Introducing new faculty in the Physical Sciences Division 2023-24

September 15, 2023

Please welcome the faculty joining the Physical Sciences Division in the 2023–24 academic year.

Pedram Hassanzadeh, Geophysical Sciences

Pedram Hassanzadeh

Pedram Hassanzadeh is an associate professor in the Department of the Geophysical Sciences starting January 2024. He works on improving the fundamental understanding of the physics of extreme weather events (e.g., heat waves, tropical cyclones, and droughts), and how they might change as the climate system warms. His work combines theory, observational data, and computer models. In recent years, a major theme in his research has been developing new tools, by integrating deep learning, applied math, and numerical analysis techniques, to improve the predictive capability for multiscale nonlinear dynamical systems such as climate and turbulence.

He received his MA in Mathematics working on optimal transport and PhD in Mechanical Engineering working on geophysical and astrophysical turbulence from UC Berkeley in 2012 and 2013, respectively. He was a Ziff environmental fellow at the Harvard University Center for the Environment and the Department of Earth and Planetary Sciences until 2016. Before joining the University of Chicago, Hassanzadeh was an assistant and then associate professor of mechanical engineering and Earth, environmental and planetary sciences at Rice University. He has received a number of honors, including a CAREER Award from NSF and a Young Investigator Award from the Office of Naval Research.

Andrew Higginbotham, Physics and the James Franck Institute

Andrew Higginbotham

Andrew Higginbotham is an assistant professor in the Department of Physics and the James Franck Institute starting in November 2023. He experimentally explores the connection between condensed matter physics and quantum information science and is currently investigating how collective behavior, for instance phase transitions, can emerge in simple quantum devices and affect their performance. Before joining the UChicago faculty, he was an assistant professor at IST Austria, a research scientist at Microsoft, and a National Research Council postdoctoral fellow at JILA/CU Boulder. He holds a BSc from Harvey Mudd College, MPhil from Cambridge University, and a PhD from Harvard University.

Ari Holtzman, Computer Science

Ari Holtzman

Ari Holtzman is an assistant professor in the Department of Computer Science starting July 2024. His research interests have spanned everything from dialogue, including winning the first Amazon Alexa Prize in 2017, to fundamental research on text generation, such as proposing Nucleus Sampling, a decoding algorithm used broadly in deployed systems such as the OpenAI API as well as in academic research. Ari completed an interdisciplinary degree at NYU combining Computer Science and the Philosophy of Language. He will receive his PhD from the University of Washington.

William Hoza, Computer Science

William Hoza

William Hoza is an assistant professor in the Department of Computer Science. He recently completed a postdoctoral appointment at the University of California, Berkeley in the Simons Institute for the Theory of Computing. His PhD is from the University of Texas at Austin, where he was advised by David Zuckerman. He studies topics in computational complexity theory, especially pseudorandomness and derandomization.

Nikolaos Ignatiadis, Statistics and the Data Science Institute

Nikolaos Ignatiadis

Nikolaos Ignatiadis is an assistant professor of Statistics and Data Science with formal training in mathematics, molecular biology, and computation. His research is driven by the new modeling and inference opportunities made possible through the wealth of modern data. He develops both practical and theoretically justified statistical methods, accompanied by robust software implementations. His methodological interests encompass empirical Bayes analysis, causal inference, multiple testing, and statistics in the presence of contextual side-information.

Before joining the UChicago faculty, he was a postdoctoral research scientist in the Department of Statistics at Columbia University. Ignatiadis received his PhD from Stanford’s Statistics department in the summer of 2022, and his thesis was recognized with the Jerome H. Friedman dissertation award. Previously, he earned degrees in Mathematics, Molecular Biotechnology, and Scientific Computing at the University of Heidelberg in Germany, where he was a researcher at the European Molecular Biology Laboratory.

Haotian Jiang, Computer Science

Haotian Jiang

Haotian Jiang is an assistant professor in the Department of Computer Science starting July 2024. He is currently a Postdoctoral Researcher at Microsoft Research, Redmond. In December 2022, he obtained his PhD from the Paul G. Allen School of Computer Science & Engineering at University of Washington under the supervision of Yin Tat Lee. He is broadly interested in theoretical computer science and applied mathematics. His primary area of expertise is the design and analysis of algorithms for continuous and discrete optimization problems and algorithm design through the lens of discrepancy theory. His work on optimization has been recognized by a Best Student Paper Award in SODA 2021.

Allison Karp, Geophysical Sciences

Allison Karp

Allison Karp is an assistant professor in the Department of the Geophysical Sciences starting July 2024. Karp is a molecular paleoecologist, who uses organic geochemical and stable isotopic tools to study how disturbance processes, such as wildfire and herbivory, interacted with carbon cycling and climate change in ancient terrestrial ecosystems. Karp’s work mainly focuses on fires in savannas and grasslands, which today account for ~80% of global annual burned area. Before starting at the University of Chicago, Karp was a National Science Foundation Postdoctoral Fellow with a joint appointment at Yale University and Brown University. Karp received a bachelor’s in Biology and Environmental Earth Science from Washington University in St. Louis in 2015 and a PhD in Geosciences from Pennsylvania State University in 2020.

