Accelerated CS @ Boston University, graduating a year early. I build and run production systems as the sole engineer — a degree planner used by 1,000+ students, and a nonprofit's entire ~300-endpoint platform — and I contribute to the LLM inference stack: vLLM, llm-d, KServe and the Kubernetes Gateway API Inference Extension.
CTO & Sole Engineer Honor Society of Cinematic Arts Multi-tenant platform · ~300-endpoint API · live across 20+ chapters in 9 countries & 250+ users portal |
SWE Intern BU Spark! Harvard Herbaria OCR confidence layer (FastAPI) · micro-credentialing platform · BU Spark!'s project showcase (sole author, live at atlas.buspark.io) |
SWE Intern Doro (Local Treasure) TikTok OAuth, Stripe checkout & moderation tooling (Django/DRF) getdoro.co |
Research Assistant BU CISS Financial markets × geopolitical uncertainty · Python data workflows |
Previously — AI & Software Engineer Intern @ HireBuddy · Lead Software Engineer @ Hack4Impact BU (incoming President, Dec 2026)
I contribute to vLLM, llm-d (a CNCF sandbox distributed LLM inference stack founded by Red Hat, Google Cloud, IBM Research, CoreWeave and NVIDIA), KServe, Grafana, Redis, Keras, and Red Hat's InstructLab. Also Prisma, MLflow, Datadog, SGLang and the Model Context Protocol registry. Same method everywhere: reproduce the failure with executed evidence before proposing the fix.
68 pull requests across 25 repositories in 18 organizations |
28 merged — including a fix in Grafana core shipping in 13.3.0, plus vLLM, KServe, redis-py, node-redis, Keras and the MCP registry |
9 defects found and filed against a newly released no-Kubernetes deployment path, 6 reproduced live with tracebacks |
19 reviews on other contributors' pull requests across 10 repositories |
Most of it is in llm-d-benchmark: I built the core of its Benchmarking Agent (deterministic recommendations, Kubernetes job rendering and SLO-goodput scoring, 2,500+ lines), then took its new no-Kubernetes deployment path from first run to working on a laptop with no cluster and no GPU: 13 merged PRs, including a fix for a container name collision that let one deployment's teardown destroy a sibling deployment's containers.
Reviewing counts too. On someone else's pull request I found that a proposed dependency pin would fail the project's own version comparison, so every install would re-run the installer and overwrite the user's kubectl binary. Proven by running the project's own function against the proposed value.
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Autonomous banks, central banks & payment rails (RTGS/ACH/SWIFT/CLS/DTCC), systemic-risk analysis, crisis replay & a hand-written regulatory DSL.
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Degree planning for 1,000+ BU students across nearly 10,000 courses. Requirement-validation engine, vector-embedding semantic recommender replacing a quota-limited LLM at zero marginal cost, Redis layer at ~81% hit rate over 660k+ lookups.
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The production system of record behind my CTO role — member lifecycle, payment-gated resources, evaluation forms & a film festival, with per-chapter tenant isolation.
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Built the AI candidate-sourcing engine (Elasticsearch-DSL over CoreSignal) — tripled candidates surfaced per search (100 → 300) — and co-built a GPT-4o-mini pre-screening engine scoring across 10 evidence-backed dimensions.
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OCR confidence-preservation layer for a specimen-digitization ML platform — a versioned per-field certainty envelope surfacing Azure vision-OCR to reviewers, plus SSRF + authorization hardening. Client-owned (BU Spark!) — no public repo |
Owned the SE-Chem badge-authoring wizard end-to-end — decomposed an 843-line page into components, killed a stale-closure bug, and batched Client-owned (BU Spark!) — no public repo |
🏆 InstructLab KnowFlow LangGraph + MCP agentic workflow automating InstructLab contributions — accepted for upstream evaluation. |
⚛️ Quantum Explorer Interactive 3D quantum-entanglement platform with AI-powered concept grading. |
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flowchart LR
UI["Frontend\nReact · Next.js · Three.js"] --> API["APIs\nFastAPI · Flask · Express · DRF"]
API --> AI["AI Layer\nLangGraph · MCP · embeddings · TF-IDF retrieval"]
API --> DATA[("Data\nPostgres · Redis · Elasticsearch")]
AI --> DATA
API --> INFRA["Infra\nDocker · AWS · Vercel · Railway"]
DATA --> INFRA
