Future Leaders Summit

The MIDAS annual Future Leaders Summit offers outstanding graduate students, postdocs, and early-career faculty from around the US the opportunity to engage in research discussions with peers and with research leaders, and receive career mentoring, as they grow to become leaders in data science and artificial intelligence (AI) research.

2024 Future Leaders Summit

Time and Location: April 8-10, 2024 on the University of Michigan Ann Arbor campus.

Theme: “Responsible data science and AI.”

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Faculty Mentors

Brittany Aguilar

Science Associate
Schmidt Sciences

Elizabeth Bondi-Kelly

Visiting Assistant Professor of Electrical Engineering and Computer Science, College of Engineering
University of Michigan

Bill Currie

Associate Dean, Research and Engagement, Professor of Environment and Sustainability, School for Environment and Sustainability

Jamal El-Hindi

Former U.S. Treasury Financial Crimes Enforcement Network (FinCEN) Deputy Director
Clifford Chance

Arya Farahi

Assistant Professor, Dept of Statistics and Data Science
University of Texas, Austin

Kent Foster

Director, Innovation + Society
Microsoft

R. Stuart Geiger

Assistant Professor, Dept of Communication and the Halıcıoğlu Data Science Institute
Affiliate Faculty, Institute for Practical Ethics,
Computer Science & Engineering, and Computational Social Science,
University of California, San Diego

Min Kyung Lee

Assistant Professor, School of Information
University of Texas, Austin

Michael Tjalve

Chief AI Architect, Tech for Social Impact at Microsoft Philanthropies
Assistant Professor, Linguistics
University of Washington

Elizabeth Yakel

Olivia Frost Collegiate Professor of Information, School of Information;
Faculty Associate, Inter-University Consortium for Political and Social Research, Institute for Social Research
University of Michigan

The 2024 Future Leaders Summit is sponsored by

2023 Future Leaders Summit

Time and Location: April 12-14, 2023 on the University of Michigan Ann Arbor campus.

Theme: “Responsible data science and AI.”

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Faculty Mentors

Tanya Berger-Wolf

Director of the Translational Data Analytics Institute,
The Ohio State University

Andrew J. Connolly

Visiting Assistant Professor of Electrical Engineering and Computer Science, College of Engineering
University of Michigan

H. V. Jagadish

Professor of Computer Science and Engineering, MIDAS Director
University of Michigan

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Mark Moldwin

Professor, Climate and Space Sciences and Engineering
University of Michigan

Josh Pasek

Associate Professor of Communication and Media & Political Science, MIDAS Associate Director,
University of Michigan

Ellie Sakhaee

Office of Responsible AI
Microsoft

The 2023 Future Leaders Summit is sponsored by

Microsoft

Rocket Companies

2022 Future Leaders Summit

Time and Location: April 6-7, 2022 on the University of Michigan Ann Arbor campus.

Theme: “Responsible data science and AI.”

Article: MIDAS Future Leaders Summit highlights Responsible Data Science and AI

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Faculty Mentors

H. V. Jagadish

Professor of Computer Science and Engineering, MIDAS Director
University of Michigan

Frauke Kreuter

Professor of Survey Methodology, Director of Social Data Science Center
University of Maryland

David Mongeau

Director, School of Data Science
Professor of Practice
University of Texas at San Antonio

Shashi Shekhar

Professor of Computer Science
University of Minnesota

Michael Wellman

Professor and Chair of Computer Science and Engineering
University of Michigan

The 2022 Future Leaders Summit is sponsored by

Michigan DEI Office

Microsoft

Rocket Companies

2020 Data Science Consortium

The 2020 cohort comes from 28 universities, and will meet virtually on Oct. 29th-30th to participate in research talk presentations, networking sessions, and mentoring opportunities.

The research talk presentations will take place from 12:00pm – 2:00pm EST each day and are open to the public.

Mentoring session speakers:

Dr. Jeffrey Fessler, Professor of the Department of Electrical and Computer Engineering, University of Michigan
Dr. Margaret Levenstein, Director of the Inter-university Consortium for Political and Social Research, University of Michigan
Dr. Kevin Ward, Professor of Emergency Medicine, University of Michigan
Dr. Michael Wellman, Professor and Chair of the Department of Computer Science and Engineering, University of Michigan
Dr. Marybeth Broadbent, Data Scientist at Google

October 30th Schedule

12:00pm – 12:10pm: Opening remarks, Jing Liu (Managing Director, MIDAS)

