- Assistant Professor of Computing and Mathematical Sciences and Economics
Eric Mazumdar's research lies at the intersection of machine learning and economics. He is broadly interested in developing the tools and understanding necessary to confidently deploy machine learning algorithms into societal-scale systems. Practically, he applies his work to work to problems in intelligent infrastructure, online markets, e-commerce, and the delivery of healthcare.
- Georgia will join Caltech in January 2023.
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.
- Assistant Professor of Computing and Mathematical Sciences
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.
- von Karman Instructor in Computing and Mathematical Sciences
Ricardo Baptista’s research is motivated by the need for accurate uncertainty quantification in complex physical systems. He is interested in developing scalable computational methods for Bayesian inference and probabilistic modeling, in particular using measure transport and dimension reduction techniques. Practically, he applies his work to improve predictions and build insights for problems in science, engineering, and medicine.
- Yang will join Caltech in January 2024.
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 – from synthesizing highly structured data to solving complicated inverse problems – across a range of engineering disciplines.