzano
    University of Michigan
    Finding Low-Rank Structure in Messy Data
  • Adriene Beltz
    University of Michigan
    Person Specific Temporal Networks: Accuracy, Dynamics, and Emojis 😕
  • Michael Betancourt
    Symplectomorphic, LLC
    Scalable Bayesian Inference with Hamiltonian Monte Carlo
  • David Blei
    Columbia University
    Probabilistic Topic Models and User Behavior
  • Yoram Bresler
    University of Illinois, Urbana-Champaign
    Sparse Signal Recovery in Bilinear Inverse Problems
  • Tamara Broderick
    Massachusetts Institute of Technology
    Automated Scalable Bayesian Inference Via Data Summarization
  • Tianxi Cai
    Harvard University
    Efficient Use of EHR for Biomedical Translational Research
  • Chris Callison-Burch
    University of Pennsylvania
    The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences
  • Claire Cardie
    Cornell University
    Information Extraction from Online Text -- from Opinions to Arguments to Persuasion
  • Matias D. Cattaneo
    University of Michigan
    Two-Step Estimation and Inference with Possibly Many Included Covariates
  • Michael Cavaretta
    Ford Motor Company
    Data and Analytics: Emerging Trends and Opportunities at Ford Motor Company
  • Yang Chen
    University of Michigan
    Determine the Number of States in Hidden Markov Models via Marginal Likelihood
  • Yuejie Chi
    Ohio State University
    Solving Corrupted Systems of Quadratic Equations, Provably
  • Ivo D. Dinov
    University of Michigan
    The Enigmatic Kime: Time Complexity in Data Science
  • Ioana Dumitriu
    University of Washington
    Larger, Faster, Random(ized): Computing in the Era of Big Data
  • Carol Flannagan
    University of Michigan
    From Model T to Waymo: Cars have gotten better—has transportation data science?
  • Geoff Ginsburg
    Duke University
    Novel Genomic Paradigms for Early Detection and Diagnosis of Acute Infectious Disease
  • Avi Goldfarb
    University of Toronto
    Prediction Machines: The Simple Economics of Artificial Intelligence
  • Robert Goldstone
    Indiana University
    Baseball Umpire Calls as a Naturally Occurring Data Source for Revealing Principles of Bias and Learning in Perceptual Judgments
  • Rob Goodman
    A Mind at Play: How Claude Shannon Invented the Information Age
  • Justin Grimmer
    Stanford University
    How to Make Causal Inferences Using Texts
  • Margaret Hedstrom
    University of Michigan
    Why Data Science Needs Curation and Why Curation Needs Data Science
  • Ihab F. Ilyas
    University of Waterloo
    Building Scalable Machine Learning Solutions for Data Curation
  • John P.A. Ioannidis
    Stanford University
    The Power of Bias and What To Do About It
  • Tony Jebara
    Columbia University
    Probabilistic Graphical Models and Online Learning
  • Michael Jordan
    University of California, Berkeley
    On Computational Thinking, Inferential Thinking and Data Science
  • Frank Kargl
    University of Ulm, Germany
    Protecting Security and Privacy of Connected & Automated Cars
  • Gary King
    Harvard University
    Big Data is Not About the Data!
