I am a fifth year PhD student at Stanford University, advised by Professors Guido Imbens and Vasilis Syrgkanis. I have developed causal inference and machine learning solutions using large (petabyte, terabyte) advertisement, graph, panel, social media, autosuggest, and web search data. Yes, I am excited about Generative AI :)!

I have >3 years of industry research experience and ~13 years of academic research experience. During doctoral studies, I spent ~18 months as a Visiting Research Scientist on Meta’s Central Applied Science team, and during MS studies, I worked at Microsoft Research NYC (4 full time research internships and 5 months consulting).

Research Before Doctoral Work

Before this, I conducted observational studies, crowdsourced experiments, and laboratory experiments to (1) build advertising products for web search, (2) develop human-computer interaction technology (e.g. Explainable AI), and (3) elucidate neurobiological mechanisms, respectively.

I was simultaneously a full time MS student in Applied Statistics for Social Science Research at New York University and a full time Research Intern in the Computational Social Science group at Microsoft Research. During some of that time, I was also a part time Research Technician working on Explainable Artificial Intelligence in the Cognitive and Data Science Lab at Rutgers University—Newark. Prior to my statistics and machine learning career, I studied the impact of age and lifestyle factors on learning and memory (e.g. probabilistic reinforcement learning).