Teaching FRE 7773 Machine Learning in Financial Engineering

I led a team building conversational AI for Square Messages, a messaging hub for Square merchants to communicate with their customers. We saved merchants time by progressively automating their conversations with in-house chatbots and large language models.

Worked on the Risk team building frameworks for managing the life cycle of machine learning models for fraud detection.

Freelance data science consulting for a range of clients and industries. Also tried to start a machine learning model monitoring company.

Provided technical mentorship to post-PhD, early career Data Scientists. Topics include product thinking, software engineering, and machine learning.

Joined at the Seed stage as the first Data person and third Engineer. I built a lot of things and then ended up managing a team of MLEs, analysts, and engineers. Primary work involved recommendation systems, forecasting, computer vision, and mixed integer programming.

Increasing personalization by building data science products. My work is broadly grouped into 3 subject areas:‌• Recommender systems: modern, contextualized recommendations utlizing implicit feedback and side information in a learning to rank, factorization-based framework. So many buzzwords.‌• Optimization/Operations: Solving assortment, allocation, and scheduling problems using mixed-integer programming.‌• Classification/Regression: Predicting things with medium-sized datasets (hundreds of millions of samples, thousands of features).

I worked in Abhay Pasupathy's lab using scanning tunneling microscopy and spectroscopy to study strongly correlated phenomena like charge density waves and unconventional superconductivity. The work ranged from designing and building scientific instrumentation to image processing and computational modeling.

Built TutorWorthy, a Python-based web application that helps private NYC tutors find their market value.

Fabrication, transport, and atomic force microscopy of graphene-dielectric heterostructures in the lab of Philip Kim

Microfluidics device fabrication for lab-on-a-chip applications.

Low temperature transport characterization of devices used for optical spectroscopy of self assembled quantum dots.