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 REST Batch API end-to-end on cloud-native Kubernetes infrastructure: async GPU workers, exactly-once billing for consistency, DB schema, SDK, Helm deployment, ~7,000 lines of tests.
• Benchmarked frontier LLMs before production serving: Poisson-distributed concurrency sweeps measuring time-to-first-token, inter-token latency, and throughput at p50/p95/p99 across multi-GPU HGX deployments.
• Owned LLM inference gateway load management on NVIDIA B200/B300 GPUs: priority scheduling so batch cannot starve interactive chat, per-model streaming timeouts, and token-budget admission control on GPU memory pressure.
• Deployed a novel text-to-image model to production (GPU config, gateway routing, bounded concurrency, pricing, UI) and built the content-safety guardrail layer: a moderation API and an actionable error taxonomy for opaque refusals.
• Merged 133 PRs across 10 repos in Rust, TypeScript, Python, SQL, and Helm through GitHub Actions CI/CD, building distributed systems for scalability; unified two agent execution paths, deleting ~6,000 legacy lines.
• Shipped a nightly memory-consolidation engine, an automation builder driven by a backend tool-calling loop, chat streaming hardened against state bugs, and an inline co-editing canvas with conflict auto-resolution.

• Led an 8-person team on full-stack development of an ESG risk monitoring platform for Investcorp ($60B+ AUM).
• Built a vendor due-diligence agentic data pipeline with fintech company QuickFi, reconciling vendor data from filings.

• Built the AI security evaluation pipeline behind "Open Weight, Open Risk" (co-author): 15 frontier open-weight LLMs 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: a small uncensored 7B architect model pivots target reasoning, lifting compliance from ~10% to >95% in 5 iterations, exposing shallow alignment in open-weight reasoning models.

• Analyzed immunological datasets with Dr. Amit Lakhanpal (Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, VAEs, t-SNE) to extract rheumatoid arthritis signatures; fine-tuned neural networks, generated ESM embeddings.

Indoor AR navigation app piloted on a multi-story Cornell academic building. / 2nd Place — MLH Big Red Hacks 2026 · Write-up

• Mapped a multi-story Cornell academic building for routing (Swift, ARKit)
• Solved indoor positioning, where GPS is unreliable, with quick, precise localization from what the camera sees and no beacons or routers, via PyTorch/RoMa and Open3D on RunPod GPUs; added an ElevenLabs voice agent

On-premises AI maintenance agent that turns machinery fault signals into cited, verified work orders. / 2nd Place — Dell × NVIDIA AI Hackathon at Cornell 2026 · Write-up · Source

• Co-built the agent loop that turns fault signals into cited, verified work orders
• Served Qwen3.6-35B-A3B via llama.cpp on Grace Blackwell inside OpenShell; its egress policy denied exfiltration

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

• Built the backend of a multi-agent security platform: an orchestrator agent reads the target codebase (routes, auth logic, raw SQL), plans targeted attacks, and dispatches pentester workers into isolated Blaxel sandboxes
• Closed the loop from finding to fix: content-hash IDs deduplicate findings, constructor workers patch code and open GitHub PRs, and an Express REST API plus Slack/Linear/GitHub integrations trigger fixes and ticket 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, unbiased debate platform with organized layouts and AI summaries. / Source

• Engineered web-data fact-checking pipelines to prevent hallucinations and an LLM consensus tracker (Anthropic API)

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

• Built Marko, indoor AR navigation from a single phone with no beacons (Write-up · Website)

• Built Groundwork, an on-premises AI maintenance agent running entirely on a Dell Pro Max GB10 (Write-up · Website)

• 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