Previous Seminars

2021

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