I'm a Data Scientist with an MS from NYU (3.96 GPA) and 4+ years of industry experience. My coursework covers machine learning, deep learning, causal inference, big data, NLP, probabilistic modeling, and optimization.

At Northeastern's Gyori Lab, I lead a research project on ontology alignment using representational learning and semantic search, with a paper accepted at ACM-BCB 2026 and a Python package released under DARPA's MapNet framework. I also have a CVPR 2026 submission where I distilled NASA's 300M-parameter foundation model down to 25M and the student outperformed the teacher on change detection.

At NYU's Urban Politics Lab, I run causal studies on urban policy. One used regression discontinuity to show congestion pricing cut noise complaints by 70%, another is a first-of-its-kind quasi-experimental analysis of board-ups and crime across 6K+ properties.

As a Research Assistant at NYU, I worked on quantifying 45 years of sexual harassment coverage trends in the New York Times across 8,900 articles, building reproducible analysis and visualizations for a non-technical audience.

Before grad school, I spent 4 years at Sunshine Marketing Agencies where I set up the company's first analytics function, built ETL pipelines and forecasting systems, and scaled the team to 5 analysts. That work supported 5.25x revenue growth, reduced product returns by 33% across 700 SKUs, and I built a full-stack order management system in MERN/Firebase. At TCS before that, I automated testing and migration workflows via PowerShell, cutting delivery time by 35% and earning a Star Team Award.

On the project side, I've deployed a multi-agent SEC filing analyst with LangGraph and Docker Compose, a fraud detection pipeline as a FastAPI endpoint, and a recommendation system on 27M+ ratings in PySpark. I work across Python, R, SQL, PySpark, PyTorch, Docker, and GCP.