Hi! I am George Noarov, a 3rd year Computer Science PhD student at the University of Pennsylvania fortunate to be advised by Michael Kearns and Aaron Roth. Prior to this, I graduated Magna Cum Laude with a B.A. in Mathematics from Princeton University, where I was fortunate to be advised by Mark Braverman and Matt Weinberg.
I am broadly interested in theoretical computer science and machine learning. My current research develops techniques for fair, robust and trustworthy ML and distribution-free uncertainty quantification (including conformal prediction). I also work on algorithmic game theory and online learning.
Throughout my academic journey, I have participated in enriching research programs and industry internships, including as a visiting graduate student in the semester-long Data-Driven Decision Processes Program at the Simons Institute for the Theory of Computing, UC Berkeley, and as an intern at Goldman Sachs.
In 2023, I am thrilled to be funded by an Amazon Fellowship for Research in Trustworthy AI. I am also grateful to be the recipient of several academic awards, including the Undergraduate Award in Applied and Computational Mathematics and Princeton’s Shapiro Prize for Academic Excellence, and fellowships, including a Joseph Henry Summer Fellowship. I am also a member of the Phi Beta Kappa and Sigma Xi honor societies.
To learn more about my research, you can navigate to the Publications page or my Google Scholar profile.