Projects

U-M Vertical Flight is a multidisciplinary student design team I founded to compete in Vertical Flight Society design-build-fly competitions. As Founder & Electrical Lead, I own the full electrical stack — from power architecture to sensor integration — on a real flying aircraft.

• Founded the team from scratch; recruited a multidisciplinary group of engineers and secured $20k+ in funding for hardware, components, and competition entry.
• Designing and integrating the full electrical system: power distribution, flight controllers, ESC wiring, and sensor suites (LiDAR, thermal cameras) for an eVTOL UAV.
• Coordinating CAD in SolidWorks/Siemens NX across mechanical, electrical, and software sub-teams to meet VFS competition design specifications.
• Leading team operations: sprint planning, documentation, budget management, and stakeholder presentations to faculty advisors and industry sponsors.
• Competing in Vertical Flight Society Design-Build-Fly: a national-level engineering competition judged on technical design reports and live flight performance.

Co-authoring a research publication at U-M's Magnetometer Laboratory. The core problem: current classification requires a 48-hour observation window after a geomagnetic spike to confirm whether a storm follows — too slow for operational space weather forecasting. Our Random Forest model classifies the event type in real time using only ground-based Sym-H magnetometer features, removing dependence on L1 solar wind satellites (DSCOVR/ACE).

• Trained Random Forest classifiers (balanced + unbalanced) on 20 years of Ebre Observatory SC events (2006–2025) — 3.7:1 SSC-to-SI ratio spanning Solar Cycle 24 — achieving 80% recall on the minority class (Sudden Impulses).
• Engineered two physically-grounded features from 1-minute Sym-H data: 24-hour pre-event standard deviation (geomagnetic quietness) and 10-minute maximum amplitude jump (compression shock) — plus amplitude-to-variability ratio for the balanced model.
• Validated using Leave-One-Year-Out cross-validation across Solar Cycle 24 phases (maximum, rising, declining, minimum) to characterize minimum data requirements for operational deployment.
• Conducted multi-station superposed epoch analysis using SuperMAG ground magnetometer network to characterize SSC vs. SI magnetic field signatures by latitude — confirming the physical basis of the engineered features.
• Calibrating Helmholtz Coil apparatus for RM3100 magnetometer sensor testing, validating hardware reliability for spaceflight environments in the same lab.

Collaborating with Curtis Ling (MaxLinear founder & CTO) to build a non-profit platform that connects student researchers to funding and enables donors direct access to ongoing university research.

• Co-designing the platform architecture using Python, React, and modern web technologies to serve the University of Michigan research community.
• Building systems that enable transparent, direct funding pipelines between donors and student-led research projects.

Engineering a secure, self-hosted financial platform that automates transaction classification, integrates real-time data via Plaid API, and provides dynamic visualization through React—reducing manual accounting workflows by 70%.

• Architected Python backend services that ingest and classify financial events with explainable outcomes, processing thousands of transactions with actionable insights.
• Integrated Plaid API for real-time financial data ingestion; PostgreSQL for persistent storage; React for dynamic, interactive visualization.
• Built workflow automations reducing manual accounting time by 70% and providing real-time visibility into spending patterns.