About Me

I am a fourth year undergraduate in Computer Science and Statistics at Harvard University where I am advised by Yaron Singer and David Parkes.

My research is at the intersection of machine learning and the social sciences. I am attracted by the dynamic nature of social data and the algorithmic and statistical challenges it entails. The problems I engage with are of importance as we are increasingly using ML to improve decision-making in domains like business, politics and legal systems.


Label Propagation Networks
with Nir Rosenfeld and Amir Globerson
ICLR 2019 (under review)

Robust Classification of Financial Risk
with Daniel Giebisch, Suproteem Sarkar and Yaron Singer
NIPS 2018 Workshop on AI in Financial Services
Oral Presentation

Working Papers

Generalized Multi-self Models for Irrational Choice
with Nir Rosenfeld and Yaron Singer

Counterfactual Fair Inference with Unobserved Confounders
with Debmalya Mandal

Work Experience

I have worked for a $9B e-commerce with 300M users as a machine learning engineer, launched a Tokyo branch of a Harvard-MIT data consulting firm, and helped build a tool every Android developer uses.



Programming: Python, R, Matlab, SQL, C++, Go, Ruby
Machine Learning: TensorFlow/Keras, PyTorch, Pyro, Edward
DB & Infrastructure: MongoDB, AWS, Hadoop, Docker
Open Source Contributions: Homebrew, FFaker (Rails library)
Side Projects: Virtual Reality Pet, a Google Cardboard app with 12,000+ downloads.


Selected Coursework

Undergraduate Courses
Linear Algebra, Algorithms and Data Structures, Theory of Computation

Graduate Courses
Probability, Inference, Optimization, Machine Learning, Economics and Computation, Causal Inference, Econometrics