Returned as TA (Prof. Matthew Gormley and Prof. Henry Chai) for 10-601, CMU's flagship ML course offered by MLD. Writing practice problems and updating the latter sections of the course to reflect the current trends in literature. Enrolled by 487 students (one of the largest in CMU/SCS).

Course TA for 10-601 (Prof. Matthew Gormley). Worked on exam/homework prep and automating MOSS plagiarism detection tasks. Includes holding course recitations and Office hours for 392 students.

Addressed object understanding problems in the perception long-tail by leveraging large foundational vision-language models (VLMs) to outperform production models

Implemented 3D Point Cloud Transformer (PCT) in JAX, achieving XX F1-score for mission-critical objects

Extended Google research’s internal VLM with PCT and implemented distributed multi-pod TPU fine-tuning of the fused 5.3B parameter model with T5x backend

Presented within the broader perception team and garnered positive feedback for the whole project. Also recognized as part of the 2023 Intern spotlight: https://youtu.be/QPVlRHZFZog?t=7

Mentored 2 interns on self-supervised domain adaptation for detecting cell organelle in 3D tomogram slices.

Applied ML to CLM problems in the legal tech space (more info below).

Engineered end-to-end ML flow cycle [data engineering - model training loop - serving deployment - online maintenance] using a microservices architecture.

As the resident CV expert, solved doc analysis and text typography research problems (similar to ones stated below) using hypothesis testing and weak supervision to handle OOD inference.