Work and projects by Shayan Poigai
[ CAPTAIN'S LOG ]
Experience
- Sept 2025 — June 2026
CS Teaching Assistant & Algorithms Grader
Santa Clara University
- Mentored 40+ students in C++ — memory management, pointers, and OOP — and debugged logic errors live using GDB.
- Graded Theory of Algorithms for 40+ students across 2 sections, evaluating runtime analysis, greedy, divide-and-conquer, and dynamic-programming solutions.
- Dec 2025 — June 2026
Machine Learning Research Assistant
Santa Clara University
- Spent six months studying what people actually think about LLMs as a therapeutic tool, not what they say in surveys, but what they say to each other, unprompted, across mental-health communities on Twitter, Reddit, and YouTube.
- The modeling wasn't the hard part. 5,000+ scraped posts are a mess of slang, typos, and sarcasm; most of my time went into Pandas preprocessing, normalizing text until BERTopic, LDA, and NMF could surface real structure instead of noise.
- Co-authored the paper as primary writer, which meant learning LaTeX in Overleaf, fighting table formatting until the columns behaved, and hunting down references — figuring out which prior work actually supported a claim rather than just sounding adjacent to it.
- Writing the paper forced a rigor the code never did. Producing a topic model is one thing; defending why the topics mean anything is another.
- Co-authored a short paper accepted to HIBIBI 2026 (collocated with IEEE/ACM ASONAM), to be published by Springer - the payoff for months of wrangling messy data and learning to write like a researcher.
- May 2026 — Present
Co-Founder & CTO / Lead Software Engineer
Adorus — Jewelry E-commerce Startup
- Adorus started as an idea between siblings. My sister and I co-founded it, split the work by what we're each good at, and agreed on one thing: build something we'd actually be proud to put our name on.
- Given my technical background, I took ownership of the entire engineering side. Every layer, data models, API contracts, component architecture, and deployment, is mine to design and be accountable for, which means there's no senior engineer to check my thinking. I've learned to be my own code reviewer.
- We seriously considered Shopify. Building it ourselves was cheaper and gave us full control over how the site works, so we went that way, a decision I own the consequences of every time something breaks at 2am.
- The stack: React.js/Node.js frontend, Java Spring Boot services, PostgreSQL, deployed on AWS EC2 with S3 and CloudFront serving product images through a private bucket. Most of the real work has been the unglamorous foundations: authentication, image pipelines, a schema that won't buckle when real orders arrive.
- The parts I couldn't build, we went and found. My sister and I reached out cold on social media to photographers and marketers working in the industry, asking people with real careers to help us. Learning to ask for help turned out to be a skill in itself.
- Next: building toward AI agents over an MCP server to give us a clearer read on customer behavior and satisfaction, so the site can adapt to how people actually use it rather than how we assumed they would.
- June 2026 — Present
Software Engineering Intern
HerbsPro
- Implementing AI backend features across the e-commerce stack.
Projects
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Disease Tracker
October 2025- FastAPI REST API serving real-time disease metrics and risk assessments with low-latency responses.
- AWS Bedrock drives an automated AI risk-scoring pipeline over public-health data.
- DynamoDB schemas designed for scalable ingestion and clean backend-to-frontend flow.
Built in a weekend at the AWS × INRIX 2025 hackathon — the Bedrock scoring layer is what turned a raw data feed into something that actually flags emerging hotspots.
Restaurant Agentic RAG Pipeline
July 2025- Modular RAG framework over Ollama + ChromaDB for semantic search across dynamic datasets.
- MCP (Model Context Protocol) server exposing local data as tools for AI agents via JSON-RPC.
- Extensible by design — new datasets drop in with minimal refactoring.
The surprise: retrieval quality lived or died on chunking, not the model. The first version confidently returned wrong passages until I shrank the chunks, added overlap, and layered in metadata filters.