From 2015–2022, MIDAS organized a weekly seminar series, which brought many prominent researchers to U-M as speakers and enabled research conversations both between these speakers and U-M researchers, as well as among U-M researchers. However, because of the nature of data science and AI research, the seminars cover a very wide range of topics and any particular topic is covered by a small number of seminars scattered throughout the year.
Starting in 2022, in order to intensify the research discussions and collaborations that such seminars initiate, MIDAS has pivoted to monthly mini-symposia. Organized in collaboration with our affiliate faculty members, these mini-symposia feature multiple speakers and focus on one research theme.
Past Seminars
- Sandra Gonzalez-Bailon, Associate Professor, Communications, University of Pennsylvania
- Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics, and…
- Sabina Leonelli, Professor, Philosophy and History of Science, University of Exeter
- Matthew Salganik, Professor, Sociology, Princeton University
- Yun Song, Professor, Computer Science & Statistics, UC Berkeley
- Yuri M. Zhukov & Ken Kollman. Frederick G. L. Huetwell Professor, Political Science
- Donglin Zeng. Professor, Biostatistics, University of North Carolina
- Clément Royer, Associate Professor, Computer Science, Université Paris Dauphine-PSL
- Jennifer Bobb, Kaiser Permanente Washington Health Research Institute
- Virginia Dignum, Professor, Wallenberg Chair in Responsible AI, Umeå University
- Enrico Di Minin, Professor, Geosciences and Geography, University of Helsinki
- Lia Corrales, Assistant Professor, Astronomy, University of Michigan
- Rebecca Hubbard, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
- Chris Lindsell, Biostatistics and Biomedical informatics, Vanderbilt University
- Katie Bouman, Rosenberg Scholar, Assistant Professor, Computing and Mathematical Sciences, Caltech
- Pauline Kim, Daniel Noyes Professor of Law, Washington University in St. Louis, School of Law
- Yong Chen, Associate Professor, Biostatistics, University of Pennsylvania Director, Computing, Inference, and Learning Lab, University of Pennsylvania Senior Fellow, Institute for Biomedical Informatics, University of Pennsylvania
- Juliana Freire, Professor, Computer Science and Engineering, NYU
- Ben Green & Jeff Sheng, Postdoctoral Scholar – Michigan Society of Fellows, Assistant Professor – Ford School of Public Policy & Assistant Professor – School of Information; Postdoctoral Scholar – Michigan Society of Fellows, Assistant Professor – School of Information
- Phebe Vayanos, Towards robust, interpretable, and fair social and public health interventions
- Tarun Joshi, SHAP Values for Explaining CNN Text Classifiers
- Mahlet (Milly) Zimeta, Knowns and Unknowns: Data and Metaphor
- Linwei Hu, Supervised Machine Learning: Applications, Opportunities, and Challenges in Banking with a Focus on Interpretability
- Jie Chen, Supervised Machine Learning: Applications, Opportunities, and Challenges in Banking with a Focus on Interpretability
- Stefano Ermon, AI for Sustainable Development
- Greg Asner, Large-scale Mapping of Corals to Guide Reef Conservation
- Olga Russakovsky, Fairness in Visual Recognition: Redesigning the Datasets, Improving the Models and Diversifying the AI Leadership
- Ben Wellington, Shaping a City with Open Data
- Vicki Bogan, Financial Decision-Making and Mental Health
- Vipin Kumar, Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery
- Anne Plant, The Role of Measurement Science in Advancing Data Reliability
- Mona Diab, Faithfulness in Natural Language Generation in an Era of Heightened Ethical Al Awareness: Opportunities for MT
- Heng Ji, Knowledge Extraction to Accelerate Scientific Discovery
- Simine Vazire, Evaluating research on its own merits
- Casey Greene, Machine learning can help to realize the bounty of the commons
- Misty Heggeness, Estimating the immediate impact of the COVID-19 shock on parental attachment to the labor market and the double bind of mothers
- Arya Farahi, KiTE: A framework for algorithmic trustworthiness
- Timnit Gebru, Computer Vision: Who is Helped and Who is Harmed?
