brief background
Education: Robotics + Computer Science at University of Pennsylvania
Experience: Recommendation systems at Shoreline/Nvidia, Data Skipping at Databricks, and rust that goes brrr at Mooncake Labs
Research: Co-author Demo at SIGMOD 2025 on ScaleLLM, System Paper at NSDI 2025 on market-based microservice load handling, continued work in UPenn's systems + ML lab
industry
Mooncake Labs - Founding Engineer
#3 hire - stay tuned! data durability, recovery, connectors, talking to customers and more.
Databricks - Intern
Increased performance of one of our largest customers' pipelines by > 500% by helping to skip over large amount of data.
Also started the first young engineer tech talk series, with > 100 attendees for all the talks.
Shoreline -> Nvidia - Intern
Built a recommendation system for runbooks, achieved 95% search recall, explored synthetic benchmarks while they were still nascent.
research
ScaleLLM - SIGMOD 2025
Co-authored a demo at SIGMOD 2025 on ScaleLLM, a system that allows for scaling LLM usage to massive workloads on constrained tasks. Continiuing work on pushing the performance of tiny LMs (<1m params) to generate context-free grammars.
Rajomon - NSDI 2025
Worked on benchmarking microservice overload frameworks - gnarly Go code for distributed systems.
projects
pBFT - asynchronous distributed fault tolerance
Main contributor on a project bringing an asynchronous fault tolerance algorithm from a cyber-physical setting to a data center setting. Advised by Prof. Linh Phan.
Sketchy
WIP - A new interface for fine-grained image editing control through sketch + text diffusion models, with only a lightweight guidance adapter required. Continiuing work with UPenn's Computer Vision lab.