New 2023 academic school year brings new postdoctoral researchers to the Data Science Institute
The Data Science Institute is proud to welcome a new cohort of postdoctoral researchers. The Postdoctoral Scholars Program at the Data Science Institute offers fellowships for Postdoctoral Scholars who wish to deepen their knowledge of cutting-edge data science and computing research while developing additional expertise in a specific, applied problem domain.
Learn more about each of the new DSI scholars below.
Vasilis Charisopoulos
Vasilis is broadly interested in developing numerical optimization methods for machine learning, signal processing and scientific computing. He holds a PhD in Operations Research & Information Engineering from Cornell University. Vasilis was recognized as a Rising Star in Computational and Data Sciences by the UT Austin Oden Institute in 2023.
Yo Joong “YJ” Choe
Yo Joong “YJ” Choe received his Ph.D. in Statistics and Machine Learning from Carnegie Mellon University. YJ is broadly interested in evaluating and understanding modern machine learning predictors, including black-box classifiers, sequential forecasters, and large language models. He currently focuses on two research directions: (1) leveraging modern statistical methodology, such as game-theoretic statistics, anytime-valid inference, and causal inference, for rigorously evaluating black-box predictors; and (2) understanding and interpreting large language models, including their embedding spaces. During a hiatus from his Ph.D. program, he worked as a Research Scientist at Kakao Brain and Kakao, specializing in deep learning and natural language processing. YJ holds an M.S. in Machine Learning from Carnegie Mellon University and a B.S. in Mathematics and Computer Science from the University of Chicago.
Cristina Garbacea
Cristina Garbacea has a PhD degree in Computer Science and Engineering from University of Michigan Ann Arbor, a MSc degree in Artificial Intelligence from University of Amsterdam and a double BSc degree in Computer Science and Electrical Engineering. Her research interests are focused on deep learning for natural language processing, in particular on robust, controllable and sample-efficient natural language generation and evaluation. Her PhD thesis is titled “Neural Language Generation for Content Adaptation: Explainable, Efficient Low-Resource Text Simplification and Evaluation”, and explores ways to reduce the language complexity of professional content to make it accessible to broad audiences. Throughout her graduate studies she has interned multiple times with Google Deepmind and Microsoft Research.
Chong Liu
Chong Liu earned his PhD in Computer Science from UC Santa Barbara in 2023. His research interests include machine learning topics such as global optimization, bandits, and active learning, as well as topics in artificial intelligence such as experimental design, computational materials science, and drug discovery. As part of his academic interests, Chong serves as an Editorial Board Reviewer of the Journal of Machine Learning Research.
Chong is also a FAA-Certified Private Pilot with Airplane Single Engine Land Rating (PP-ASEL).
Jonatas Marques
Jonatas A. Marques was previously a PhD student in Computer Science at the Federal University of Rio Grande do Sul (UFRGS, Brazil), advised by Luciano Paschoal Gaspary. His current research interests are on the intersection of machine learning and computer networking, with focus on programmable networking and network management. Jonatas is part of the Internet Equity Initiative at DSI, with the goal of measuring and analyzing Internet performance and reliability to address inequity in U.S. communities.
Isaac Mehlhaff
Isaac Mehlhaff’s research is driven by substantive questions in public opinion and political psychology: How and why do citizens change their attitudes on political issues? How do these attitude changes drive mass polarization? How is polarization causally related to other features of government and society? He investigates these questions by using and developing methods in natural language processing, Bayesian statistics, and causal inference, with a particular focus on high-quality measurement.
Isaac is part of the Data & Democracy initiative at DSI, with the goal of developing and deploying flexible chatbots to study political communication, persuasion, and misinformation. He holds a PhD and MA in political science from The University of North Carolina at Chapel Hill and a BA in history, political science, and economics from the University of Wisconsin-Madison.
Seyed Esmaeili
Seyed Esmaeili will join the Data Science Institute from Berkeley in December.