Ethan Cai · Resume
Email: ethan_cai@berkeley.edu
Portfolio: https://ethancai.com/
LinkedIn: https://www.linkedin.com/in/ethan-cai-25a775314/
GitHub: https://github.com/vitgruib
B.S. Electrical Engineering and Computer Sciences (EECS)
Prior coursework: SDSU Calculus III, Linear Algebra (4.0); UCSD Extended Studies: Deep Neural Networks (4.0).
• Languages: Python, TypeScript, JavaScript, Java, C++, C#, HTML/CSS
• Frameworks & Tools: PyTorch, scikit-learn, pandas, NumPy, CleanRL, React, Node.js, Firebase, Unity, Azure, GitHub Actions
• Foundations: Machine Learning, Reinforcement Learning (PPO, curriculum learning), Large Language Models (LLMs), LLM Evaluation, Web Scraping, Statistical Analysis, Data Structures & Algorithms
Cai, E.; Kouznetsova, V.L.; Tsigelny, I.F. Metabolomics-Based Machine Learning Diagnostics of Post-Acute Sequelae of SARS-CoV-2 Infection. Metabolites 2025, 15(12), 801. doi.org/10.3390/metabo15120801
Research Apprentice
• Post-training an open-weight LLM with PPO for clinical history summarization: adapting and tuning a reliability-monitoring framework into an LLM-as-a-judge whose scores are the reward, and validating the final results against UCSF radiologists' assessments.
Research Intern | Code: https://github.com/vitgruib/SIPACL
• Implemented prioritized level replay on CleanRL's PPO in Scenic/MetaDrive highway simulations, with a disk-backed replay buffer and rank-based resampling; replay-trained agents beat baseline PPO in 21 of 30 seeds (sign test, p < 0.05).
Research Intern | Code: https://github.com/Ethanc143/Metabolomics-Based-Machine-Learning-Diagnostics-of-Post-Acute-Sequelae-of-SARS-CoV-2-Infection
• Built an assay-agnostic Long COVID diagnostic from metabolomic profiles; a multi-layer perceptron classifier reached 0.899 AUC-ROC and separated Long COVID from four of five clinically similar conditions (ME/CFS, Lyme disease, POTS, IBS).
Machine Learning Engineer
• Built a golf-score prediction model for FanSlices, a fantasy sports betting platform, and an internal HTML/CSS/JS dashboard to test, compare, and visualize it.
Python, TypeScript | Code: https://github.com/jadenvv/Vigil
• Won the grand prize (Bright Data track) at the Talk to Users Hackathon (SF Tech Week, 20 selected teams) with a 12-hour build of an LLM scanner that flags misleading SEC Marketing Rule claims in a firm's marketing.
• Led the project: defined the problem and pitch, ran live interviews with target users and turned their feedback into product changes, and ramped up on SEC marketing compliance mid-event.
• Took over and rebuilt from scratch a two-pass, cross-referenced inference pipeline and a harness testing it against a one-prompt LLM wrapper (F1 0.97 vs. 0.57, 4 vs. 111 wrong flags, 125 cases); added 24 cases from 12 SEC orders fining advisers.
Unity, C#, Firebase | Code: https://github.com/Ethanc143/Human-Benchmark-3D
• Built and published a VR block-tapping memory test on SideQuest (1,800+ downloads), with a Firebase backend that logs performance and benchmarks players by age bracket. Founded and led a 20+ member VR Club.
TypeScript, React, SpacetimeDB, Claude, Azure | Code: https://github.com/vitgruib/CalHacks13.0
• Designed and built an AI grant office (Cal Hacks 13.0) automating a lab's tedious checks against obscure funding rules (no supplies in a grant's last 90 days) and conflicting official records; built its database backend, React front end, evaluation and GitHub Actions CI/CD to Azure; caught 38 of 38 freezes on 37 NIH grants and 28 of 28 audited unallowable items.
• Talk to Users Hackathon, Grand Prize (Bright Data Track, 2026)
• USACO Gold (2024)
• AIME Qualifier, 3 years (2023-2025)
• Games for Change Winner (2024)
• National Merit Scholar (2026)
Plain-text version: /resume.md