Education
Stanford University
Expected June 2027
Graduate Technical Coursework:
- Programming Languages (CS 242)
- Deep Learning for NLP (CS 224n)
- Machine Learning (CS 229)
- Reinforcement Learning (CS 234)
- ML with Graphs (CS 224w)
- Fundamentals of Blockchain Infrastructure (EE 374)
- Parallel Computing with CUDA/OpenMP/MPI (CME 213)
Undergraduate Technical Coursework:
- Parallel Computing (CS 149)
- Groups and Rings (MATH 120)
- Real Analysis (MATH 171)
- Linear Algebra and Matrix Theory (MATH 113)
- Operating Systems (CS 111)
- Algorithms (CS 161)
Work Experience
Software Engineering Intern at Stealth
Oct 2025 - Present • Mountain View, CA
- Working on stealth projects.
Researcher at Stanford NLP
Sep 2025 - Present • Stanford, CA
- Advised by Chris Potts & Zhengxuan Wu. Developed BoundBench and formalized the PRBO objective to measure & lower-bound steering techniques LLM behavior; combined concept-incorporation + distributional-shift metrics with IWAE-style, logit-based estimators for fast probability estimates without LLM judges.
- Designed a benchmarking plan across common steering methods (Rank-1 ReFT, activation patching, steering vectors, DiffMean, probes, SAEs, LoRA/FT), with criteria that elicit target behavior while preserving base-model propensities and linking scores to downstream tasks.
- Reference: BoundBench Presentation
Software Engineering Intern at Meta
June 2025 - Sep. 2025 • Menlo Park, CA
- Disaster Recovery Team. Leveraged Python and statistical analysis in a large-scale Linux environment to build an automated reporting system that quantitatively analyzed disaster recovery test outcomes using time-series analysis and pattern recognition techniques, saving dozens of weekly engineering hours through data-driven optimization.
- Architected a systematic data analysis pipeline to evaluate operational risk using statistical modeling approaches, implementing modules for time-series analysis of execution latency, error rate tracking with machine learning classification, and quantitative assessment of system failure events to inform data-driven engineering priorities.
- Deployed a mission-critical analytics engine into production, engineering a full CI/CD pipeline with automated model validation and scheduled job execution to ensure reliable, periodic delivery of quantitative risk insights to downstream systems—demonstrating experience with systematic, data-driven approaches to complex problem-solving.
AI/ML Engineer Intern at Biostate AI
Nov. 2024 - Mar. 2025 • Palo Alto, CA
- Developed and implemented an end-to-end ML pipeline utilizing bulk RNA-seq expression data from proprietary and public datasets to train a 100M+ parameter transformer model, achieving state-of-the-art performance in autoregressive generation of "future" RNAseqs with biologically viable expression patterns.
- Established comprehensive internal benchmarking protocols and implemented robust data tagging systems to prevent contamination during large model pretraining, while specializing and curating datasets for performance testing.
- Built and deployed an automated bioinformatics platform that integrated omics data analysis pipelines with fine-tuned LLMs with DPO, optimized for generating scientific abstracts and publication-quality figures, streamlining research workflows while maintaining 95% expert-rated accuracy.
Research Assistant at Stanford Autonomous Systems Laboratory (ASL)
Aug. 2024 - Mar. 2025 • Stanford, CA
- Student researcher working on autonomous systems for trajectory optimization and applying on-board VLMs/LLMs/CV for anomaly detection/reaction under Professor Marco Pavone (Director of Nvidia's Autonomous Systems Division).
- Engineered a unified software application for Gazebo/Robot Operating System 2 simulation integration with PX4, utilizing Nvidia Orin Jetson Nano and motion-capture Pub-Sub model for real-time autonomous system testing.
- Developed and implemented trajectory optimization and obstacle avoidance algorithms for kinodynamic motion planning in an indoor environment as part of a 3-person team. Project demo can be found here.
Data Science Intern at Air Force Research Laboratory (AFRL)
June 2023 - Sep. 2023 • Dayton, Ohio
- Designed and implemented a novel group-theoretic MCMC algorithm that significantly improved sampling efficiency for systems with discrete symmetries, demonstrated through application to dielectric polymers.
- Created clustering algorithms that achieved up to 50% faster convergence compared to standard and umbrella sampling methods by leveraging symmetry properties in potential energy landscapes.
- Leveraged UMAP/t-SNE for feature extraction of MCMC data and built out PyTorch Autoencoder to detect polymer characteristic anomalies depending on reconstruction error, boosted detection accuracy by 15%. Read more here.