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Michigan Institute for Data Science (MIDAS) Seminars

Upcoming Seminars

MIDAS Seminar | Symposium

MIDAS mini-symposium | AI-driven Research Workflows

8:30am – 5:00pm in Weiser Hall

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Other Event | MIDAS Seminar

MIDAS Annual Data Science & AI Summit 2023

8:00am – 5:00pm in TBD

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Past Seminars

MIDAS Seminar | Symposium

MIDAS mini-symposium | Generative AI: Diffusion Models for Scientific Machine Learning

8:30am – 5:00pm in Weiser Hall

NSF Priorities and Opportunities for Data Science and AI

Dr. Pamela Davis-Kean, Program Director, Developmental Science Program of the NSF SBE Directorate

2:30pm – 4:30pm

An Under the Hood Look at Generative AI: Potentials and Pitfalls | Generative AI Coast-to-Coast Series

David Evan Harris, Senior Research Fellow,
International Computer Science Institute

Rada Mihalcea, Janice M Jenkins Collegiate Professor of Electrical Engineering and Computer Science; Director, Michigan Artificial Intelligence Laboratory; ,
University of Michigan

Sarah Stone: Moderator, Executive Director, eScience Institute,
University of Washington

3:00pm – 4:30pm

Conversations on Policy, Ethics, and Generative AI | Generative AI Coast-to-Coast Series

Marjory Blumenthal

Merve Hickok, President, Center for AI & Digital Policy,
School of Information – University of Michigan

Jing Liu : Moderator, Executive Director, Michigan Institute for Data Science,
University of Michigan

1:00pm – 2:30pm

Generative AI in the Lab | Generative AI Coast-to-Coast Series

Xia Ning, Professor, Biomedical Informatics, Computer Science and Engineering,
The Ohio State University

Arlei Silva, Assistant Professor of Computer Science,
Rice University

Angela Wilkins: Moderator, Executive Director, Ken Kennedy Institute,
Rice University

1:00pm – 2:30pm

NSF Priorities and Opportunities for Data Science and AI

Dr. Alfred Hero, Program Director in the NSF CISE Directorate, and the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and R. Jamison and Betty Williams Professor of Engineering, University of Michigan

1:00pm – 2:30pm in 340 West Hall

From Theory to Practice: Building Ethical and Trustworthy AI

9:00am – 5:00pm in Johnson Rooms, Lurie Engineering Center (3rd floor)

Data and AI in Society Forum Series: Generative AI, Composition, and Creativity

3:30pm – 4:30pm in Watkins Lecture Hall, Earl V. Moore Building, School of Music

Responsible Data Science and AI mini-symposium

1:30pm – 5:30pm in Rackham 4th floor Amphitheater

MIDAS Seminar | Women in Computing | Alumni

Beyond the First Portrait of a Black Hole

Katie Bouman, Assistant Professor, Computing and Mathematical Sciences, Caltech

4:00pm – 5:00pm in Remote/Virtual

U-M Data Science Annual Symposium 2020

With Prof. David Fouhey and Prof. Jason Corso

9:00am – 5:00pm in Remote/Virtual

Data Science and Politics

David Shor, SuperPAC Data Scientist, Democratic Consultant

8:00am – 5:00pm

MIDAS Reproducibility Day

2:00pm – 5:00pm

Beyond Algorithmic Fairness: What Social Justice Needs from Data Science

Kentaro Toyama, Professor of Community Information, Professor of Information, School of Information, University of Michigan

4:00pm – 5:00pm

MIDAS Reproducibility Challenge Showcase

Chris Brooks, Assistant Professor of Information – School of Information, University of Michigan

1:00pm – 2:00pm

“Is AI Racist?”

Dr. Jagadish and Other Experts, Professor, Electrical Engineering and Computer Science , Director of the Michigan Institute for Data Science

4:30pm – 5:30pm

MIDAS Reproducibility Challenge Showcase: #6

Sharon Glotzer, Professor, Chemical Engineering and Materials Science & Engineering, U-M

1:00pm – 2:00pm

MIDAS Reproducibility Challenge Showcase: Sharon Glotzer – An Open Software Approach for Reproducible Research for Materials Design

SHARON GLOTZER, Professor – Chemical Engineering and Materials Science and Engineering, University of Michigan

1:00pm – 2:00pm

MIDAS Reproducibility Challenge Showcase: #5

John Silberholz & Nigel Michki, Associate Professor, Technology & Operations, U-M & Doctoral student, Biophysics, U-M

1:00pm – 2:00pm

A Mobile/wearable Platform for Collecting Data on Sleep and Circadian Rhythms in the Time of COVID