Harley Katz, Astronomy & Astrophysics

Harley Katz

 Harley Katz is an assistant professor in the Department of Astronomy & Astrophysics starting January 2024. Katz develops and runs large computer simulations that model gravity, gas dynamics, magnetic fields, radiation, and chemistry to understand how the Universe evolved from a hot distribution of hydrogen and helium to the complex set of astrophysical objects that we can see with our telescopes today. His work makes direct predictions that are currently being tested with the Hubble and James Webb Space Telescopes. He holds a BSc in physics and astronomy from the University of Maryland, College Park as well as a PhD in astronomy from the University of Cambridge.

David Keith, Geophysical Sciences

David Keith

David Keith has worked near the interface between climate science, energy technology, and public policy since 1990. Best known for his work on the science, technology, and public policy of solar geoengineering, Keith has worked on diverse topics including economics of electricity decarbonization, public perception of emerging technologies, and the climate impacts of wind power. Keith is Professor of Geophysical Sciences and founding faculty director of the Climate Systems Engineering initiative at University of Chicago. He founded Carbon Engineering, a Canadian company developing technology to capture CO2 from ambient air.

Frederic Koehler, Statistics and the Data Science Institute

Frederic Koehler

Frederic Koehler is an assistant professor in the Department of Statistics and the Data Science Institute starting January 2024. His primary interest lies in fundamental problems at the intersection of algorithms, statistics, and learning. For instance, understanding the interplay between memorization and generalization to unseen examples, analyzing tradeoffs between computational and statistical efficiency, and designing algorithms for sampling from high-dimensional distributions. He received his PhD from MIT and most recently was a Motwani postdoctoral fellow in the Department of Computer Science at Stanford University.

Mina Lee, Computer Science

Mina Lee

Mina Lee is an assistant professor in the Department of Computer Science starting July 2024. Her research goal is to design and evaluate language models to enhance our productivity and creativity and understand how these models change the way we write. She has built various writing assistants, including an autocomplete system, a contextual thesaurus system, and a creative story–writing system. In addition, she has developed a new framework to evaluate language models based on their ability to interact with humans and augment human capabilities. She was named one of MIT Technology Review’s Korean Innovators under 35 in 2022, and her work has been published in top-tier venues in natural language processing (e.g., ACL and NAACL), machine learning (e.g., NeurIPS), and human-computer interaction (e.g., CHI). Her recent work on human-AI collaborative writing received an Honorable Mention Award at CHI 2022 and was featured in various media outlets, including The Economist. Mina received her PhD from Stanford University in 2023.

Bo Li, Computer Science

Bo Li

Bo Li is an associate professor in the Department of Computer Science. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, AI’s 10 to Watch, MIT Technology Review TR-35 Award, Dean’s Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star Award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from tech companies such as Amazon, Meta, Google, Intel, MSR, eBay, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for robust learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and The New York Times.

Tian Li, Computer Science

Tian Li

Tian Li is an assistant professor in the Department of Computer Science starting July 2024. Her research interests are in distributed optimization, federated learning, and trustworthy ML. Tian is receiving her PhD from Carnegie Mellon University. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She received the Best Paper Award at the ICLR Workshop on Security and Safety in Machine Learning Systems, was invited to participate in the EECS Rising Stars Workshop, and was recognized as a Rising Star in Machine Learning/Data Science by multiple institutions.

Xinran Li, Statistics

Xinran Li

Xinran Li is an assistant professor in the Department of Statistics. He specializes in randomization-based inference for causal effects, sensitivity analysis for observational studies, experimental design such as rerandomization, and Bayesian inference. He also applies these methodologies to social and biomedical sciences and is passionate about exploring new avenues to apply and advance these methodologies. Prior to joining UChicago, he was an assistant professor in the Department of Statistics at the University of Illinois at Urbana-Champaign. He received his BS in mathematics and applied mathematics and BA in economics from Peking University, and PhD in statistics from Harvard University. 

Kexin Pei, Computer Science

Kexin Pei

Kexin Pei is an assistant professor in the Department of Computer Science starting July 2024. His research lies at the intersection of Security, Software Engineering, and Machine Learning, focusing on developing data-driven program analysis approaches to improve the security and reliability of traditional and AI-based software systems. He gets most excited about developing machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities. His research has received the Best Paper Award in SOSP, a Distinguished Artifact Award, been featured in CACM Research Highlight, and won CSAW Applied Research Competition Runner-Up. He works with the Learning for Code team at Google DeepMind, building program analysis tools based on large language models. Kexin received his PhD in Computer Science from Columbia University.

