Welcome! I am a PhD student in the Department of Computer Science, Cornell University. I am very fortunate to be advised by Jon Kleinberg.
Research Interests: I am interested in AI, Machine Learning (particularly Deep Learning) and Theory. My broad research goal is to better bridge the gap between theory and practice, especially in Machine Learning. I am presently looking at bringing greater interpretability to empirical observations in Deep Learning with a mixture of experiments and theoretical analysis.
Previous projects include providing a theoretical model for better understanding group performance (Team Performance and Test Scores) and designing, analysing and empirically evaluating a generative model for user trails (forthcoming.)
Before Cornell, I was at the University of Cambridge (Trinity College) where I completed my Bachelors and Masters (Part III of the Tripos) in Mathematics. I spent the latter half of my life in the UK, but have also lived in France, South Africa, India and the US.Research Internships:
Abstract. Team performance is a ubiquitous area of inquiry in the social sciences, and it motivates the problem of team selection -- choosing the members of a team for maximum performance. Influential work of Hong and Page has argued that testing individuals in isolation and then assembling the highest-scoring ones into a team is not an effective method for team selection. For a broad class of performance measures, based on the expected maximum of random variables representing individual candidates, we show that tests directly measuring individual performance are indeed ineffective, but that a more subtle family of tests used in isolation can provide a constant-factor approximation for team performance. These new tests measure the 'potential' of individuals, in a precise sense, rather than performance; to our knowledge they represent the first time that individual tests have been shown to produce near-optimal teams for a non-trivial team performance measure. We also show families of subdmodular and supermodular team performance functions for which no test applied to individuals can produce near-optimal teams, and discuss implications for submodular maximization via hill-climbing.