I just graduated from NYU with a CS B.S. (and a Math minor), specializing in Computational Neuroscience. Because reading neural time-series data isn't painful enough, I do heavy software engineering and distributed systems work on the side.

When I have free time, I actively contribute to ray-project/ray or read big books that are hard to understand.

• Scalability over usability: If it doesn't require a distributed cluster to compute linear regression, I don't want it.
• 103% CPU usage: Maximizing process efficiency by making my local cooling fans sound like a jet engine.
• Running large jobs on login nodes: The ultimate life hack. Keeps my cluster fair-share (sshare) metric low while ruining the day for everyone else SSH'd into the cluster.
• AI force push and pray: Letting my AI fix my colleagues AI slop code.
• Fudging ML accuracy at all costs: Tuning is expensive, so I optimize performance by seamlessly leaking my training data directly into my test validation loop.
• Always merging local fixes: If it works on my machine's specific hardcoded path layout, it's ready for production.
• Stating "STEM is dead": Considering to switch to painting, but still finishing my math problem sets and manually debugging dependencies until 3:00 AM.

• [In Review] #63547 Docs: Python Dependency Guide: Added a developer guide mapping Ray's 3-layer dependency graph, uv conflict resolution workflows, and cross-platform architecture edge cases.
• [Merged] #60522 Modernize AxSearch API to 1.x: Upgraded core tuning infrastructure for ax-platform 1.0+ compatibility and strict error handling.
• [Closed] #62596 Split ci_docgpu CPU/GPU depsets: Restructured dependency lockfiles to isolate and resolve complex pip-compile version clashes (+pt27cpu vs +pt27cu128).
• [Closed] #62471 Fix Conda PermissionError on Windows: Isolated container-build race conditions caused by in-place conda update behavior during runtime cleanup.

• [Under Review / Anonymous Draft] Evaluation Traps in EEG Disease Classification: Identity Leakage, Lucky Folds, and Objective Mismatch Replicated Across AD, FTD, MDD, and SCZ: (Currently under double-blind review for NeurIPS; preprint server migration pending). Evaluated distributed optimization profiles across multiple neural datasets. Implemented a hybrid PySpark $\to$ Ray Core batch data pipeline to isolate and execute thousands of independent, iterative machine learning runs.

• 🌍 2 Ways of Analyzing Geographic Culture Through X API v2 + British LLM [Award Winner]: An award-winning architectural blueprint on scraping, filtering, and passing regional social telemetry for culture mapping.
• 🐳 How You Can Make PySpark Work Across Docker, Singularity, and HPC: A deployment manual on bridging heavy enterprise JVM ETL layers across containers and environments (especially for high-performance computing clusters).
• 💻 Fastest Guide to macOS Terminal Setup: Autocomplete, Aliases & Colors: A no-nonsense guide for optimizing Zsh workflows, setting robust aliases, and establishing terminal visual structure.