June 2, 2026
Congratulations to the following PSD faculty members who have received NSF CAREER Awards. According to the NSF, the prestigious CAREER award supports early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
Yuxin Chen
An assistant professor in Computer Science, Yuxin Chen received an award for his project, “Active Representation Learning for Real-World Adaptive Experimental Design.”
Chen’s research focuses on active learning and sequential decision making, with an emphasis on data- and compute-efficient learning in complex environments. His work explores how learning systems can jointly optimize representation and decision making, enabling adaptive data acquisition, long-horizon planning, and robust performance under uncertainty. He develops methods that bridge reinforcement learning, active data collection, and structured modeling, with applications in robotics, scientific discovery, and engineering systems.
The funded project will help guide scientific discovery, engineering design, and complex decision making by efficiently identifying the most informative experiments, especially when uncertainty is high and observations are limited. The project will integrate data representation and experiment selection into a unified learning framework, where the strategy for choosing experiments is itself learned from data rather than specified by fixed rules.
Promit Ghosal
An assistant professor in Statistics, Promit Ghosal received an award for his project, “Geometric Foundations of Stochastic Dynamics: From Critical Phenomena to Quantum and Algorithmic Systems.”
Ghosal’s research lies at the intersection of probability theory, mathematical physics, optimal transport, and machine learning. His work develops new probabilistic and geometric methods to understand the structure of complex random systems, from critical stochastic partial differential equations and quantum geometry to modern generative AI algorithms. By combining ideas from renormalization, conformal field theory, optimal transport, and stochastic analysis, his research seeks to uncover the geometric principles that govern randomness across mathematics, physics, and data science.
The funded project will establish new mathematical foundations for understanding how geometry shapes random phenomena across scales. It will develop methods to characterize fractal structures in critical stochastic systems, build probabilistic models for quantum geometric spaces arising in mathematical physics, formulate entropy-based principles for optimal transport in curved geometries with connections to gravitation, and provide rigorous guarantees for interacting particle algorithms underlying modern generative AI. The project also includes mentoring students across institutions, developing graduate courses at the interface of probability and machine learning, and organizing research programs that broaden participation in the mathematical sciences.
Rana Hanocka
An assistant professor in Computer Science, Rana Hanocka received an award for her project, “Mesh Renaissance: From AI Foundations to Interactive 3D Creation.”
Hanocka has been developing 3D AI methods for over a decade, contributing foundational work in applying neural networks to meshes. She pioneered fundamental neural network operators that enable effective learning directly on mesh surfaces. Her research established a new paradigm for analyzing, understanding, and editing 3D geometry by leveraging 2D visual priors, overcoming the need for large-scale 3D training datasets. Her research has been recognized with paper awards at leading computer vision conferences and by the Frontiers of Science Award, the Pazy Memorial Award, the Dan David Prize, among others.
The funded project advances her vision to make AI on meshes as powerful and accessible as it is for images and text. Despite significant progress in 3D AI, meshes, the standard representation of 3D shapes in computer graphics and the backbone of fields from robotics to evolutionary biology, have been largely left behind by modern AI tools. The award will support three research thrusts: building a general-purpose mesh foundation model that produces universal features applicable across diverse downstream tasks; developing a suite of multimodal mesh editing tools that allow users to manipulate 3D content through text, images, point clicks, and scribbles; and designing mesh agents that enable iterative, co-creative 3D modeling through dialogue, bringing to 3D creation the same kind of interactive refinement that large language models have unlocked for text.
Elizabeth Jerison
An assistant professor in Physics, Elizabeth Jerison received an award for her project, “A Dynamical Systems Framework for Systemic Inflammatory Response.”
Jerison is a biological physicist who studies immunity. Her work aims to develop mathematical frameworks that describe states of the immune system, and how cellular interactions give rise to these states, with a focus on inflammatory response. Her research group combines experimental and computational approaches and uses zebrafish as their primary biological model system. This system enables high-throughput genomics experiments and live imaging of immune responses within the transparent larvae.
This award will fund experiments and AI/ML-driven computation to build a dynamical systems framework for systemic inflammatory responses. These dramatic organism-wide immune flares can be lethal, and much remains unknown about how their dynamics are controlled. Jerison’s team will use genomics and imaging-based experiments to trace trajectories of these responses in the zebrafish and develop models to predict and alter the course of these trajectories. The award will also support education and outreach, including by extending collaborative learning and physics of living systems curriculum into intro physics major courses.
Alex Kale
An assistant professor in Computer Science, Alex Kale received an award for his project, “Seeing What Matters: Reframing Visualization as Data Disclosure.”
Kale’s research develops tools and theory for helping people think with data, addressing cognitive and technical challenges that arise during data analysis and communication. His recent and ongoing work contributes new ways of using data visualization, AI, and other computational tools to create rigorous and personalized ways of interacting with data that promote skepticism, agency, and situated judgment. Through an interdisciplinary approach spanning user-centered software design, behavioral experiments, and theoretical formalisms, Kale contributes advancements in statistical software, exploratory data analysis, and decision aids across applications in the sciences, education, policy, and finance.
The funded project reframes visualization as a mechanism for data disclosure, providing a new way of conceptualizing design objectives for visualization in terms of whether the data signals that designers and audiences care about are shown or hidden. Forms of information loss resulting from visualization design choices are poorly formalized, leaving visualization designers and audiences to guess at the truthfulness of a chart, and conversely, its capacity to reveal sensitive information. Kale’s lab will address this challenge by developing new software and educational materials that help designers and students become better informed moral actors when communicating with visualizations, and by developing techniques to support skeptical reading of visualizations by broader audiences.
Zoe Yan
A Neubauer Family Assistant Professor in Physics and the James Franck Institute, Zoe Yan received an award for her project, “Ultrapolar Molecules: New Opportunities for Quantum Simulation.”
Quantum mechanics shapes how systems behave at every scale, from the dynamics of neutron stars to the rearrangement of electrons during chemical reactions. Yan and her group will use ultracold quantum gases—atoms or molecules cooled to nearly absolute zero temperature—to study and simulate such systems. A major recent advance has been the use of ultracold polar molecules, whose strong, tunable interactions provide powerful control over their internal states and motion. The next step is to produce more strongly interacting types of polar molecules (silver-potassium), to reach the interaction strengths needed to explore exotic new states of matter. This could be a viable pathway to reveal microscopic details behind topological p+ip superfluids, dipolar supersolids, and dipolar quantum spin liquids.