Currently creating a new student taught class centered around many things production for ML Systems. This includes deployment, data drifts, online & active learning, continual training, MLOps tooling and frameworks, etc.
This is advised/sponsored by Professor Christopher De Sa and Munich RE.
Junior Year
Won first in ProjectX, a competition hosted by UofT, in the epidemiology category with a paper published to the corresponding
conference.
Contributed to CoalescenceML, an open-source MLOps framework to develop industry-grade production ML pipelines coalescing the MLOps stack under one umbrella.
Led some initiatives on Cornell Data Science:
Pushed for all projects to be pitched team-wide rather than keeping internal subteam projects. This allowed for collaboration and more ambitious projects to be undertaken.
As onboarding chair, I created the first iteration of a centralized onboarding campaign for the entire team, consisting of weekly lectures and culminating in a datathon. This improved
team culture as well as technical proficiency amongst new members.
Had the education team begin recruiting with other subteams to increase team size by threefold.
Placed 4th in kaggle competition amongst a class size of ~400 for CS 4780 - Intro to ML
Built the beginnings of a basketball sports betting system with some basic benchmark models here.
Lectured for the student run Intro to Data Science class - INFO 1998.
Freshman Year
Won a hackathon (Cornell Hospitality Hackathon sponsored by Hilton)
The solution consisted of a guest room assignment clustering algorithm in addition to additional
features on the Hilton Rewards App to better optimize room turnovers. This would have the benefits of
allowing guests to check-in earlier if they arrive before the hotel’s universal check-in time (potentially
even removing it). This also alleviates a lot of the physical ailments that cleaning staff often suffer
due to the incredibly fast turnover pace.
2nd Place in the Cornell Mathematical Competition for Modeling