Founding Engineer at Starboard · MS Computer Engineering, NYU
I’m a founding engineer with an MS in Computer Engineering from NYU, specializing in AI systems, multi-agent architectures, and applied machine learning. I build production-grade AI — from fine-tuning LLMs to deploying autonomous agent systems that handle real-world logistics and data-processing problems.
Away from the keyboard: competitive contract bridge and poker, and an interest in strategy games.
Coursework — Quantum Computing, Machine Learning, Deep Learning, Computer System Architecture, Network Security, Mathematics, Physics, Finance.
Coursework — Advanced Mathematics & Statistics, Advanced Algorithms, Parallel Programming, Formal Language & Automata Theory, Computer Networks.
Multi-agent systems with tool use that autonomously process shipment tracking, monitor carrier portals, and reconcile data across logistics platforms. Fine-tuned open-source LLMs for document parsing.
Hybrid quantum–classical neural network estimating six-year lung-cancer risk from a single low-dose CT scan.
Built and scaled backend archival systems for organizations and individuals with Python, Django, and PostgreSQL.
Built ELMS, an exposure and limit-management engine covering 35M+ customers, and resolved production issues in the core Loan Management System.
A Dueling Double DQN trained with a curriculum of rule-based opponents, plus an exact alpha-beta solve proving the game is a first-player win from all 27 openings.
A red-teaming entry that searches tool-using LLM agents (gpt-oss, gemma) for exfiltration chains — a 73.88 public score on Kaggle’s AI Agent Security challenge.
Decentralized, end-to-end encrypted Web3 messaging with token-gated communities and in-chat crypto payments.
An MCP-compliant Chrome sidebar that stays open across tabs and answers questions about the page you’re on.
Hybrid quantum–classical network estimating six-year lung-cancer risk from a single low-dose CT scan.
Three procedurally generated polycube packing puzzles: 2D tiling, 3D lattice, and a Tetris-style mode.
Self-contained RAG assistant over lecture notes to make professors’ office hours more productive.
Calculates exposure across products for 35M+ customers and flags breaches to the lending system.
FinBERT and Llama read Reddit sentiment; a Random Forest on top forecasts GameStop’s price direction (accuracy 0.59).
A paired-contract trade on the 2026 IPL champion.
The market priced the two top seeds like a coin flip (0.75 combined) while the base rate for a top-two seed winning sat at 93.3%. A paired RCB + GT position on Kalshi captured the gap — positive expected value, returning ~27.7%.
Exact game solving meets deep reinforcement learning.
An exhaustive alpha-beta solver proves TicTacPro is a first-player win from every one of its 27 openings (700M nodes, 246s) — refuting the opposite conclusion that time-limited minimax self-play had confidently converged on. A Dueling Double DQN (792K parameters) trained with a curriculum of rule-based opponents reaches 100% against random and bullseye opponents, with training-pipeline pitfalls reported as negative results.
A Kaggle red-teaming competition; searched tool-using LLM agents for exfiltration chains, reaching a 73.88 public score.
An Acorns alternative with far more control over risk appetite and investment types.
chromeControl, an MCP-compliant Chrome AI assistant (see Projects).
Squaris, a spatial packing puzzle game.
Attempted the BlueQubit problem of Peaked Circuits.
Financial modeling for max profit — algorithmic and manual trading tracks.
A RAG/LangChain chatbot helping students grasp hard-to-find topics.
Bridge Represented NYU in contract bridge — intercollegiate tournaments, scrimmages, and the North American Bridge Championships (Philadelphia, 2025).
Poker Represented NYU at the Intercollegiate Poker Association annual tournament, 2025.
Clubs Bridge & Spades, SASE, IEEE, soccer intramurals (5v5, 9v9).