Activities

This page covers what sits between coursework and shipping: leadership, competitions, outreach, and the skills I keep stress-testing in real settings.

Leadership and community

IU Data Science Club — Head of Technology

I design and lead biweekly hands-on workshops for the club on data engineering, machine learning, and statistical analysis. Most of the job is turning a vague "can you teach us X" into a notebook people actually leave with — which, it turns out, is most of teaching.

Indiana University Student Government (Equity and Justice Committee)

Worked on policy and budget conversations where the hard part was alignment, not opinion volume. This made me better at reading stakeholders before proposing technical fixes.

Perplexity AI campus ambassador

Did practical outreach around responsible AI usage and generated 45+ qualified leads. More importantly, I learned how to translate model talk into language non-technical students actually use.

Machine Learning 4 All

Weekly build sessions and paired projects. Good forcing function for showing rough prototypes early instead of polishing in private for too long.

Competitions and hackathons

NCAA Final Four Analytics Challenge

Public leaderboard work with model iteration under deadline pressure. This one taught me to separate private confidence from public score movement.

Related post: /post/ncaa-seed-model-zones

LabLab AI Trading Agents — Captain Whiskers

An autonomous on-chain trading agent fusing variational-quantum portfolio optimization, Byzantine-fault-tolerant verification, and post-quantum signatures. The clearest case of the quant, HPC, and quantum threads meeting in one build under a hackathon clock.

Other hackathons

Earlier hackathon work spanned agentic RAG for document diligence and rapid prototyping under compressed constraints — useful reps in scope discipline and shipping rough systems that actually run.

Belden sales competition case work

Sales competition case-study materials are present in the desktop archive history from Fall 2025. This work sharpened structured problem framing, presentation under constraints, and making quantitative arguments understandable to business audiences.

Soft skills I actively practice

Technical communication under mixed audiences (students, faculty, builders, recruiters)
Scope negotiation when requirements and time both move
Team execution rhythm: ship, review, tighten, repeat
Leadership without title dependency
Translating uncertainty into testable next steps

Hard skills with proof links

HPC and systems measurement: BigRed200 cache characterization (/post/qhpc-cache-finance-kernels)
ML factor modeling: regularized regression and regime testing (/post/regimefactorzoo-sparse-factors-regimes)
ML-oriented engineering: model + pipeline iteration (/post/ncaa-seed-model-zones)
Product implementation: Chrome MV3 extension and UX flow (/post/ai-ethics-coach-shipped)
Quantum + optimization: variational quantum portfolio optimization (/post/captain-whiskers-quantum-trading-agent)

Why this matters for what comes next

I am building toward a PhD in quantitative finance. Activities like these are where I pressure-test whether my math and engineering stack holds up in messy, real conditions.

Navigate

Projects: /portfolio
Blog posts: /my-blog
Timeline: /timeline