Accelerated CS @ Boston University, graduating a year early. I contribute to llm-d and vLLM, where my job is finding the paths a new feature quietly fails on. Alongside that: a degree planner used by ~900 students, a nonprofit's entire 250+ endpoint platform, and a bounty board that puts real projects in front of any BU student.
CTO & Sole Engineer Honor Society of Cinematic Arts Multi-tenant platform · 250+ endpoint API · live across 15+ chapters & 150+ members |
SWE Intern BU Spark! Harvard Herbaria OCR confidence layer (FastAPI) + micro-credentialing platform |
SWE Intern Doro (Local Treasure) TikTok OAuth, Stripe checkout & moderation tooling (Django/DRF) |
Research Assistant BU CISS Financial markets × geopolitical uncertainty · Python data workflows |
Previously — AI & Software Engineer Intern @ HireBuddy · Lead Software Engineer @ Hack4Impact BU
I contribute to llm-d, a CNCF sandbox distributed LLM inference stack founded by Red Hat, Google Cloud, IBM Research, CoreWeave and NVIDIA, and to vLLM.
4 pull requests merged, reviewed and approved by two separate core maintainers including the project lead |
9 defects found and filed against a newly released no-Kubernetes deployment path, 6 reproduced live with tracebacks |
11 pull requests across the core, benchmark and documentation repositories |
2 code reviews given on other contributors' pull requests |
What I am actually useful for: running a brand-new feature on a machine its maintainers do not have, a clean macOS laptop with no cluster and no GPU, and finding the paths where it reports success while doing nothing. That produced nine issues against one feature, including a container name collision that let one deployment's teardown destroy a sibling deployment's running 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 silently 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 ~900 BU students across 6,000+ courses. Requirement-validation engine, vector-embedding semantic recommender replacing a quota-limited LLM at zero marginal cost, Redis layer at ~81% hit rate over 66k+ 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. |
🥈 Cognify AI course & career advisor over 6,000+ BU courses with Gemini recommendations & skill-gap analysis. |
⚛️ 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
