Celebrating outstanding early-career scholars shaping the future of statistics and data science.

The Department of Statistics and Data Science at Cornell University is proud to launch the inaugural Rising Stars in Statistics and Data Science workshop Friday, September 18, 2026. The annual workshop brings together a select group of exceptional early-career scholars from multiple institutions whose work is at the forefront of statistics, data science, and related areas.

Through research talks, discussion, and community-building activities, this invitation-only event will spotlight emerging leaders while fostering new connections across the broader statistics and data science community. 
 

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About the Workshop

Rising Stars in Statistics and Data Science is an invitation-based workshop for outstanding early-career researchers. The program is designed to recognize emerging scholars, amplify their work, and create meaningful opportunities for connection across institutions and research areas.

Participants will share their research, engage in discussion with Cornell faculty and fellow invitees, and take part in activities intended to build community and encourage future collaboration.

The workshop reflects Cornell’s commitment to advancing excellence in statistics and data science and to supporting the scholars who will help define the field in the years ahead.
 

Why Attend?

  • Present research in a highly engaged academic setting
  • Connect with a select cohort of rising scholars from across the field
  • Build relationships with Cornell faculty and peer researchers
  • Explore new ideas across statistics, machine learning, and data science
  • Take part in a workshop designed around both research excellence and community

Register to attend


Program Highlights

Over three days, participants will take part in a program that combines research exchange with community-building.

Thursday, September 17
Arrival

Friday, September 18
Research talks, poster session, and faculty engagement

Saturday, September 19
Community activities and departure

The program is designed to balance intellectual exchange with opportunities for conversation, mentorship, and connection.

View full program


Participation

Participation in Rising Stars in Statistics and Data Science is by invitation. Invited participants will be selected by the workshop organizers and review committee based on the strength, originality, and breadth of their research contributions.

All invited participants will have the opportunity to take part in the workshop. Presentation formats will be determined by the committee, which will select a subset of participants for research talks and others for poster presentations.

Invited speakers:

Yang Cao
Postdoctoral Associate, Department of Statistics and Data Science, Yale University
High-dimensional statistical inference; multiple testing and knockoffs; single-cell and spatial multi-omics

Margalit Glasgow
NSF Postdoctoral Fellow, MIT
Theory of neural networks; mean-field and feature-learning dynamics; distributed optimization; random graphs

Adam Q. Jaffe
Postdoctoral Research Scientist, Department of Statistics, Columbia University
Probability theory; stochastic processes; interacting particle systems; random structures

Junu Lee
Ph.D. Candidate, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
Selective and post-selection inference; multiple testing; e-values and e-processes; sequential and distribution-free inference

Jiadong Liang
Postdoctoral Researcher, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
Stochastic approximation and online inference; reinforcement learning; diffusion models; high-dimensional learning

Renyuan Ma
Ph.D. Candidate, Department of Statistics and Data Science, Yale University
Random matrix theory; high-dimensional statistics; non-separable covariance models; random optimization

Aram-Alexandre Pooladian
Foundations of Data Science Postdoctoral Associate, Yale University
Optimal transport; diffusion and flow-based generative models; variational inference; statistical theory of generative modeling

Yuli Slavutsky
Founder’s Postdoctoral Research Scientist, Department of Statistics, Columbia University
Robust prediction across environments; representation learning; empirical Bayes; latent-variable and hierarchical models

Ian Waudby-Smith
Miller Fellow, Department of Statistics, University of California, Berkeley
Sequential inference; anytime-valid inference; e-values and e-processes; concentration inequalities; causal inference

Eric Xia
Postdoctoral Research Associate, Operations Research and Financial Engineering, Princeton University
Statistical theory and methodology; transfer and surrogate-assisted learning; prediction with auxiliary information

Kangjie Zhou
Postdoctoral Associate, Department of Statistics and Data Science, Cornell University
High-dimensional statistics; computational-statistical gaps; deep-learning theory; implicit bias and nonconvex optimization

Poster presenters:

Awni Altabaa
Ph.D. Student, Department of Statistics and Data Science, Yale University
Foundations of language models; latent reasoning and representation learning; relational reasoning; transformer theory

Anna Brandenberger
Ph.D. Student, Department of Mathematics, MIT
Probability and random structures; random graphs and networks; high-dimensional and combinatorial probability

