2019-2020 Seminar Themes

  • Data Science and the Human Health
  • Data Science and the Human Society
  • Data Science, Language and Music
  • Spotlight on AI
  • Spotlight on Computing
  • Spotlight on Statistics

Upcoming Seminars

Seminars with an (*) next to the title will be live-streamed at http://midas.umich.edu/seminar-stream/

Seminars with a (+) will be recorded and can be viewed by clicking on the seminar title/picture at the bottom of this page, then selecting “recorded seminar”

November 25, 2019
3:30pm- 4:30pm
Weiser Hall, 1010
500 Church Street

Ricardo Baeza-Yates, NTENT

(*+) Bias in Search and Recommender Systems

December 2, 2019
3:30pm- 4:30pm
Room 340, West Hall

Arthur Spirling, Associate Professor, New York University

What works, what doesn’t, and how to tell the difference for applied research

January 13, 2020
Room 340, West Hall

January 27, 2020

3:30 – 4:30 pm
Room 340, West Hall

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
  • David Dunson
    Duke University
    Arts & Sciences Distinguished Professor of Statistical Science & Mathematics
  • 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
  • Erin O’Brien Kaleba
    University of Michigan
    The Precision Health Analytics Platform: data, analytics, and resources for your research
  • 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
  • Smita Krishnaswamy
    University of Michigan
    Learning Representations for Enabling Insight Into Big Biomedical Data
  • Alex Leow
    University of Illinois at Chicago
    Making Connections: Data Science Approaches to Understanding Mood and Cognition in the Modern Era
  • Jure Leskovec
    Stanford University
    How Powerful are Graph Neural Networks?
  • Jun Li
    University of Michigan
    Benchmarking at scale: comparing analysis workflows for single-cell genomic data
  • 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
  • Patrick Park
    University of Michigan
    The Strength of Long-Range Ties
  • 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
  • Gerald Quon
    University of California, Davis
    Quantifying cell type-specific changes in transcriptional state and gene co-regulation across multiple datasets using scRNA-seq
  • 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
  • Elyas Sabeti
    University of Michigan
    Predictive Analytics in Healthcare: From Anomaly Detection to Development of Clinical Decision Support Systems
  • 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
  • Cinzia Villanucci Smothers
    University of Michigan
    The Precision Health Analytics Platform: data, analytics, and resources for your research
  • 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
  • Sean Taylor
    Causal Modeling with Many Experiments
  • 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
  • Pei Wang
    Icahn School of Medicine at Mount Sinai
    Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity
  • 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
  • Bin Yu
    University of California at Berkeley
    PCS framework, interpretable machine learning, and deep neural networks
  • Ji Zhu
    University of Michigan
    Matrix Completion in Network Analysis