About
I'm an engineer focused on machine learning, computer vision, and the backend systems that carry research into production. Right now I'm the founding engineer at FlowState AI, building enterprise video intelligence from the ground up.
Before that I built NeRF-based 3D reconstruction at MIT Lincoln Laboratory, multimodal models at Openstream.ai, and led the SLAM team for Carnegie Mellon's autonomous race car. I studied Information Systems with a minor in Artificial Intelligence at Carnegie Mellon.
Outside of work I play chess at a national level — ranked #48 in the U.S. for age 18.
Education
Carnegie Mellon University
B.S. Information Systems · Minor in Artificial Intelligence
GPA 3.8 · Dec 2024
Toolkit
Languages
Backend & Systems
AI & Multimodal
Infra & Cloud
Experience
Jan 2025 — Present
FlowState AI
Founding Engineer (Employee #1)
Building the backend and core platform for scalable video ingestion, retrieval, and agentic search — powering enterprise video intelligence across 10,000+ hours of content. Leading architecture, product execution, and early team building.
May 2024 — Jul 2024
Openstream.ai
Machine Learning Intern
Designed a multimodal fusion mechanism for temporally aligned audio and video, and trained 100+ models behind a personality-detection system that scores the five OCEAN traits from a live video feed.
Jun 2023 — Jul 2023
MIT Lincoln Laboratory
Machine Learning Intern
Implemented Neural Radiance Field methods (Instant-NGP, Nerfacto) to reconstruct photorealistic 3D models from sparse, low-quality 2D imagery — reaching 30+ PSNR while cutting training time roughly in half.
Jan 2023 — May 2023
CMU Mobility Privacy & Security Lab
Research Assistant
Built a Python/GCP web-crawling pipeline to extract and process metadata from 3M+ Google Play apps for large-scale privacy trend analysis.
Sep 2021 — Sep 2023
LeadershipCarnegie Autonomous Racing
Path Planning & SLAM Team Lead
Led a team of five building the car's GraphSLAM system (pose-graph optimization for mapping and localization) and debugged the path planner that completed the first autonomous lap in North American history at New Hampshire Motor Speedway.
Chess
TEDx Talk
Lessons from street chess
A talk on confidence, creativity, adaptability, and learning from unexpected teachers.
The throughline
Learning from messy systems, not just clean ones.
I have played competitive chess for nearly 15 years, but some of the most memorable lessons came far from formal tournaments: from street chess players in Chicago, Amsterdam, Zurich, and New York.
That experience shaped how I approach engineering. The systems I like building rarely start clean. They begin as noisy sensor streams, sparse images, messy webpages, or thousands of hours of unstructured video, and the work is turning that ambiguity into something people can reason with.
“Sometimes the best lessons come from the least expected teachers.”
The talk is about chess hustlers, but the idea carries into the rest of this site: intelligence often emerges from experience, improvisation, and imperfect information.
Selected work
Contact
Let's build something.
Always happy to talk about ML systems, video understanding, or an interesting problem. The fastest ways to reach me are email and LinkedIn.