Yuchen Chen
Ph.D. Student, Department of Statistics & Data Science, Carnegie Mellon University
High-dimensional statistics; nonconvex learning dynamics; single-index models; statistical theory of machine learning

Daniil Dmitriev
Postdoctoral Researcher, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
High-dimensional learning theory; random features; robust mixture learning; discrete diffusion models; differential privacy

Avrajit Ghosh
Postdoctoral Fellow, Simons Institute for the Theory of Computing and BAIR, University of California, Berkeley
Optimization dynamics in deep learning; implicit bias and implicit regularization; generalization; inverse problems

Wenjie Guan
Ph.D. Student, Department of Statistics and Data Science, Cornell University
Statistical machine learning; theory of transformer reasoning and generalization; high-dimensional inference

Yu Gui
Postdoctoral Researcher, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
Selective and distribution-free inference; conformal inference; causal inference; multimodal representation learning

Rohan Hore
Postdoctoral Fellow, Department of Statistics, Stanford University
Distribution-free inference; conformal inference; multiple testing and variable selection; conditional-independence testing

Feiyang Li
Ph.D. Student, Department of Statistics and Data Science, Cornell University
Statistical machine learning

Seunghyun (Sky) Lee
Ph.D. Candidate, Department of Statistics, Columbia University
High-dimensional latent-variable models; empirical Bayes and variational inference; graphical models; statistical theory of generative models

Bernardo Marenco
Universidad de la República
Statistical network models; random graphs; graph representation learning

Kenta Takatsu
Columbia University
Causal inference; semiparametric and nonparametric inference; doubly robust machine learning; inference for stochastic optimization

Boyu Wang
Ph.D. Student, Department of Statistics and Data Science, Cornell University
Statistics and data science

Yuepeng Yang
Postdoctoral Researcher, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania
High-dimensional statistical learning; reinforcement learning; matrix completion and multi-matrix estimation; ranking

 

Organizing Team

Rising Stars in Statistics and Data Science is organized by Cornell faculty in Statistics and Data Science, with event coordination support from staff.

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Jelena Bradic 
Professor of Statistics and Data Science

Faculty Organizer

Jelena Bradic is a professor of statistics and data science. Bradic’s research centers on causal inference, machine learning, and high-dimensional robust statistics, with applications in areas including public health and policy learning. 


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Ahmed El Alaoui
Assistant Professor of Statistics and Data Science

Faculty Organizer

Ahmed El Alaoui is an assistant professor of statistics and data science. His research interests revolve around high-dimensional phenomena in statistics and probability theory, statistical physics, algorithms, and problems where these areas meet. 

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Dana Yang
Assistant Professor of Statistics and Data Science

Faculty Organizer

Dana Yang is an assistant professor of statistics and data sciences. Prior to joining Cornell in 2022, she completed a Simons-Berkeley fellowship, where she participated in the Computational Complexity of Statistical Inference program at UC Berkeley. 


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Megan Adams
Assistant to the Chair, Department of Statistics and Data Science

Event Coordinator

Travel Information

Cornell University is located in Ithaca, New York. Detailed travel guidance, including airport options, ground transportation, and campus logistics, is available for invited participants.

We encourage attendees to review travel information below in advance and reach out with any questions related to planning their visit.

Participants should plan to arrive in Ithaca on Thursday, September 17, 2026, ahead of the workshop on Friday, September 18, and depart on Saturday, September 19. The formal program concludes Friday evening, and optional hiking and informal community activities are planned for Saturday, so guests should book return travel accordingly.

Ithaca Tompkins International Airport (ITH) is the closest airport to Cornell University. Cornell also identifies Syracuse Hancock International Airport, Elmira Corning Regional Airport, Greater Binghamton Airport, Frederick Douglass Rochester International Airport, and Buffalo Niagara International Airport as regional alternatives within driving distance of campus. Rail travelers should note that the nearest Amtrak station is in Syracuse.

Visitors traveling from NYC may also reach Ithaca by bus. Cornell’s Campus-to-Campus service connects Ithaca and New York City, and Cornell also points visitors to intercity bus options including OurBus, Greyhound, Coach USA, FlixBus, and Trailways. The Campus-to-Campus trip is approximately 4.5 to 5 hours from the final Ithaca departure stop.

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Contact

Questions about the workshop, invitations, travel, or event logistics may be directed to the organizing team.

risingstars [at] cornell.edu (risingstars[at]cornell[dot]edu)
 

risingstars [at] cornell.edu (Email the organizing team)

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