Presentations

Data Science and the Physical World

Time	Name	University	Title of Presentation
12:10pm – 12:25pm	Karianne Bergen	Harvard University	Shaking up Earthquake Science in the Age of Big Data
12:25pm – 12:40pm	Roshan Kulkarni	Iowa State University	Modeling for ambiguous SNP calls in allotetraploids
12:40pm – 12:55pm	Chris Powell	Oakland University	PATS: a taxon re-sampling pipeline to test phylogenetic stability in large datasets
12:55pm – 1:10pm	Sameer	Penn State University	Unveiling the nature of the Circumgalactic medium
1:10pm – 1:25pm	Yan Li	University of Minnesota	Physics-guided Energy-efficient Path Selection
1:25pm – 1:40pm	Elham Taghizadeh	Wayne State University	Framework for Effective Resilience Assessment of Deep-Tier Automotive Supply Networks

Data Science and Human Society

Time	Name	University	Title of Presentation
12:10pm – 12:25pm	Aviv Landau	Columbia University	Artificial Intelligence-Assisted Identification of Child Abuse and Neglect in Hospital Settings with Implications for Bias Reduction and Future Interventions
12:25pm – 12:40pm	Thibaut Horel	Massachusetts Institute of Technology	The Contagiousness of Police Violence
12:40pm – 12:55pm	Chris Ick	New York University	Robust Sound Event Detection in Urban Environments
12:55pm – 1:10pm	Renhao Cui	Ohio State University	Restricted Paraphrase Generation Model for Commercial Tweets
1:10pm – 1:25pm	Rezvanah Rezapour	University of Illinois at Urbana-Champaign	Text Mining for Social Good; Context-aware Measurement of Social Impact and Effects Using Natural Language Processing

October 30th Schedule

12:00pm – 12:10pm: Welcome back, Jing Liu (Managing Director, MIDAS)

Presentations

Data Science Theory and Methodology

Time	Name	University	Title of Presentation
12:10pm – 12:25pm	Paidamoyo Chapfuwa	Duke University	Bringing modern machine learning to survival analysis
12:25pm – 12:40pm	Heakyu Park	Georgia Institute of Technology	Bluff: Interactive Interpretation of Adversarial Attacks on Deep Learning
12:40pm – 12:55pm	Tanima Chaterjee	University of Illinois at Chicago	On the Computational Complexities of Three Privacy Measures for Large Networks Under Active Attack
12:55pm – 1:10pm	Behnaz Moradi	University of Virginia	Introducing retraced non-backtracking random walk (RNBRW) as a tool to uncover the mesoscopic structure of open-source software (OSS) networks
1:10pm – 1:25pm	Sanjeev Kaushik	Florida International University	Securing the IoT Communication
1:25pm – 1:40pm	Arya Farahi	University of Michigan	Towards Trustworthy and Fair Classifiers
1:40pm – 1:55pm	Qi Zhao	University of California, San Diego	Persistence Enhanced Graph Neural Network

Data Science and Human Health

Time	Name	University	Title of Presentation
12:10pm – 12:25pm	Sarah Ben Mamaar	Northwestern University	Comprehensive analysis of the reproducibility of RNAseq computational pipelines
12:25pm – 12:40pm	Váleri Vásquez	University of California, Berkeley	Optimizing Genetic-Based Public Health Interventions
12:40pm – 12:55pm	Abby Stevens	University of Chicago	Modeling the Impact of Social Determinants of Health on COVID-19 Transmission and Mortality to Understand Health Inequities
12:55pm – 1:10pm	Sean Kent	University of Wisconsin	Multiple Instance Learning from Distributional Instances
1:10pm – 1:25pm	LaKeithia Glover	Clark Atlanta University	Suicide Rates on African African Youth as it relates to the Influence of Different types of Communication
1:35-1:40pm	Matt Satusky	University of North Carolina at Chapel Hill	BioData Catalyst and Deep Learning: Feature extraction on a full-feature platform
1:40pm – 1:55pm	Juandalyn Burke	University of Washington	Using an Ecological Inference Software Tool to Detect Vote Dilution

Other Attendees

Name	University
Sandrine Muller	Columbia University
Jonathan Proctor	Harvard University
Etienne Nzabarushimana	Indiana University
Kate Mortensen	Indiana University
Zhouhan Chen	New York University
Ashley Superson	Oakland University
Alex Aguilar	Rice University
Adam Anderson	University of California, Berkeley
Like Hui	University of California, San Diego
Elena Graetz	University of Illinois at Chicago
Jayant Gupta	University of Minnesota
Aji John	University of Washington

