Brennan Antone

Faculty Mentors:
Connie Yuan, Communication, Global Development, College of Agriculture and Life Sciences
Malte Jung, Information Science, Cornell Bowers College of Computing and Information Science

Brennan is an Assistant Research Professor at the Center for Data Science for Enterprise and Society, with a focus on social network analysis.  Brennan’s work leverages network analytic methods to study interpersonal relationships in teams, organizational communication technology, and network interventions.

Su Jia

Faculty Mentors:
Peter Frazier, School of Operations Research and Information Engineering, Cornell Engineering
Nathan Kallus, School of Operations Research and Information Engineering, Cornell Engineering
Karan Girotra, Johnson Grad School of Management, SC Johnson, Cornell Dyson School of Applied Economics

Su is an Assistant Research Professor at the Center for Data Science for Enterprise and Society (CDSES) at Cornell University.  His research focuses on the interplay between data, algorithms, and markets. More precisely, Su is interested in designing algorithms for learning and optimization problems in online marketplaces, such as pricing, advertising and a/b testing. 

Su is the recipient of the 2022 INFORMS  George B. Dantzig Dissertation Award.  This award is given for the best dissertation in any area of operations research and the management sciences that is innovative and relevant to practice.

Unso (Eun Seo) Jo

Faculty Mentors:
Marten van Schijndel, Linguistics, College of Arts and Sciences
David Mimno, Information Science, Cornell Bowers College of Computing and Information Science

Unso (Eun Seo) Jo is a computational historian interested in topics at the intersection of AI and the humanities with a focus on international and linguistic inclusivity. Her current book project on the history of AI is a global history of how people have imagined man-made intelligence since the Enlightenment and the ways in which these ideas still have an impact today. She is also working on training and evaluating Asian language models as well as building datasets they could be trained on. Before Cornell Dr. Jo worked as a research engineer at Hugging Face where she has worked on projects such as multilingual search and few-shot learning. She has interdisciplinary academic training in History (PhD) and Computer Science (MS) and her passion is in bridging engineering and the humanities!

Yoav Kolumbus

Faculty Mentors:

Éva Tardos, Computer Science, Cornell Bowers College of Computing and Information Science

David Easley, Economics, College of Arts and Sciences

Robert Kleinberg, Computer Science, Cornell Bowers College of Computing and Information Science

Yoav is an Assistant Research Professor at the Center for Data Science for Enterprise and Society. His research interests lie at the interface of machine learning, algorithmic game theory, dynamical systems, and networks, with a focus on learning in games and interactions between learning and strategic play in multi-agent systems of social and economic importance. Prior to joining Cornell, Yoav completed his Ph.D. in Computer Science at the Hebrew University of Jerusalem, advised by Noam Nisan, and his M.Sc. in Physics, advised by Sorin Solomon.

Aleksandr Michuda

Faculty Mentors:
Aditya Vashistha, Information Science, Cornell Bowers College of Computing and Information Science
Brian Dillon, SC Johnson, Cornell Dyson School of Applied Economics

Aleks is an Assistant Research Professor at the Center for Data Science for Enterprise and Society. Aleksandr studies how machine learning and big data can be used to solve problems in development economics. He is particularly interested in the role of rideshare applications in transforming the labor market in emerging markets. He has worked with various corporate partners to make rigorous and policy relevant research. He currently has a joint appointment in the Department of Information Science, School of Computing and Information Science and with the Dyson School of Applied Economics and Management. He graduated from UC Davis with a PhD in Agricultural and Resource Economics.

Alexander Terenin

Faculty Mentors:
Ziv Scully, Operations Research and Information Engineering, College of Engineering
David Bindel, Computer Science, Cornell Bowers College of Computing and Information Science

Alexander Terenin is an Assistant Research Professor at Cornell. He is interested in machine learning, particularly for problems where the data is not fixed, but is gathered interactively by the learning machine. His work focuses on data-efficient interactive decision-making algorithms such as Bayesian optimization, and uncertainty-aware probabilistic models that power such algorithms, including Gaussian processes. His technical contributions to this area have won multiple best-paper-type awards at top machine learning conferences. More recently, he is also interested in non-probabilistic approaches to decision-making, particularly in non-discrete problems, including for multi-armed bandits, online learning, and reinforcement learning.

Alfredo Torrico

Faculty Mentors:
Samitha Samaranayake, Civil and Environmental Engineering, Cornell Engineering
Nikhil Garg, School of Operations Research and Information Engineering, Jacobs Technion-Cornell Institute

Alfredo is an Assistant Research Professor at the Center for Data Science for Enterprise and Society.  His main research interests lie at the intersection of optimization and operations research, with a focus on matching markets, resource allocation and subset selection problems. In particular, he develops algorithmic tools and design optimization models to tackle challenges that appear in applications such as online advertising, student-school allocation, online matching platforms, facility location and data summarization.  Alfredo is also interested in (1) societal challenges that arise in multi-agent and societal systems such as fairness, diversity, and the spread of misinformation; (2) the interactions between optimization and machine learning such as the design of new approaches for submodular optimization.