Upcoming Seminars

Fall 2020 Seminar Schedule

August/September 2020

8/31/2020 – Kentaro Toyama, University of Michigan

9/14/2020MIDAS Reproducibility Day

9/21/2020Ryan Adams, Princeton University

9/28/2020David Shor, Democratic Political Consultant

October 2020

10/5/2020Christopher Re, Stanford University

10/12/2020Ivana Seric, Philadelphia 76ers Data Scientist

10/26/2020Thorsten Joachims, Cornell University

November 2020

11/2/2020Christo Wilson, Northeastern University

11/10/2020 MIDAS Annual Symposium (Day 1)

11/11/2020MIDAS Annual Symposium (Day 2)

11/16/2020Paul Bennett, Microsoft

11/23/2020 – TBA

December 2020

12/7/2020Xiaoqian Jiang, University of Texas Health Science Center

12/14/2020 – TBA

12/21/2020 – TBA

Previous Seminars

2020

  • Daniel Forger
    Professor of Computational Medicine, Bioinformatics, and Mathematics, University of Michigan
    A mobile/wearable platform for collecting data on sleep and circadian rhythms in the time of COVID
  • Wayne Wang*, Al Hero**, & Walter Dempsey***
    *PhD Student, Statistics **Professor of EECS, Engineering ***Assistant Professor, School of Public Health, University of Michigan
    A scalable tool for longitudinal Twitter analysis: understanding the impact of COVID-19 on public discourse
  • J. Scott Van Epps
    Assistant Professor, Emergency Medicine, University of Michigan
    The perils and promise of machine learning for life-threatening infection
  • Holly Hartman & Jack Griffin
    *PHD Candidate, Biostatistics, School of Public Health, University of Michigan **Founder/CEO of FoodFinder, University of Michigan Alum (2019)
    Using Data for Good in the Age of COVID-19
  • Rada Mihalcea
    Professor, EECS – College of Engineering, University of Michigan
    Life during COVID-19: a Billion-word Story as Told by a Natural Language Processing Researcher
  • Veera Baladandayuthapani & Zhenke Wu
    Professor of Biostatistics, University of Michigan & Assistant Professor of Biostatistics, University of Michigan
    Understanding COVID-19 Dynamics via Individual-level Temporal and Network Modeling: Lessons from India and China
  • Karandeep Singh
    Assistant Professor, Learning Health Sciences and Medicine, University of Michigan
    Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients
  • Jon Zelner
    Assistant Professor, Epidemiology, University of Michigan
    If the outbreak ended, does that mean the interventions worked?
  • Bhramar Mukherjee
    John D. Kalbfleisch Collegiate Professor of Biostatistics, University of Michigan
    Predictions, Role of Interventions and the Crisis of Virus in India: Data Science Call to Arms
  • Siqian Shen
    Associate Professor Department of Industrial & Operations Engineering, University of Michigan
    From Data to Actions, From Observations to Solutions — A Summary of Operations Research and Industrial Engineering Tools for Fighting COVID-19
  • Nicholas Diakopoulos
    Assistant Professor, School of Communication, Northwestern University Director, Computational Journalism Lab
    The Role of Algorithmic Intermediaries in Shaping Attention to News
  • Luonan Chen
    Chinese Academy of Sciences
    Criticality detection and time-series prediction from small samples by dynamics-based model-free approaches
  • Benjamin Lauderdale
    University College London
    Model-Based Pre-Election Polling for National and Sub-National Outcomes
  • Qianying Lin
    MIDAS Data Science Fellow
    COVID-19 outbreak in Wuhan, China: in retrospect and in prospect
  • Jen Stirrup
    Founder and CEO, Data Relish; Microsoft Regional Director
    Is it possible to democratize AI for businesses?
  • Julia Stoyanovich
    New York University
    Follow the Data! Responsible Data Science Starts with Responsible Data Management
  • Edward Stabler
    Professor of Linguistics, University of California, Los Angeles
    Adversarial Training for Semantic Parsing
  • Charles Yang
    University of Pennsylvania
    How Language Makes Us Smart (Without Big Data)

2019

  • Animashree Anandkumar
    California Institute of Technology
    Infusing Structure into Machine Learning Algorithms
  • Ricardo Baeza-Yates
    NTENT
    Bias in Search and Recommender Systems
  • Michael Betancourt
    Symplectomorphic, LLC
    Scalable Bayesian Inference with Hamiltonian Monte Carlo
  • Claire Cardie
    Cornell University
    Information Extraction from Online Text -- from Opinions to Arguments to Persuasion
  • David Dunson
    Duke University
    Arts & Sciences Distinguished Professor of Statistical Science & Mathematics
  • Arya Farahi
    University of Michigan
    A Quest for the Dark Sector of the Universe: The Role of Galaxy Clusters in the Era of Precision Cosmology
  • Justin Grimmer
    Stanford University
    How to Make Causal Inferences Using Texts
  • Erin O’Brien Kaleba
    University of Michigan
    The Precision Health Analytics Platform: data, analytics, and resources for your research
  • 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
  • Qianying Lin
    University of Michigan
    Integration of Epidemic Time Series and Genetic Sequences
  • Chris Miller
    University of Michigan
    Quantify Systematics from Mislabeled Truth Tables in Supervised Learning
  • 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
  • Gerald Quon
    University of California, Davis
    Quantifying cell type-specific changes in transcriptional state and gene co-regulation across multiple datasets using scRNA-seq
  • Mark Robinson
    University of Zurich
    Statistical Methods for Flexible Differential Analysis of Cross-sample Single-cell RNA-seq Datasets
  • Elyas Sabeti
    University of Michigan
    Predictive Analytics in Healthcare: From Anomaly Detection to Development of Clinical Decision Support Systems
  • 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
  • Arthur Spirling
    New York University
    Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research
  • Sean Taylor
    Lyft
    Causal Modeling with Many Experiments
  • Pei Wang
    Icahn School of Medicine at Mount Sinai
    Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity
  • Chris Wiggins
    Columbia University, The New York Times
    Data Science at The New York Times
  • 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

