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MIDAS Seminars

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Past Seminars

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

MIDAS Seminar | Women in Computing

Race-Aware Algorithms: Fairness, Nondiscrimination, and Affirmative Action

Pauline Kim, Daniel Noyes Professor of Law, Washington University in St. Louis, School of Law

4:00pm – 5:00pm in Remote/Virtual

Lecture by Yong Chen

Yong Chen, Associate Professor, Biostatistics, University of Pennsylvania

4:00pm – 5:00pm in Remote/Virtual

Distinguished Lecture | MIDAS Seminar | Women in Computing

Towards Usability, Transparency, and Trust for Data-Driven Discovery

Juliana Freire, Professor, Computer Science and Data Science, New York University

4:00pm – 5:00pm in 140 Lorch Hall

MIDAS Seminar | Women in Computing

Towards robust, interpretable, and fair social and public health interventions

Phebe Vayanos, WiSE Gabilan Assistant Professor of Industrial & Systems Engineering and Computer Science, University of Southern California

4:00pm – 5:00pm in Remote/Virtual

Explainability for Neural Text Classifiers

Tarun Joshi, Financial Services Director, Wells Fargo

4:00pm – 5:00pm in Remote/Virtual

Lecture by Mahlet (Milly) Zimeta

Mahlet (Milly) Zimeta, Head of Public Policy, Open Data Institute

4:00pm – 5:00pm in Remote/Virtual

MIDAS Seminar | Women in Computing

Supervised Machine Learning: Applications, Opportunities, and Challenges in Banking with a Focus on Interpretability

Jie Chen & Linwei Hu

4:00pm – 5:00pm in Remote/Virtual

AI for Sustainable Development

Stefano Ermon, Assistant Professor, Computer Science, Stanford University

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

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

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