  • Tamara Kolda
    Sandia National Labs
    An Overview of Tensor Decompositions for Data Analysis,with Emphasis on Computation and Scalability
  • Danai Koutra
    University of Michigan
    Inferring, Summarizing and Mining Large-scale Graph Data
  • Alex Leow
    University of Illinois at Chicago
    Making Connections: Data Science Approaches to Understanding Mood and Cognition in the Modern Era
  • Bing Liu
    University of Illinois, Chicago
    Lifelong Machine Learning
  • Tim McKay
    University of Michigan
    Personalizing Education at Scale
  • Chris Miller
    University of Michigan
    Quantify Systematics from Mislabeled Truth Tables in Supervised Learning
  • Emily Mower Provost
    University of Michigan
    Human-Centered Computing: Using Speech to Understand Behavior
  • Susan Murphy
    University of Michigan
    Learning Treatment Policies in Mobile Health
  • Mark Newman
    University of Michigan
    Structure and Function in Complex Networks
  • Matthew Nokleby
    Wayne State University
    Bits through Sensors: Bounds on Classification and Learning Performance via Information Content
  • Robert Nowak
    University of Wisconsin-Madison
    Learning Human Preferences and Perceptions From Data
  • Mesrob I. Ohannessian
    University of California, San Diego
    Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization
  • Dimitris Papanikolaou
    Harvard University
    The Potential of On-Demand Mobility: Lessons from System Analysis and Data Visualization
  • Josh Pasek
    University of Michigan
    What Can Tweets Tell Us About Public Opinions? Uncovering the Data Generating Process by Linking Twitter Data with Surveys
  • Sandy Pentland
    Massachusetts Institute of Technology Media Lab
    Social Physics: Harnessing Big Data and Machine Learning in Support of Human Goals
  • Jose Perea
    Michigan State University
    The Shape of Data
  • Dragomir Radev
    University of Michigan
    Natural Language Processing for Collective Discourse
  • Christopher Ré
    Stanford University
    DeepDive: A Dark Data System
  • Christ D. Richmond
    Arizona State University
    Parameter Bounds Under Misspecified Models and Some Perspectives for Data Science and Learning
  • Mark Robinson
    University of Zurich
    Statistical Methods for Flexible Differential Analysis of Cross-sample Single-cell RNA-seq Datasets
  • Christopher J. Rozell
    Georgia Institute of Technology
    Closing the Loop Between Mind and Machine: Building Algorithms to Interface with Brains at Multiple Scales
  • Perry Samson
    University of Michigan
    Learning Analytics: Mining Student Behaviors During Class to Predict Exam Scores
  • Gil Shamir
    Google AI
    Leveraging Information Theory to Practical Machine Learning: Minimum Description Length Regularization for Online Learning
  • Jimmy Soni
    A Mind at Play: How Claude Shannon Invented the Information Age
  • Marc A. Suchard
    University of California, Los Angeles
    Reliable Evidence from Health Care Data: Lessons from the Observational Health Data Sciences and Informatics (OHDSI) Collaborative
  • Amit Surana
    United Technologies Research Center
    Koopman Operator Theoretic Framework for Dynamic Data Analytics
  • Jonathan Terhorst
    University of Michigan
    New Methods for Detecting Natural Selection in Large Samples of Genetic Data
  • Candace Thille
    Stanford University
    Technology, The Science of Learning, and Transformation in Higher Education
  • Lieven Vandenberghe
    University of California, Los Angeles
    Sparsity and Decomposition in Semidefinite Optimization
  • Pascal Van Hentenryck
    University of Michigan
    Data and Decision Science for Mobility Services
  • Lav Varshney
    University of Illinois at Urbana-Champaign
    Information-Theoretic Approaches to Neural Network Compression, Clustering and Concept Learning
  • Lav Varshney
    University of Illinois at Urbana-Champaign
    On Data-driven Creativity
  • Pramod Viswanath
    Univeristy of Illinois at Urbana-Champaign
    Geometries of Word Embeddings
  • Kevin Ward
    University of Michigan
    Data in Motion Phenotyping: From the Intensive Care Unit to the Home
  • Jenna Wiens
    University of Michigan
    Increasing the Utility of Machine Learning in Clinical Care
  • Chris Wiggins
    Columbia University, The New York Times
    Data Science at The New York Times
  • Rebecca Willett
    University of Wisconsin
    Estimating High-Dimensional Autoregressive Point Processes
  • Jeannette Wing
    Columbia University
    Data Science at Columbia University
  • Yao Xie
    Georgia Institute of Technology
    Sequential Change-point Detection over Dynamic Networks
  • Kevin Xu
    University of Toledo
    Statistical Models for Analyzing Dynamic Social Network Data
  • Ji Zhu
    University of Michigan
    Matrix Completion in Network Analysis