Organizers

Harlin Lee is a Hedrick Assistant Adjunct Professor at UCLA Mathematics. She received her PhD in Electrical and Computer Engineering and MS in Machine Learning from Carnegie Mellon University in 2021. Prior to that, she got her BS and MEng in Electrical Engineering and Computer Science from MIT in 2016 and 2017, respectively. Her research is on learning from high-dimensional data supported on structures (such as graphs/networks or low-dimensional subspace), motivated by applications in healthcare and social science including data science for social good. She has been recognized with Rising Stars in Data Science (2022), Rising Stars in Computational and Data Sciences (2022), CMU ECE Outstanding Woman in Engineering (2021), and Best Poster Prize at February Fourier Talks (2019).

Eamon Duede is a philosopher of science and a computational researcher. His research agenda is broad and advances both our theoretical and empirical understanding of how emerging technologies (particularly AI) affect the way we do science and investigates how we can both study and use computational methods to better understand the joint processes of discovery and justification. He is currently a joint PhD Candidate in the departments of Philosophy and the Committee on the Conceptual and Historical Studies of Science at the University of Chicago. He is a Fellow in the Pritzker School of Molecular Engineering AI-Enabled Molecular Engineering of Materials and Systems for Sustainability program, an Affiliated Researcher at Globus Labs, and a member of the KnowledgeLab.

Rishi Sonthalia is a Hedrick Assistant Adjunct Faculty in the math department at UCLA. Before UCLA he was a visiting researcher at Yale University (2020-2021) and was an Applied and Interdisciplinary Mathematics Ph.D. student at the University of Michigan (2016-2021). He is interested in understanding the intrinsic geometric and probabilistic structure of data to design effective algorithms and tools that can be applied to machine learning and across all branches of science. The current focus of his research is to increase the effectiveness of machine learning techniques by developing a mathematical and algorithmic framework using which, given any type of data and a downstream task, we can learn an optimal representation.

Program Committee

Jacob G. Foster (UCLA)

Erin Leahey (University of Arizona)

Mario Krenn (Max Planck Institute for the Science of Light)

James Evans (University of Chicago)

Jevin West (University of Washington)