- Stan Ahalt, The Changing Landscape of Biomedical Data Collections
- Eric Xing, From Performance-Oriented AI to Production- and Industrial- AI
- Xiaoqian Jiang, Genomic Data Sharing: The Privacy Risk and Technical Mitigations
- Stuart Soroka, Tracking the ‘Mood’ of U.S. Social Media Coverage, 1990-2020
- Paul Bennett, Robust and Transparent AI in Search and Recommendation
- Eric Horvitz, Fireside Chat
- Lauren Klein, Data Feminism
- Catherine D’Ignazio, Data Feminism
- Christo Wilson, Auditing for Bias in Resume Search Engines
- Thorsten Joachims, Fair Ranking with Biased Data
- Cristian Danescu-Niculescu-Mizil, Towards an artificial intuition: Conversational markers of (anti)social dynamics
- Ivana Seric, The Evolution of Basketball with Data Science
- Christopher Re, Theory and Systems for Weak Supervision
- David Shor, Data Science in Politics,
- Ryan Adams, Some New Ideas for Unbiased Gradient Estimation in Optimization
- Kentaro Toyama, Beyond Algorithmic Fairness: What Social Justice Needs From Data Science
- Daniel Forger, A mobile/wearable platform for collecting data on sleep and circadian rhythms in the time of COVID
- Wayne Wang, Al Hero, & Walter Dempsey, A scalable tool for longitudinal Twitter analysis: understanding the impact of COVID-19 on public discourse
- J. Scott Van Epps, The perils and promise of machine learning for life-threatening infection
- Holly Hartman & Jack Griffin, Using Data for Good in the Age of COVID-19
- Rada Mihalcea, Life during COVID-19: a Billion-word Story as Told by a Natural Language Processing Researcher
- Veera Baladandayuthapani & Zhenke Wu, Understanding COVID-19 Dynamics via Individual-level Temporal and Network Modeling: Lessons from India and China
- Karandeep Singh, Validating a Widely Implemented Deterioration Index Model Among Hospitalized COVID-19 Patients
- Jon Zelner, If the outbreak ended, does that mean the interventions worked?
- Bhramar Mukherjee, Predictions, Role of Interventions and the Crisis of Virus in India: Data Science Call to Arms
- Siqian Shen, From Data to Actions, From Observations to Solutions — A Summary of Operations Research and Industrial Engineering Tools for Fighting COVID-19
- Nicholas Diakopoulos, The Role of Algorithmic Intermediaries in Shaping Attention to News
- Luonan Chen, Criticality detection and time-series prediction from small samples by dynamics-based model-free approaches
- Benjamin Lauderdale, Model-Based Pre-Election Polling for National and Sub-National Outcomes
- Qianying Lin, COVID-19 outbreak in Wuhan, China: in retrospect and in prospect
- Jen Stirrup, Is it possible to democratize AI for businesses?
- Julia Stoyanovich, Follow the Data! Responsible Data Science Starts with Responsible Data Management
- Edward Stabler, Adversarial Training for Semantic Parsing
- Charles Yang, How Language Makes Us Smart (Without Big Data)
- Animashree Anandkumar, Infusing Structure into Machine Learning Algorithms
- Ricardo Baeza-Yates, Bias in Search and Recommender Systems
- Michael Betancourt, Scalable Bayesian Inference with Hamiltonian Monte Carlo
- Claire Cardie, Information Extraction from Online Text — from Opinions to Arguments to Persuasion
- David Dunson, Arts & Sciences Distinguished Professor of Statistical Science & Mathematics
- Arya Farahi, A Quest for the Dark Sector of the Universe: The Role of Galaxy Clusters in the Era of Precision Cosmology
- Justin Grimmer, How to Make Causal Inferences Using Texts
- Erin O’Brien Kaleba, The Precision Health Analytics Platform: data, analytics, and resources for your research
- Smita Krishnaswamy, Learning Representations for Enabling Insight Into Big Biomedical Data
- Alex Leow, Making Connections: Data Science Approaches to Understanding Mood and Cognition in the Modern Era
- Jure Leskovec, How Powerful are Graph Neural Networks?
- Jun Li, Benchmarking at scale: comparing analysis workflows for single-cell genomic data
- Qianying Lin, Integration of Epidemic Time Series and Genetic Sequences
- Chris Miller, Quantify Systematics from Mislabeled Truth Tables in Supervised Learning
- Josh Pasek, What Can Tweets Tell Us About Public Opinions? Uncovering the Data Generating Process by Linking Twitter Data with Surveys
- Patrick Park, The Strength of Long-Range Ties
- Gerald Quon, Quantifying cell type-specific changes in transcriptional state and gene co-regulation across multiple datasets using scRNA-seq
- Mark Robinson, Statistical Methods for Flexible Differential Analysis of Cross-sample Single-cell RNA-seq Datasets
- Elyas Sabeti, Predictive Analytics in Healthcare: From Anomaly Detection to Development of Clinical Decision Support Systems
- Gil Shamir, Leveraging Information Theory to Practical Machine Learning: Minimum Description Length Regularization for Online Learning
- Cinzia Villanucci Smothers, The Precision Health Analytics Platform: data, analytics, and resources for your research
- Arthur Spirling, Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research
- Sean Taylor, Causal Modeling with Many Experiments
- Pei Wang, Constructing tumor-specific gene regulatory networks based on samples with tumor purity heterogeneity
- Chris Wiggins, Data Science at The New York Times
- Bin Yu, PCS framework, interpretable machine learning, and deep neural networks