2018

  • Adriene Beltz
    University of Michigan
    Person Specific Temporal Networks: Accuracy, Dynamics, and Emojis 😕
  • Tamara Broderick
    Massachusetts Institute of Technology
    Automated Scalable Bayesian Inference Via Data Summarization
  • Chris Callison-Burch
    University of Pennsylvania
    The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences
  • Matias D. Cattaneo
    University of Michigan
    Two-Step Estimation and Inference with Possibly Many Included Covariates
  • Yang Chen
    University of Michigan
    Determine the Number of States in Hidden Markov Models via Marginal Likelihood
  • 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
  • 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
  • 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
  • Frank Kargl
    University of Ulm, Germany
    Protecting Security and Privacy of Connected & Automated Cars
  • Christ D. Richmond
    Arizona State University
    Parameter Bounds Under Misspecified Models and Some Perspectives for Data Science and Learning
  • 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
  • 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
  • Pramod Viswanath
    Univeristy of Illinois at Urbana-Champaign
    Geometries of Word Embeddings
  • Jenna Wiens
    University of Michigan
    Increasing the Utility of Machine Learning in Clinical Care
  • Arya Farahi
    University of Michigan
    A Quest for the Dark Sector of the Universe: The Role of Galaxy Clusters in the Era of Precision Cosmology
  • Jeannette Wing
    Columbia University
    Data Science at Columbia University
  • Kevin Xu
    University of Toledo
    Statistical Models for Analyzing Dynamic Social Network Data

2017

  • Laura Balzano
    University of Michigan
    Finding Low-Rank Structure in Messy Data
  • David Blei
    Columbia University
    Probabilistic Topic Models and User Behavior
  • Yoram Bresler
    University of Illinois, Urbana-Champaign
    Sparse Signal Recovery in Bilinear Inverse Problems
  • Tianxi Cai
    Harvard University
    Efficient Use of EHR for Biomedical Translational Research
  • Michael Cavaretta
    Ford Motor Company
    Data and Analytics: Emerging Trends and Opportunities at Ford Motor Company
  • Carol Flannagan
    University of Michigan
    From Model T to Waymo: Cars have gotten better—has transportation data science?
  • Rob Goodman
    Author
    A Mind at Play: How Claude Shannon Invented the Information Age
  • Margaret Hedstrom
    University of Michigan
    Why Data Science Needs Curation and Why Curation Needs Data Science
  • Tony Jebara
    Columbia University
    Probabilistic Graphical Models and Online Learning
  • Danai Koutra
    University of Michigan
    Inferring, Summarizing and Mining Large-scale Graph Data
  • Emily Mower Provost
    University of Michigan
    Human-Centered Computing: Using Speech to Understand Behavior
  • 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
  • Dimitris Papanikolaou
    Harvard University
    The Potential of On-Demand Mobility: Lessons from System Analysis and Data Visualization
  • Jose Perea
    Michigan State University
    The Shape of Data
  • Christopher J. Rozell
    Georgia Institute of Technology
    Closing the Loop Between Mind and Machine: Building Algorithms to Interface with Brains at Multiple Scales
  • Jimmy Soni
    Author
    A Mind at Play: How Claude Shannon Invented the Information Age
  • Lav Varshney
    University of Illinois at Urbana-Champaign
    On Data-driven Creativity
  • Yao Xie
    Georgia Institute of Technology
    Sequential Change-point Detection over Dynamic Networks

2015 and 2016

  • 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
  • Yuejie Chi
    Ohio State University
    Solving Corrupted Systems of Quadratic Equations, Provably
  • Geoff Ginsburg
    Duke University
    Novel Genomic Paradigms for Early Detection and Diagnosis of Acute Infectious Disease
  • Michael Jordan
    University of California, Berkeley
    On Computational Thinking, Inferential Thinking and Data Science
  • 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
  • Bing Liu
    University of Illinois, Chicago
    Lifelong Machine Learning
  • Tim McKay
    University of Michigan
    Personalizing Education at Scale
  • Susan Murphy
    University of Michigan
    Learning Treatment Policies in Mobile Health
  • Mark Newman
    University of Michigan
    Structure and Function in Complex Networks
  • Mesrob I. Ohannessian
    University of California, San Diego
    Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization
  • Sandy Pentland
    Massachusetts Institute of Technology Media Lab
    Social Physics: Harnessing Big Data and Machine Learning in Support of Human Goals
  • Dragomir Radev
    University of Michigan
    Natural Language Processing for Collective Discourse
  • Christopher Ré
    Stanford University
    DeepDive: A Dark Data System
  • Perry Samson
    University of Michigan
    Learning Analytics: Mining Student Behaviors During Class to Predict Exam Scores
  • Amit Surana
    United Technologies Research Center
    Koopman Operator Theoretic Framework for Dynamic Data Analytics
  • Kevin Ward
    University of Michigan
    Data in Motion Phenotyping: From the Intensive Care Unit to the Home
  • Rebecca Willett
    University of Wisconsin
    Estimating High-Dimensional Autoregressive Point Processes