Danny Forger, Professor of Computational Medicine, Bioinformatics, & Mathematics, U-M, University of Michigan

1:00pm – 2:00pm

A Scalable Tool for Longitudinal Twitter Analysis

Al Hero & Walter Dempsey, Professor, EECS & Engineering, U-M & Assistant Professor, School of Public Health, UM, University of Michigan

1:00pm – 2:00pm

MIDAS Seminar | Student Event

A Data Scientist Plays Games

Nick Berry, Data Scientist, Formerly with Microsoft, GameHouse, and Facebook

12:00pm – 1:30pm in Remote/Virtual

Is It Possible to Democratize AI for Business

Jen Stirrup, Regional Director

3:30pm – 4:30pm in 340 West Hall

How Language Makes Us Smart (Without Big Data)

Charles Yang, Director

3:30pm – 4:30pm in 340 West Hall

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

3:30pm – 4:30pm in 340 West Hall

Bias in Search and Recommenders Systems

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

3:30pm – 4:30pm in Weiser Hall

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

2:00am – 3:00pm in Forum Hall, Palmer Commons

How Powerful Are Graph Neural Networks?

Jure Leskovec, Associate Professor, Stanford University

3:30pm – 4:30pm in Weiser Hall

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

3:30pm – 4:30pm in 340 West Hall

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

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

3:30pm – 4:30pm in 340 West Hall

PCS Framework, Interpretable Machine Learning, and Deep Neural Networks

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

3:30pm – 4:30pm in Weiser Hall

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

3:30pm – 4:30pm in Hussey Room, Michigan League

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

Jun Li, Professor of Human Genetics, University of Michigan

3:30pm – 4:30pm in Weiser Hall

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

Elyas Sabeti, PhD, MIDAS Fellow, University of Michigan

3:30pm – 4:30pm in Vandenberg Room, Michigan League

The Strength of Long-Range Ties

Patrick Park PhD, MIDAS Fellow

3:30pm – 4:30pm in Vandenberg Room, Michigan League

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

Data Science at The New York Times

Chris Wiggins, PhD, Associate Professor, Columbia University

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

Scalable Bayesian Inference With Hamilton Monte Carlo

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

Infusing Structure into Machine Learning Algorithms

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

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

Gil Shamir, PhD

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

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

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

How to Make Causal Inferences Using Texts

Justin Grimmer, Professor, Department of Political Science Stanford University

Person Specific Temporal Networks: Accuracy, Dynamics, and Emojis 😕

Adriene Beltz, Assistant Professor of Psychology, University of Michigan

Reliable evidence from health care data: lessons from the Observational Health Data Sciences and Informatics (OHDSI) collaborative

Marc A. Suchard, Professor Department of Biostatistics Department of Biomathematics Department of Human Genetics, University of California, Los Angeles

Two-Step Estimation and Inference with Possibly Many Included Covariates

Matias D. Cattaneo, Professor of Economics Professor of Statistics, University of Michigan

The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences

Chris Callison-Burch, PhD, Associate Professor, University of Pennsylvania

Determine the Number of States in Hidden Markov Models via Marginal Likelihood

Yang Chen, Assistant Professor of Statistics, University of Michigan

New methods for detecting natural selection in large samples of genetic data

Jonathan Terhorst, Assistant Professor of Statistics, University of Michigan

Statistical models for analyzing dynamic social network data

Kevin S. Xu, Assistant Professor, Electrical Engineering and Computer Science, University of Toledo

The Enigmatic Kime: Time Complexity in Data Science

Ivo D. Dinov, Professor of Health Behavior and Biological Sciences, School of Nursing Professor of Computational Medicine and Bioinformatics, Medical School Associate Director of Education and Training, Michigan Institute for Data Science, University of Michigan

Prediction Machines: The Simple Economics of Artificial Intelligence

Avi Goldfarb, PhD, Ellison Professor of Marketing, Rotman School of Management University of Toronto

The power of bias and what to do about it

John P.A. Ioannidis, MD, DSC, Professor of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, Stanford University

Parameter Bounds Under Misspecified Models and Some Perspectives for Data Science and Learning

Christ D. Richmond, PhD

Sparsity and Decomposition in Semidefinite Optimization

Lieven Vandenberghe, PhD

Protecting Security and Privacy of Connected & Automated Cars

Frank Kargl, PhD, Director, Institute for Distributed Systems, The University of Ulm, Germany

Increasing the Utility of Machine Learning in Clinical Care

Jenna Wiens, PhD, Assistant Professor, Electrical Engineering & Computer Science, The University of Michigan

