RésuméDownload

• Bachelor of Science in Electrical & Computer Engineering, GPA 3.75 / 4.00. Expected graduation: December 2028.
• Coursework: CS 4414 (Systems Programming), ECE 3140 (Embedded Systems), ECE 2300 (Digital Logic and Computer Organization), MATH 2940, PHYS 2213, MATH 2930.

• Owned and shipped an OpenAI-compatible Batch API end-to-end (async worker draining against GPU capacity, per-request billing, DB schema, SDK, Helm deployment), backed by ~7,000 lines of contract tests; integrated Pure Storage FlashBlade S3 results storage for large inference artifacts.
• Benchmarked frontier LLMs before production serving: Poisson-distributed concurrency sweeps measuring time-to-first-token, inter-token latency, end-to-end latency, pre-emption, and throughput at p50/p95/p99 across multi-GPU HGX deployments.
• Owned the gateway load-management layer (priority scheduling, per-model streaming timeouts/keepalive, per-key concurrency limits, token-budget admission control gating batch work on GPU memory pressure) on NVIDIA B200/B300 GPUs.
• Brought a novel text-to-image model through full production deployment (GPU configuration, gateway routing, bounded concurrency, per-image pricing, UI) and built the platform content-safety layer.
• Merged 133 PRs across 10 repos (Rust, TypeScript, Python, SQL, Helm); unified two divergent agent execution paths, deleting ~6,000 lines of legacy code.

• Led an 8-person engineering team building an ESG risk monitoring platform for Investcorp ($60B+ AUM).
• Implemented a vendor due-diligence agentic pipeline with fintech company QuickFi.
• Collected vendor data from government websites, regulatory filings, and public databases; reconciled it into consolidated profiles and generated written due-diligence reports.

• Built the evaluation pipeline behind "Open Weight, Open Risk" (co-author): 15 frontier open-weight models across 800 WMDP-derived adversarial bio/chem requests, run locally on B200-class GPUs with vLLM and SGLang.
• Co-designed the training-free "inception" attack, in which a small uncensored 7B architect model pivots target reasoning and lifts compliance from a ~10% baseline to >95% under 5 iterations, exposing "shallow alignment" in open-weight reasoning models, with a PhD researcher at the Cornell NLP Group.

• Analyzed immunological datasets with Dr. Amit Lakhanpal and generated protein embeddings using Meta's ESM model to support rheumatoid arthritis treatment research.

Agentic penetration testing platform that turns vulnerability findings into ready-to-merge GitHub PRs. / Best Developer Tool — Cornell AI Hackathon 2026

• Orchestrated security agents using Mastra and OpenRouter, with isolated execution via Blaxel Sandboxes
• Designed Slackbot and Datadog middleware for triggering scans and ingesting logs to surface findings

Training-free jailbreak in which an uncensored model steers the target's chain-of-thought. / Publication

• Achieves near-universal compliance across open-weight models (DeepSeek, Kimi, GLM, Qwen)
• Iterative prompt injection and LLM-as-judge pipelines to streamline evaluation
• Evaluated 15 frontier models across 800 WMDP-derived biosecurity and chemical security requests

Moderated debate platform with fact-checking pipelines and an LLM-driven consensus tracker (Anthropic API, Supabase, Vercel/Railway). / Source

• Engineered fact-checking pipelines with web data normalization, preventing hallucinations in consensus

Pluggable PyTorch interface with automatic optimization/profiling and configurable multi-accelerator support, reducing boilerplate in ML research. / Source

• Built Dispatch, an agentic penetration testing platform automating vulnerability detection and remediation (Pitch Deck · GitHub)
• Developed integrations with Datadog, Slack, and Linear for seamless integration with developer workflows