MIDAS organizes the annual Future Leaders Summit (previously known as the Data Science Consortium). The aim is to build a network for these students and postdoctoral fellows, help them to receive feedback about their research, and nurture them to become the next generation of academic leaders in data science.

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.”

More event information

Faculty Mentors:

Tanya Berger-Wolf

Tanya Berger-Wolf

Director of the Translational Data Analytics Institute

The Ohio State University

Andrew Connolly

Andrew J. Connolly

Director of the eScience Institute

University of Washington

H. V. Jagadish

Professor of Computer Science and Engineering, MIDAS Director

University of Michigan

Mark Moldwin

Professor, Climate and Space Sciences and Engineering

University of Michigan

Josh Pasek

Josh Pasek

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

University of Michigan

Ellie Sakhaee

Ellie Sakhaee

Office of Responsible AI


The 2023 Future Leaders Summit is sponsored by

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

More event information

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

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:

Full 2020 Cohort, Schedule and Zoom Links

October 29th Schedule

12:00pm – 12:10pm: Opening remarks, Jing Liu (Managing Director, MIDAS) [Join via Zoom]


Data Science and the Physical World [Join via Zoom]

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 [Join via Zoom]

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) [Join via Zoom]


Data Science Theory and Methodology [Join via Zoom]

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 [Join via Zoom]

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 Consortium is sponsored by

2019 Data Science Consortium

See Full 2019 Cohort

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”