Day 1 – Monday, November 14
9:00 AM – 9:10 AM
Opening Remarks
Dr. H. V. Jagadish, Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Computer Science and Engineering; MIDAS Director
View Recording View Slides9:10 AM – 10:30 AM
Keynote Speaker – Dr. Suzanne Bakken, Professor of Biomedical Informatics and Alumni Professor of the School of Nursing, Columbia University; Editor-in-Chief, Journal of the American Medical Informatics Association
Advancing Discovery and Application in Health Data Science: An Editor’s Perspective
10:30 AM – 12:20 PM
Program Kick-Off – Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Futures Program
This new campus-wide program will train 60 postdocs in the next six years and, with the postdoc program at the core, build momentum for the campus research community and external collaborators to use AI methods for breakthroughs in science and engineering research.
AI Science Talks
Program Introduction
Presented by H.V Jagadish
Designing Robust Macine Learning Classifiers
Presented by Atul Prakas
Data Science in Space Weather Forecasting
Presented by Yang Chen
From AI to ET: Image Processing, Spectral Modeling, and Population Demographics to Study Planets Around Other Stars
Presented by Michael Meyer
The AI Forest: Using Deep Learning to Track Wild
Presented by Jacinta Beehner
Uncertainty and Decisions: Tools for Bayesian Inference and Uncertainty Quantification in Science
Presented by Alex Gorodetsky
Integration of Artificial Intelligence in Manufacturing Systems
Presented by Kira Barton
Curriculum and Reinforcement Learning for Molecular Conformer Sampling
Presented by Paul Zimmerman & Ambuj Tewari
Deep Learning at the Edge of the Solar System
Presented by David Gerdes
Digital Twin Collaboration
Presented by Eunshin Byon
Data Analytics for the Internet of Things
Presented by Judy Jin & Raed Al Kontar
Summary and Closing Remarks
Presented by William Currie
12:20 PM – 2:00 PM
Lunch and Poster Session
Showcasing a wide range of data science and AI research projects and grassroots data science and AI organizations.
2:00 PM – 4:00 PM
Research Vision Talks – Session 1
Interdisciplinary Research on Suicide Risk: Bridging the Distance Between Data and Inference
Presented by Briana Mezuk
Personalized Treatment Assignment Rules for Vaccine Uptake in Behavioral Science Field Experiments with Large Multi-Arm Trials
Presented by Rahul Ladhania
Development of Understandable Artificial Intelligence (UAI) Methods in Physical Sciences
Presented by Y Z (Yang Zhang)
Data Science Without Data Collection Using FedScale
Presented by Mosharaf Chowdhury
Apply AI to Ionosphere Space Weather Specification and Forecast
Presented by Shasha Zou
4:00 PM – 5:30 PM
Showcase of U-M data science and AI research organizations
Michigan AI Lab
Rada Mihalcea, Janice M Jenkins Collegiate Professor of Computer Science and Engineering and Professor of Electrical Engineering and Computer Science
E-Health and Artificial Intelligence
Akbar Waljee, Professor of Internal Medicine, Medical School
Henrike Florusbosch, Program Manager, Medical School
University of Michigan Software and Data Carpentries
Patrick Schloss, Program Director of Microbiology and Immunology AP&A, Frederick G Novy Collegiate Professor of Microbiome Research and Professor of Microbiology and Immunology, Medical School
Michigan Institute for Computational Discovery and Engineering
Krishna Garikipati, Professor of Mechanical Engineering, Center Director, Michigan Institute for Computational Discovery and Engineering Research, and Professor of Mathematics
Karthik Duraisamy, Professor of Aerospace Engineering
University of Michigan Precision Health
Sebastian Zoellner, John G Searle Associate Professor of Biostatistics, Professor of Biostatistics, and Professor of Psychiatry, Medical School
Consulting for Statistics, Computing and Analytics Research
Kerby Shedden, Professor of Statistics, Professor of Biostatistics, and Center Director, Statistical Consultation and Research
Bold Challenges
Dawn Tilbury, Associate Vice President for Research-Convergence Science, Ronald D and Regina C McNeil Department Chair of Robotics, Herrick Professor of Engineering, Professor of Robotics, Professor of Mechanical Engineering, and Professor of Electrical Engineering and Computer Science
Arthur Lupia, Gerald R Ford Distinguished University Professor of Political Science, Professor of Political Science, Research Professor, Center for Political Studies, Institute for Social Research and Center Director, UMOR Office of the Vice President for Research
Center for Ethics, Society, and Computing
Sophia Brueckner, Associate Professor of Art and Design, Associate Professor of Information, and Associate Professor of Digital Studies Institute
Digital Studies Institute
Germaine Halegoua, John D Evans Development Professor, Associate Professor of Communication and Media, Associate Professor in the Digital Studies Institute and Director Graduate Studies, Digital Studies Institute
Science, Technology and Public Policy Program
Molly Kleinman, Assistant Director, Ford School of Public Policy
5:30 PM – 6:45 PM
Reception and Networking
All attendees are invited. Come and talk with U-M data science and AI organizations, researchers, and students, as well as industry and public-sector partners.
