Nathan's Notes
Stanford undergraduate that has been doing machine learning since middle school. Interested in implementing machine learning models from scratch, developing creative hackathon projects, and speeding up model training/inference with FSDP, quantization methods, and low-level architecture design. Check out my blog, Github, LinkedIn, or shoot me an email.
Work History
K-Scale Labs
May 2024 – Present
- Developed quantized OpenVLA for the open-source robot “Stompy” using bitsandbytes, focusing on CUDA integration.
- Led PPO and physics simulator development with MJX and JAX, exporting Flax models with Tensorflow.js for inference.
- Integrated quantization methods into PyTorch by coding at the Python/C++ interface with Pybind.
Stanford University School of Medicine
Sep 2023 - Present
- Developed deep learning models with Pytorch and Weights & Biases to identify Right Ventricular insertion points for AHA Segmentation model on cDTI MRI images.
- Created an annotation system using AWS S3 for efficient data management.
- Competed in lab’s team for MICCAI 2024, focusing on MRI image reconstruction across various sampling rates, distributions, and contrasts.
Jane Street
Mar 2024
- Achieved 1st place in an intensive quantitative trading program by developing high-frequency arbitrage trading bots.
- Conducted manual arbitrage strategies with ETFs to identify real-time opportunities and maximize returns.
- Built a deep understanding of arbitrage mechanics and market dynamics in a live-trading environment.
University of Delaware
Feb 2021 - Dec 2022
- Led an independent investigation on phosphorene’s anisotropic properties, publishing results on ChemRxiv and presenting at conferences including but not limited to the American Physics Society and Society of Engineering Science.
- Conducted quantum simulations (LAMMPS, SIESTA) and analyzed DFT data, modeling stress-strain responses and identifying novel anisotropic material applications.
- Published conference proceedings, demonstrating applications in flexible electronics, catalysis, and fracture-resistant materials.
The Summer Science Program
Jun 2022 - Jul 2022
- Designed procedures and characterized UmCdc14 phosphatase protein using wet-lab methods and MOE drug-discovery software.
- Conducted homolog modeling, ligand docking, BLAST sequencing, and other computational techniques for enzyme inhibitor design.
- Published data into Purdue’s research repository after writing a comprehensive report.
Education
Stanford University, School of Engineering
Sep. 2023 – May 2026
- Relevant Coursework: Computer Organization & Systems, Polya Problem Solving Seminar, Modern Mathematics: Discrete Methods, Introduction to Probability Theory, Applied Matrix Theory, Machine Learning, Design and Analysis of Algorithms, Deep Learning for Computer Vision, Natural Language Processing with Deep Learning
- Clubs: Stanford Daily, BASES, Undergraduate Research Program, Student Robotics Club
- Research Conferences: American Physics Society March Meeting, MIT Undergraduate Technology Research Conference, Society of Engineering Science Annual Technical Meeting, Sigma Xi Student Research Conference
Skills
- Languages: Python, Typescript, C/C++, Java, JavaScript, Swift
- Frameworks: React, React Native, Django, NodeJS, Pytorch, TensorFlow
- Technologies: Firebase/Firestore, GCP, AWS, Docker, Git, Redis, Apache Kafka, Electron, XCode, Android Studio, CUDA
- Awards: Google Company Challenge Winner @ LAHacks, Telemedicine & Health Winner @ HopHacks 2024, 3x AIME Qualifier, 2x USNCO Finalist (National Top 50), USACO Gold (International Top 2000), 2x USABO Semifinalist (National Top 10%), Coca-Cola Scholarship Finalist