- Ji Zhu, Matrix Completion in Network Analysis
- Adriene Beltz, Person Specific Temporal Networks: Accuracy, Dynamics, and Emojis 😕
- Tamara Broderick, Automated Scalable Bayesian Inference Via Data Summarization
- Chris Callison-Burch, The Promise of Crowdsourcing for Natural Language Processing and Other Data Sciences
- Matias D. Cattaneo, Two-Step Estimation and Inference with Possibly Many Included Covariates
- Yang Chen, Determine the Number of States in Hidden Markov Models via Marginal Likelihood
- Ivo D. Dinov, The Enigmatic Kime: Time Complexity in Data Science
- Ioana Dumitriu, Larger, Faster, Random(ized): Computing in the Era of Big Data
- Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence
- Robert Goldstone, Baseball Umpire Calls as a Naturally Occurring Data Source for Revealing Principles of Bias and Learning in Perceptual Judgments
- Ihab F. Ilyas, Building Scalable Machine Learning Solutions for Data Curation (Recording for U-M Affiliates only)
- John P.A. Ioannidis, The Power of Bias and What To Do About It
- Frank Kargl, Protecting Security and Privacy of Connected & Automated Cars
- Christ D. Richmond, Parameter Bounds Under Misspecified Models and Some Perspectives for Data Science and Learning
- Marc A. Suchard, Reliable Evidence from Health Care Data: Lessons from the Observational Health Data Sciences and Informatics (OHDSI) Collaborative
- Jonathan Terhorst, New Methods for Detecting Natural Selection in Large Samples of Genetic Data
- Candace Thille, Technology, The Science of Learning, and Transformation in Higher Education
- Lieven Vandenberghe, Sparsity and Decomposition in Semidefinite Optimization
- Pascal Van Hentenryck, Data and Decision Science for Mobility Services
- Lav Varshney, Information-Theoretic Approaches to Neural Network Compression, Clustering and Concept Learning
- Pramod Viswanath, Geometries of Word Embeddings
- Jenna Wiens, Increasing the Utility of Machine Learning in Clinical Care
- Arya Farahi, A Quest for the Dark Sector of the Universe: The Role of Galaxy Clusters in the Era of Precision Cosmology
- Jeannette Wing, Data Science at Columbia University
- Kevin Xu, Statistical Models for Analyzing Dynamic Social Network Data
- Laura Balzanom, “Finding Low-Rank Structure in Messy Data”
- David Blei, Probabilistic Topic Models and User Behavior
- Yoram Bresler, Sparse Signal Recovery in Bilinear Inverse Problems
- Tianxi Cai, Efficient Use of EHR for Biomedical Translational Research
- Michael Cavaretta, Data and Analytics: Emerging trends and opportunities at Ford Motor Company
- Carol Flannagan, From Model T to Waymo: Cars have gotten better—has transportation data science?
- Rob Goodman, A Mind at Play: How Claude Shannon Invented the Information Age
- Margaret Hedstrom, Why Data Science Needs Curation and Why Curation Needs Data Science
- Tony Jebara, Probabilistic Graphical Models and Online Learning
- Danai Koutra, Inferring, Summarizing and Mining Large-scale Graph Data
- Emily Mower Provost, Human-Centered Computing: Using Speech to Understand Behavior
- Matthew Nokleby, Bits through Sensors: Bounds on Classification and Learning Performance via Information Content
- Rob Nowak, Learning Human Preferences and Perceptions From Data
- Dimitris Papanikolaou, The Potential of On-Demand Mobility: Lessons from System Analysis and Data Visualization
- Jose Perea, The Shape of Data
- Christopher J. Rozell, Closing the Loop Between Mind and Machine: Building Algorithms to Interface with Brains at Multiple Scales
- Jimmy Soni, A Mind at Play: How Claude Shannon Invented the Information Age
- Lav Varshney, On Data-Driven Creativity
- Yao Xie, Sequential Change-point Detection over Dynamic Networks
- Goncalo Abecasis, Sequencing 10,000’s of Human Genomes: Early Results, Opportunities and Challenges
- Jacob Abernethy, Statistical and Algorithmic Tools to Aid Recovery in Flint
- Yuejie Chi, Solving Corrupted Systems of Quadratic Equations, Provably
- Geoff Ginsburg, Novel Genomic Paradigms for Early Detection and Diagnosis of Acute Infectious Disease
- Michael Jordan, On Computational Thinking, Inferential Thinking and Data Science
- Gary King, Big Data is Not About the Data!
- Tamara Kolda, An Overview of Tensor Decompositions for Data Analysis,with Emphasis on Computation and Scalability
- Bing Liu, Lifelong Machine Learning
- Tim McKay, Personalizing Education at Scale
- Susan Murphy, Learning Treatment Policies in Mobile Health
- Mark Newman, Structure and Function in Complex Networks
- Mesrob I. Ohannessian, Computation-Statistics Tradeoffs in Unsupervised Learning via Data Summarization
- Sandy Pentland, Social Physics: Harnessing Big Data and Machine Learning in Support of Human Goals
- Dragomir Radev, Natural Language Processing for Collective Discourse
- Christopher Ré, DeepDive: A Dark Data System
- Perry Samson, Learning Analytics: Mining Student Behaviors During Class to Predict Exam Scores
- Amit Surana, Koopman Operator Theoretic Framework for Dynamic Data Analytics
- Kevin Ward, Data in Motion Phenotyping: From the Intensive Care Unit to the Home
- Rebecca Willett, Estimating High-Dimensional Autoregressive Point Processes
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