Day 2 – Tuesday, November 15
9:00 AM – 10:00 AM
Keynote Speaker – David Shor, Head of Data Science, OpenLabs R&D
Data Science and American Politics
10:00 AM – 1135 AM
Research Vision Talks – Session 2
Using Machine Learning to Construct Hedonic Price Indices
Presented by Matthew D. Shapiro
Use AI to Facilitate Emotional Intelligence in Remote Work
Presented by Xuan Lu
Societal Biases in Fairy Tales Across Cultures
Presented by Winston Wu
Improving Students’ Ability to Engage with Scholarly STEM Literature via Effective Reading Practices and Novel Machine Learning-Based Tools
Presented by Kevyn Collins-Thompson
11:35 AM – 12:35 PM
Propelling Original Data Science (PODS) Grant Awards Showcase
Each year, MIDAS funds a number of innovative and high-impact data science and AI research projects. The project teams will give the audience an overview of their work.
The MIDAS Propelling Original Data Science (PODS) grant strongly encourages works that transform research domains through data science and AI, works that improve the reproducibility of research, and works that promise major impact and potential for significant expansion.
AI-Based Author Entity Disambiguation for Promoting Fair Evaluation of Women in Science
Presented by Jinseok Kim
Unlocking the Vault: Machine Learning Methods for the Mobilization of Data from Millions of Plant Images
Presented by William Weaver
Developing Language-based Tools For Real-Time Counseling Feedback
Veronica Perez-Rosas, Kenneth Resnicow, and Rada Mihalcea
Building a Genomic Literature Knowledgebase
Presented by Jie Liu
Combating and Predicting Drug Resistance using a Hybrid Mechanistic Machine Learning Model
Presented by Margaret Reuter
Improving Cardiovascular Disease Detection with a Novel Multi-label Classifier for Electrocardiograms: Capturing Label Uncertainty and Complex Hierarchical Relationships between Output Classes
Presented by Sardar Ansari
A Machine-Learning Approach to Reduce Uncertainty in Climate Forcing by Aerosols
Presented by Joyce Penner
Developing a Large-Scale Dataset to Track Romantic Relationship Formation and Maintenance
Presented by Amie Gordon
Sustainability Outcomes of Restrictions on Human Actions: COVID-19 Mobility Changes, Forest Fires and Air Pollution across Land Regimes
Arun Agrawal, Ines Ibanez, and Yang Chen
Machine Learning Guided Co-design for Reconstructive Spectroscopy
Presented by Qing Qu
12:35 PM – 12:40 PM
Poster Awards Announcement
Posters with students as first authors are entered automatically in the poster competition for cash awards
12:40 – 12:45 PM
Closing Remarks
Dr. H. V. Jagadish, Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Computer Science and Engineering; MIDAS Director
12:45 PM – 2:00 PM
Light Lunch and Networking
All attendees invited
Questions? Contact Us.
Message the MIDAS team: [email protected]