# CMS People

- von Karman Instructor in Computing and Mathematical Sciences

### Catherine Babecki

Catherine Babecki is a postdoctoral researcher jointly supported by the CMS Department as a von Karman instructor and the Mathematics Department as a Taussky-Todd Teaching Fellow, hosted by Venkat Chandrasekaran, Nets Katz, and Leonard Schulman. She is broadly interested in combinatorics, discrete geometry, spectral graph theory, theoretical computer science, and optimization. She completed her PhD with Rekha Thomas at the University of Washington, Seattle in 2023. Before UW, she received a BS in mathematics from Penn State, and an AS in mathematics from Mercer County Community College.

- von Karman Instructor in Computing and Mathematical Sciences

### Elizabeth Carlson

Elizabeth Carlson's research interests include partial differential equations and fluid dynamics, with practical emphases in data assimilation, optimization, high performance computing, and numerical analysis.

- Assistant Professor of Computing and Mathematical Sciences

### Franca Hoffmann

Franca Hoffmann's research is focused on the interface between applied mathematics and data analysis, driven by the need to provide rigorous mathematical foundations for modeling tools used in applications.

- Assistant Professor of Computing and Mathematical Sciences and Electrical Engineering; William H. Hurt Scholar

### Georgia Gkioxari

Georgia's research focuses on machine vision, namely teaching machines to see. Her work explores methods for learning from visual corpora to tackle challenging visual tasks with scalable, efficient and generalizable solutions. Georgia's research is centered around object recognition from images and videos as well as object tracking and 3D understanding.

- Yang will join Caltech in January 2024.

### Yang Song

Yang's research is aimed at unlocking the potential of machine learning, particularly in terms of probabilistic modeling and inference for high-dimensional data distributions. His work encompasses a wide array of areas, including generative modeling, deep neural networks, optimization, and AI safety. By developing effective theory and scalable algorithms, Yang seeks to build AI systems to tackle complex problems â€“ from synthesizing highly structured data to solving complicated inverse problems â€“ across a range of engineering disciplines.