Meghana Ranganathan, Geophysical Sciences

Meghana Ranganathan

Meghana Ranganathan is an assistant professor in the Department of the Geophysical Sciences starting July 2024. Her research seeks to understand the dynamics of ice sheets and glaciers, with the ultimate goal of providing more certain estimates for the amount of glacial ice that will be lost under a changing climate and how much seas will rise as a consequence. In doing so, she seeks to unite a climate science perspective with a materials science perspective in order to model the physical processes by which ice sheets change—viscous flow of ice from the interior of ice sheets towards the ocean, and brittle fracture of ice into icebergs that eventually melt into the ocean. Previously, she received her undergraduate degree in mathematics from Swarthmore College, her PhD in climate science from the Massachusetts Institute of Technology, and was a NOAA Climate & Global Change Postdoctoral Fellow at the Georgia Institute of Technology. 

Linta Reji, Geophysical Sciences

Linta Reji

Linta Reji is an assistant professor in the Department of the Geophysical Sciences starting July 2024. Her research focuses on developing mechanistic insights into how microbial communities respond to natural and anthropogenic global change factors. She is particularly interested in investigating the biogeochemical consequences of the enormous microbial diversity found in natural ecosystems. Her work integrates perturbation experiments in the lab with spatio-temporally resolved field observations, using a combination of tools from microbial ecology and biogeochemistry. She received her bachelor’s and PhD degrees from Stanford University. She will continue as a postdoctoral research associate at Princeton University until arriving at UChicago.

Mingyi Wang, Geophysical Sciences

Mingyi Wang

Mingyi Wang is an assistant professor in the Department of the Geophysical Sciences starting January 2024. Wang’s research provides novel approaches to constrain atmospheric chemistry and aerosol microphysics, addressing pressing societal needs such as science-based policy making, aerosol forcing prediction, and climate intervention assessment. Prior to joining UChicago, they were a Schmidt Science Postdoctoral Fellow in chemical engineering at Caltech. Wang received their PhD in atmospheric chemistry from Carnegie Mellon University, and their research has been recognized with the Sheldon K. Friedlander Award from the American Association for Aerosol Research.

Zoe Yan, Physics and the James Franck Institute

Zoe Yan

Zoe Yan is an assistant professor at the James Franck Institute and the Department of Physics. She studies experimental quantum many-body physics using the platforms of ultracold atoms and molecules. Her experiments combine cutting-edge technologies in trapping and imaging quantum particles and tailoring their interactions to realize custom Hamiltonians. Before joining UChicago, Yan was a Dicke Postdoctoral Fellow at Princeton University. She earned her PhD from the Massachusetts Institute of Technology and her BS with honors in physics from Stanford University. She is the recipient of the 2023 Blavatnik Regional Award for Young Scientists.

Da Yang, Geophysical Sciences

Da Yang

Da Yang is an assistant professor in the Department of the Geophysical Sciences. His research interests have focused on understanding clouds, rainstorms, and climate change. In recent work, he asked why individual convective clouds tend to organize together, forming large-scale rainstorms, such as hurricanes, what environmental factors control rainstorms’ spatial scale, and what are essential elements in forecasting rainstorms. He has also discovered that cold air rises in the tropical atmosphere. This discovery led to explorations on the buoyancy effect of water vapor, which is less familiar than thermal buoyancy due to temperature contrasts.

Yang is a recipient of a Packard Fellowship for Science and Engineering, an NSF CAREER Award, and a Miller Research Fellowship. He also serves as an associate editor for the Journal of Climate. He received a BS from Peking University and a PhD from the California Institute of Technology. Before joining UChicago, Yang was on the faculty at the University of California, Davis, and the Lawrence Berkeley National Laboratory.

Ce Zhang, Computer Science

Ce Zhang

Ce Zhang is an associate professor in the Department of Computer Science starting July 2024. He is currently an associate professor of Computer Science at ETH Zurich. Before joining the department, he will spend his time as the CTO of Together, building a decentralized cloud for artificial intelligence. His research looks at the fundamental tension between data, model, computation and infrastructure, with the final goal of democratizing machine learning and artificial intelligence. Recently, he has been focusing on building next-generation machine learning platforms and systems that are data-centric, human-centric, and declaratively scalable.

Before joining ETH, Ce finished his PhD at the University of Wisconsin-Madison and spent another year as a postdoctoral researcher at Stanford, both advised by Christopher Ré. His work has received recognitions such as the SIGMOD Best Paper Award, SIGMOD Research Highlight Award, Google Focused Research Award, an ERC grant, and has been featured and reported by Science, Nature, the Communications of the ACM, and various media outlets such as Atlantic, WIRED, Quanta Magazine, etc. He also currently serves as the co-Editors-in-Chief of DMLR, a new member of the JMLR family focusing on data-centric machine learning research.

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