The 2020 Data Science Consortium is sponsored by

Google Cloud

2019 Data Science Consortium

Name	University	Department	Abstract Title
Dhivya Eswaran	Carnegie Mellon University	Computer Science	“SedanSpot: Detecting Anomalies in Edge Streams”
Otilia Stretcu	Carnegie Mellon University	Machine Learning	“Graph Agreement Models for Semi-Supervised Learning”
William Dula	Clark Atlanta University	Mathematical Sciences	“Application of a Contour Theory Method to Load Profiling in a Power Utility”
Ipek Ensar	Columbia University	Data Science Institute	“Exercise as medicine: Can exercise behavior predict endometriosis disease symptom severity?”
Sandrine Muller	Columbia University	Data Science Institute	“Everyday Mobility Behaviors Predict Psychological Well-Being Among Young Adults”
Marko Angjelichinoski	Duke University	Electrical and Computer Engineering	“Robust Estimation and Classification for Brain-Computer Interfaces”
Didong Li	Duke University	Mathematics	“Density Estimation on Manifolds with Fisher-Gaussian Kernels”
Robert Ravier	Duke University	Electrical Engineering	“Foundational Issues in Computational Evolutionary Biology and Political Science”
Shan Shan	Duke University	Mathematics	“Probabilistic Models on Fibre Bundles”
Daniel Ruiz-Perez	Florida International University	Computing and Information Sciences	“Predicting microbe-microbe interactions from time series metagenomic data”
Thinh Doan	Georgia Institute of Technology	Electrical and Computer Engineering & Industrial and Systems Engineering	“Distributed Decision Making on Multi-Agent Systems”
Michelle Ntampaka	Harvard University	Harvard Data Science Initiative	“A Deep Learning Approach to Galaxy Cluster X-ray Masses”
Dakota Murray	Indiana University Bloomington	Informatics	“Making sense of scientific systems”
Lanmiao He	Iowa State University	Psychology	“Being aware helps: how mindfulness and data analytics benefit students and an educational NPO”
Gianina Alin Negoita	Iowa State University	Animal Science	“Face2Face: Facial Expression Transfer”
Tuhin Sarkar	Massachusetts Institute of Technology	Electrical Engineering	“Learning Structure from Unstructured Data”
Anastasios Noulas	New York University	Center for Data Science	“Wiki-Atlas: Rendering Wikipedia Content through Cartographic and Augmented Reality Mediums”
Nan Wu	New York University	Center for Data Science	“Towards solving breast cancer screening diagnosis with deep learning”
Asmamaw Gebrehiwot	North Carolina A&T State University	Applied Science and Technology	“Flood Extent Mapping using Unmanned Aerial Vehicle Imagery”
Luiz G. A. Alves	Northwestern University	Chemical & Biological Engineering	“Reconstructing commuters network using machine learning and urban indicators”
Yian Yin	Northwestern University	Industrial Engineering and Management Science	“Quantifying dynamics of failure across science, startups, and security”
Lige Gan	Oakland University	Computer Science and Engineering	“Attention-based Multi-Source Representation Learning”
Arunima Srivastava	The Ohio State University	Computer Science and Engineering	“Interpretable and Context Based Neural Network Modeling of Histology Images”
Andrew Polasky	Penn State	Meteorology and Atmospheric Science	“Statistical Downscaling of Climate Models using self-organizing maps”
Yi-Yu Lai	Purdue University	Computer Science	“Relational Representation Learning Incorporating Textual Communication for Social Networks”
Ye Emma Zohner	Rice University	Statistics	“Bayesian Functional Regression on Manifold via Spherical Wavelets”
Baharan Mirzasoleiman	Stanford University	Computer Science	“Large-scale Combinatorial Optimization for Data Summarization and Resource-efficient Machine Learning”
Stuart Geiger	UC-Berkeley	Berkeley Institute for Data Science	“Operationalizing Conflict and Cooperation Between Automated Software Agents in Wikipedia: A Replication, Refutation, and Expansion of ‘Even Good Bots Fight.’”
Yang Junwen	University of Chicago	Computer Science	“Improving Performance and Quality of Database-Backed Software”
Yushen Dong	University of Illinois at Chicago	Mathematics, Statistics, and Computer Science	“Nonparametric interaction selection for additive model”
Oluwagbemiga Ajayi	University of Maryland Baltimore County	Information Systems	“Diagnosis Of Type II Diabetes Using Machine Learning Techniques”
Ronak Razavisousan	University of Maryland Baltimore County	Information Systems	“Two Layer Clustering Analysis of Student Migration from Twitter Posts”
Srinivas Rallapalli	University of Minnesota	Bioproducts and Biosystems Engineering	“Developing an optimized decision support framework for sustainable field-scale assessment by integrating ACPF, PTMApp, and HSPF-SAM”
Tony Cannistra	University of Washington	Biology	“Data Science Methods and Climate Change Ecology: A Scientific Imperative”
Erin Wilson	University of Washington	Computer Science and Engineering	“Using microorganisms to solve macro problems: untangling the genetic circuitry of methane-eating bacteria”
Mina Karzand	UW Madison	Wisconsin Institute for Discovery	“Active Learning in the Overparameterized and Interpolating Regime”
Najibesadat Sadati	Wayne State University	Industrial and Systems Engineering	“Dynamic Resource Allocation for Coordination of Inpatient Operations in Hospitals”