Four UChicago Researchers Named Rising Stars in Data Science
The University of Chicago is proud to celebrate four exceptional early-career researchers who have been selected as Rising Stars in Data Science at the sixth annual workshop, held November 11-12 at Stanford University. The program “focuses on celebrating and fast-tracking the careers of exceptional data scientists,” providing early-career scholars with mentorship and networking as they transition to postdoctoral fellowships, tenure-track positions, or research roles in academia and industry.
This year’s Rising Stars in Data Science workshop was hosted at Stanford and co-organized by the University of California San Diego and the University of Chicago Data Science Institute (DSI), which founded the program in 2020. Over the past six years, the workshop has hosted 160 early career scholars from across 40 institutions. This fall, 30 scholars were recognized as rising stars, four of whom hailed from UChicago.
The agenda included sessions designed to prepare participants for the academic job market and beyond, among them a faculty panel offering advice on the job search process, tailoring applications, navigating department cultures, and negotiating offers. An Alumni panel, moderated by Executive Director of the DSI David Uminsky, brought back previous program participants who shared their experiences navigating the job market and offered candid advice on thriving in interdisciplinary data science fields. The workshop concluded with the panel “Beyond the Job Offer: Making the Most of Year 1,” where panelists discussed priorities when starting a faculty position, strategies for work-life balance, and tips for recruiting graduate students.
Participants also benefited from one-on-one mentor meetings and drop-in office hours with faculty from all three institutions, creating personalized guidance tailored to their individual career goals and research areas.
Scholars also presented their research through Job Talk Showcases and Lightning Talks, where they received feedback on how to effectively communicate their research vision and gained practice handling challenging questions in preparation for upcoming interviews. Each day of the workshop also concluded with receptions and poster sessions, where participants connected over shared research interests, discussed their work in depth, and built networks that will support their careers for years to come.
UChicago’s four Rising Stars represent the breadth and excellence of data science research at the University, spanning computational social science, machine learning, and human-AI interaction.
Junsol Kim
PhD Student, Department of Sociology
Junsol Kim is a PhD candidate in the Knowledge Lab at the University of Chicago, advised by Max Palevsky Professor of Sociology & Data Science and Faculty Co-Director of Novel Intelligence James Evans. He is also a Google Student Researcher. His research as a computational social scientist examines how AI and other social technologies reshape information and knowledge ecosystems. His work focuses on fine-tuning large language models using social survey data to predict public opinions, and he has applied quasi-experimental methods to large-scale social media data to study misinformation moderation and echo chambers.
Junsol’s research has been published in the Proceedings of the National Academy of Sciences (PNAS) and other top venues. He holds an MA in Sociology from UChicago and a BA/BS in Sociology and Computer Science from Yonsei University.
Tahseen Rabbani
Postdoctoral Scholar, Department of Computer Science
Tahseen Rabbani is an AI/ML postdoctoral scholar at the University of Chicago, advised by Neubauer Associate Professor of Computer Science and Data Science Ce Zhang and Assistant Professor of Computer Science and Data Science Tian Li. His research focuses on efficient algorithms for AI/ML and data privacy, with work encompassing model compression, training efficiency, privacy, distributed learning, and machine translation.
Prior to joining UChicago, Tahseen was a postdoc at Yale University’s LiGHT lab working on low-resource strategies for protein-drug matching and clinical LLMs. He received his PhD in Computer Science from the University of Maryland, College Park, where he was an NSF COMBINE Fellow and RSA Security Scholar. His background is originally in mathematics, with expertise in algebra and error-correction.
Jacy Reese Anthis
PhD Student, Sociology and Econometrics & Statistics
Jacy Reese Anthis is a computational social scientist and PhD candidate researching human-AI interaction and machine learning. His doctoral dissertation focuses on the emergence of “digital minds”—general-purpose AI systems that can work side-by-side with humans and appear to have reasoning, emotion, agency, and other mental faculties. His committee was led by Professor of Sociology and Faculty Co-Director of Novel Intelligence James Evans, Associate Professor of Econometrics and Statistics Bryon Aragam, Assistant Professor of Sociology Bernard Koch, and Professor of Econometrics and Statistics Nicholas Polson.
His research has been published in venues including CHI, ACL, and NeurIPS, and featured in global media outlets including Vox, Forbes, and The Guardian. During his PhD, he has conducted research at Stanford, UC Berkeley, Google, Microsoft, and OpenAI. He is also a visiting scholar at the Institute for Human-Centered AI (HAI) at Stanford University and co-founder of the nonprofit Sentience Institute. Jacy earned his MA in Sociology from UChicago and his BSA in Neuroscience from the University of Texas at Austin.
Nathan Waniorek
PhD Candidate, Computational and Applied Mathematics
Nathan Waniorek is a fourth-year PhD candidate in Computational and Applied Mathematics, advised by Associate Professor of Statistics Daniel Sanz-Alonso. His research interests lie at the intersection of applied mathematics, computational science, and statistics, with work focused on inverse problems, data assimilation, and scientific machine learning. His research aims to combine complex physical models with data in a mathematically principled way.
Nathan’s recent work includes establishing long-time accuracy of ensemble Kalman filters and optimal convergence rates for estimation of structured covariance operators of Gaussian processes. He earned his BS in Applied Mathematics from Case Western Reserve University.
“We’re thrilled to see this many UChicago researchers recognized among this year’s Rising Stars,” said Uminsky. “Junsol, Tahseen, Jacy, and Nathan’s work across computational social science and human-AI interaction shows the breadth and excellence of data science happening at UChicago.” He added, “The network and mentorship that early career data scientists can receive at a place like the Rising Stars Workshop uniquely equips them to launch into the next phase of their careers.”
Next year’s Rising Stars Workshop will return to the University of Chicago.
Congratulations to Junsol, Tahseen, Jacy, and Nathan on this outstanding achievement! We look forward to following your continued contributions to data science and AI.