Technology, The Science of Learning, and Transformation in Higher Education

Candace Thille, PhD

Data and Decision Science for Mobility Services

Pascal Van Hentenryck, PhD, Seth Bonder Collegiate Professor, Industiral Operations and Engineering, Computer Science and Engineering

Building Scalable Machine Learning Solutions for Data Curation

Ihab F. Ilyas, Professor, Cheriton School of Computer Science, University of Waterloo

Data Science at Columbia University

Jeannette Wing, PhD, Avanessians Director of the Data Science Institute, Professor of Computer Science, Columbia University

Geometries of Word Embeddings

Pramod Viswanath, Professor, Electrical Engineering and Computer Science, Univeristy of Illinois at Urbana-Champaign

Baseball Umpire Calls as a Naturally Occurring Data Source for Revealing Principles of Bias and Learning in Perceptual Judgments

Robert Goldstone, PhD, Chancellor's Professor of Psychological and Brain Sciences, Department of Psychological & Brain Sciences Indiana University, Bloomington

Larger, Faster, Random(ized): Computing in the Era of Big Data

Ioana Dumitriu, PhD, Department of Mathematics University of Washington

Information-Theoretic Approaches to Neural Network Compression, Clustering and Concept Learning

Lav Varshney, Assistant Professor , UIUC

Bits through Sensors: Bounds on Classification and Learning Performance via Information Content

Matthew Nokleby, PhD, Assistant Professor, Department of Electrical and Computer Engineering Wayne State University

Closing the Loop Between Mind and Machine: Building Algorithms to Interface With Brains at Multiple Scales

Christopher J. Rozell, PhD, Associate Professor, Electrical and Computer Engineering, Georgia Institute of Technology

Why Data Science Needs Curation and Why Curation Needs Data Science

Margaret Hedstrom, PhD, Robert M. Warner Collegiate Professor of Information, School of Information, University of Michigan

Probabilistic Graphical Models and Online Learning

Tony Jebara, PhD, Associate Professor of Computer Science, Columbia University

Statistical Models for Analyzing Dynamic Social Network Data

Kevin Xu, PhD, Assistant Professor, University of Toledo

Sparse Signal Recovery in Bilinear Inverse Problems

Yoram Bresler, PhD

Inferring, Summarizing and Mining Large-scale Graph Data

Danai Koutra, PhD, Assistant Professor, Computer Science and Engineering, University of Michigan

Data and Analytics: Emerging trends and opportunities at Ford Motor Company

Michael Cavaretta, Ph.D., Director, Analytics Infrastructure, Ford Motor Compnay

Findig Low-Rank Structure in Messy Data

Laura Balzano, Assistant Professor, University of Michigan

Learning Human Preferences and Perceptions From Data

Robert Nowak, McFarland-Bascom Professor in Engineering, University of Wisconsin-Madison

Probablistic Topic Models and User Behavior

David Blei, Professor of Statistics and Computer Science, Columbia University

The Shape of Data

Jose Perea, Assistant Professor, Dept of Computational Mathematics, Science & Engineering, Michigan State University

From Model T to Waymo: Cars have gotten better – has transportation data science?

Carol A. Flannagan, PhD, Research Associate Professor, University of Michigan

Sequential change-point detection over dynamic networks

Yao Xie, PhD, Georgia Institute of Technology

Human-Centered Computing: Using Speech to Understand Behavior

Emily Mower Provost, PhD, Assistant Professor, University of Michigan

The Potential of On-Demand Mobility: Lessons from System Analysis and Data Visualization

Dimitris Papanikolaou, DDes, Postdoc Researcher, Harvard Graduate School of Design

On Data-Driven Creativity

Lav Varschney, PhD, University of Illinois at Urbana-Champaign

Lifelong Machine Learning

Bing Liu, Professor, University of Illinois, Chicago

Natural Language Processing for Collective Discourse

Dragomir Radev, Professor of Information, Computer Science and Engineering, University of Michigan

Structure and Function in Complex Networks

Mark Newman, Anatol Rapoport Distinguished University Professor of Physics, Professor in the Center for the Stud, University of Michigan

Michael Jordan – MIDAS Seminar

Michael Jordan, Pehong Chen Distinguished Professor in EECS, Professor of Statistics, University of California, Berkeley

Personalizing Education at Scale

Tim McKay, Ph.D., Arthur F Thurnau Professor of Physics, Astronomy, and Education, University of Michigan

Data in Motion Phenotyping: From the Intensive Care Unit to the Home

Kevin Ward, MD, Professor and Executive Director: MCIRCC, University of Michigan