Graduated magna cum laude, with highest honors in field.
- TF for CS 153 (Compilers), Fall 2021 — Certificate of Distinction in Teaching
- TA for Summer Geometry Institute (SGI), Summer 2021
- TF for CS 161 (Operating Systems), Spring 2021 — Certificate of Distinction in Teaching
- TF for CS 182 (Artificial Intelligence), Fall 2020
- TF for CS 124 (Data Structures and Algorithms), Spring 2020 — Commendation for Extraordinary Teaching
Graduate-level technical coursework:
- Advanced Computational Complexity (CS 221)
- Random Processes and Algorithms (CS 223)
- Computational Learning Theory (CS 228)
- Advanced Computer Networks (CS 243)
- Advanced Topics in PL/AI (CS 252r)
- Advanced Topics in PL Design (CS 252r)
- Systems Security (CS 263)
- Distributed Systems Engineering (MIT 6.824)
- Shape Analysis (MIT 6.838)
- Robotic Manipulation (MIT 6.843)
- Probability (Stat 210)
- Statistical Inference (Stat 211)
Undergraduate technical coursework:
- Compilers (CS 153)
- Operating Systems (CS 161)
- Computer Graphics (MIT 6.837)
- Nanotechnology Fabrication (MIT 6.2540)
- Chaotic Dynamical Systems (Math 118r)
- Number Fields (Math 129)
- Differential Topology (Math 132)
- Honors Abstract Algebra (Math 55a)
- Honors Real and Complex Analysis (Math 55b)
Founding Engineer at Modal Labs
February 2022 – Current • New York, NY
- Left school to work on serverless compute tools in NYC for 7 months. After returning to school, I'm still working at Modal part-time.
- Led design and development for our serverless container runtime in Rust. A core component of this was a custom FUSE file system that allows multi-GiB containers to startup in seconds, up to 15x faster than with our previous setup, at ~80% lower cloud bandwidth cost. Mentored other developers who worked on this.
- Deployed infrastructure in Pulumi and Kubernetes. Advocated for, designed, monitored, and optimized a content-addressed file server and tiered cache in Rust handling peak 300 GB/min network throughput, with sub-200µs p99 latency and 99.995% uptime over 6 months.
- Headed up the modal.com website as lead frontend developer and designer. Made the design system from scratch, led UI discussions, and developed a foundation in SvelteKit for real-time user dashboards, documentation, and visualization. We have hundreds of users tracking billions of function invocations on our web interface. Mentored others in web development.
- Made over 700 pull requests, reviewed over 400 pull requests. Wrote most of our core developer environment scripts. Wrangled inconsistent docs, slow CI builds, race conditions, and backend performance problems. Fixed a lot of bugs and production issues across the board. So many bugs.
Contract Software Engineer at Prosper Robotics
January 2022 • London, UK
- Early-stage startup developing VR-teleoperated household robots. Worked with electrical and mechanical engineers.
- I wrote real-time (<10 μs) embedded microcontrollers in C++, developed new network services and robot control systems in Go. Refactored a lot of code, fixed many bugs, ported software to a new robot, introduced continuous integration, tests, static type checking for Python.
Software Engineer at Convex
June 2021 – October 2021 • San Francisco, CA
- Wrote significant components of the MVP. As an early core team member, this included rearchitecting the entire Convex client library, writing several systems components in Rust, and advising on developer experience.
Quantitative Research Intern at Jump Trading
June 2021 – August 2021 • Chicago, IL
- Won the intern trading strategies competition and rotated on a trading team to research algorithmic signals in US equities.
- Developed decentralized apps on the Ethereum blockchain for the cryptocurrency team, including new trading algorithms on DEXs. Contributed to the open source Ethereum ecosystem in Rust, Go, and Python.
Software Engineering Intern at Scale AI
December 2020 – January 2021 • San Francisco, CA
- Worked on machine learning infrastructure for a fast-growing startup using Terraform, Kubernetes, and AWS.
- Developed an in-house system to unify the ML training workflow, allowing the team to quickly iterate on models, share results on a tracking server, and launch Kubernetes distributed training jobs, saving “hours” of time on each experiment.
Architecture Intern at Nvidia
June 2020 – August 2020 • Santa Clara, CA
- Worked with the Applied Deep Learning Research (ADLR) group on deep learning models for street image segmentation.
- Individually developed and released FastSeg, an open-source library containing state-of-the-art PyTorch implementations of MobileNetV3 LR-ASPP for real-time semantic segmentation, significantly more accurate than existing public implementations.
Research Assistant at Harvard Programming Languages Group
June 2020 – August 2020 • Cambridge, MA
Undergraduate Researcher at Predictive Medicine Group
December 2019 – June 2020 • Boston, MA
Computer Science Instructor at AlphaStar Academy
December 2017 – April 2020 • Santa Clara, CA
Awards and Honors
- International Olympiad in Informatics (IOI): Gold Medalist (2018, 2019), 7th place globally
- Google Hash Code: World Finalist (2020, 2021, 2022), 6th place team out of over 100,000 students and professionals
- Google Code Jam: Round 3 Finalist (2020), placed 34th globally
- Facebook Hacker Cup: Round 3 Finalist (2020)
- PicoCTF: Winner (2018), ranked 6th
- Mathematical Olympiad Program (MOP): Participant (2017), IMO team selection group (2017, 2018)
- USA Math Olympiad (USAMO): Honorable Mention (2017), top 24 in nation
- U.S. Physics Team: Invitee (2017, 2018, 2019)
- USA Physics Olympiad (USAPhO): Gold Medalist (2017, 2018, 2019)
- National YoungArts Foundation: Winner in Classical Music (2018, 2019)
- JDR Viola Competition: Grand prize winner, performed with a professional orchestra (2019)
- Texas All-State Symphony Orchestra: Principal Violist (2017), Co-Principal (2016, 2018)