Upcoming Seminars

Full Fall 2021 Seminar Schedule

September 2021

September 13th – Olga Russakovsky, Princeton University (Virtual)

September 20th – Greg Asner, Arizona State University (Virtual)

September 27th – Stefano Ermon, Stanford University (Virtual)

October 2021

October 4th – Jie Chen & Linwei Hu, Wells Fargo (Virtual)

October 11th – Mahlet (Milly) Zimeta, Open Data Institute (Virtual)

October 25th – Tarun Joshi, Wells Fargo (Virtual)

November 2021

November 1st – Phebe Vayanos, University of Southern California (Virtual)

November 8th – Ben Green, Ford School of Public Policy & Jeff Shen, School of Information

November 15th & 16th – MIDAS Annual Symposium

November 22nd – Juliana Freire, NYU

November 29th – Yong Chen, University of Pennsylvania

December 2021

December 6th – Pauline Kim, Washington University in St. Louis (Virtual)

December 13th – Katie Bouman, Caltech

Thank You to Our Sponsors

Past Seminars

2021

  • Phebe Vayanos
    WiSE Gabilan Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California; Associate Director of CAIS, the Center for Artificial Intelligence in Society at USC
    Towards robust, interpretable, and fair social and public health interventions
  • Tarun Joshi
    Financial Services Director, Wells Fargo
    SHAP Values for Explaining CNN Text Classifiers
  • Mahlet (Milly) Zimeta
    Head of Public Policy, Open Data Institute
    Knowns and Unknowns: Data and Metaphor
  • Linwei Hu
    Vice President in Advanced Technologies for Modeling, Wells Fargo
    Supervised Machine Learning: Applications, Opportunities, and Challenges in Banking with a Focus on Interpretability
  • Jie Chen
    Managing Director in Corporate Model Risk, Wells Fargo
    Supervised Machine Learning: Applications, Opportunities, and Challenges in Banking with a Focus on Interpretability
  • Stefano Ermon
    Assistant Professor, Computer Science, Stanford University
    AI for Sustainable Development
  • Greg Asner
    Director, ASU Center for Global Discovery and Conservation Science; Professor, Environmental Science, Arizona State University
    Large-scale Mapping of Corals to Guide Reef Conservation
  • Olga Russakovsky
    Assistant Professor, Computer Science, Princeton University
    Fairness in Visual Recognition: Redesigning the Datasets, Improving the Models and Diversifying the AI Leadership
  • Ben Wellington
    Quantitative Analyst, Two Sigma
    Shaping a City with Open Data
  • Vicki Bogan
    Associate Professor, Economics and Management, Cornell University; Director, Institute for Behavioral and Household Finance, Cornell University
    Financial Decision-Making and Mental Health
  • Vipin Kumar
    Regents Professor, Department of Computer Science and Engineering, University of Minnesota
    Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery
  • Anne Plant
    NIST Fellow, National Institute of Standards and Technology
    The Role of Measurement Science in Advancing Data Reliability
  • Mona Diab
    Professor, Computer Science, George Washington University
    Faithfulness in Natural Language Generation in an Era of Heightened Ethical Al Awareness: Opportunities for MT
  • Heng Ji
    Professor, Computer Science Department, University of Illinois Urbana-Champaign
    Knowledge Extraction to Accelerate Scientific Discovery
  • Simine Vazire
    Associate Professor, Psychology, University of Melbourne
    Evaluating research on its own merits
  • Casey Greene
    Professor, Biochemistry and Molecular Genetics, University of Colorado
    Machine learning can help to realize the bounty of the commons
  • Misty Heggeness
    Research Economist, US Census Bureau
    Estimating the immediate impact of the COVID-19 shock on parental attachment to the labor market and the double bind of mothers
  • Arya Farahi
    Data Science Fellow, MIDAS, University of Michigan
    KiTE: A framework for algorithmic trustworthiness
  • Timnit Gebru
    Computer Scientist, former Co-Lead Ethical AI Research Team, Google Brain, Founder of Black in AI
    Computer Vision: Who is Helped and Who is Harmed?

2020

  • Stan Ahalt
    Professor, Computer Science, University of North Carolina at Chapel Hill Director, RENCI (Renaissance Computing Institute), University of North Carolina at Chapel Hill
    The Changing Landscape of Biomedical Data Collections
  • Eric Xing
    Professor, Computer Science, Carnegie Mellon University
    From Performance-Oriented AI to Production- and Industrial- AI
  • Xiaoqian Jiang
    Associate Professor, Biomedical Informatics, University of Texas Health Science Center
    Genomic Data Sharing: The Privacy Risk and Technical Mitigations
  • Stuart Soroka
    Michael W. Traugott Collegiate Professor in Communication and Media and Political Science, University of Michigan
    Tracking the 'Mood' of U.S. Social Media Coverage, 1990-2020
  • Paul Bennett
    Partner Research Manager, Microsoft
    Robust and Transparent AI in Search and Recommendation
  • Eric Horvitz
    Technical Fellow and Chief Scientific Officer
    Fireside Chat
  • Lauren Klein
    Associate Professor, English, Quantitative Theory and Methods
    Data Feminism
  • Catherine D'Ignazio
    Assistant Professor, Urban Science & Planning
    Data Feminism
  • Christo Wilson
    Associate Professor, Khoury College of Computer Science, Northeastern University
    Auditing for Bias in Resume Search Engines
  • Thorsten Joachims
    Professor, Department of Computer Science, Department of Information Science, Cornell University
    Fair Ranking with Biased Data
  • Cristian Danescu-Niculescu-Mizil
    Associate Professor, Department of Information Science, Cornell University
    TOWARDS AN ARTIFICIAL INTUITION: CONVERSATIONAL MARKERS OF (ANTI)SOCIAL DYNAMICS
  • Ivana Seric
    Data Scientist, Philadelphia 76ers
    The Evolution of Basketball with Data Science
  • Christopher Re
    Associate Professor, Computer Science, Stanford University
    Theory and Systems for Weak Supervision
  • David Shor
    Democratic Data Scientist
    Data Science in Politics
  • Ryan Adams
    Professor of Computer Science, Princeton University
    SOME NEW IDEAS FOR UNBIASED GRADIENT ESTIMATION IN OPTIMIZATION
  • Kentaro Toyama
    W.K. Kellogg Professor of Community Information & Professor of Information,, School of Information
    Beyond Algorithmic Fairness: What Social Justice Needs From Data Science
  • 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

MIDAS Seminar Program Committee 2021

Libby Hemphill
School of Information

Justin Johnson
Computer Science and Engineering

Sophia Brueckner
Stamps School of Art & Design

Jing Liu
MIDAS

Christopher Miller
Astronomy

Lilli Zhao
Biostatistics

Prasad Shankar
Radiology

James Walsh
MIDAS