yameen sekandari
cs + ee at stanford university

about me

i build end to end systems that take messy inputs—data, sensors, documents, recordings—and turn them into usable tooling.

i focus on pipelines, dashboards, and product workflows that stay reliable under real constraints.

experience

software and data intern

forum mobility

june 2025 – september 2025 · san francisco, ca

  • built nightly etl enriching 5,000+ fleet accounts with esg, compliance, and grant eligibility; synced outputs into salesforce
  • productized enrichment into routing and filters; drove 35% more qualified accounts weekly for outbound workflows
  • launched an auto-refresh incentives database tracking 300 climate grants via scraping with state and deadline search

software engineer

stanford solar car project

october 2024 – present · stanford, ca

  • shipped real-time telemetry and pit monitoring used for race strategy; helped azimuth place 2nd in sov at fsgp
  • instrumented race operations for sustained performance across 24 hours; enabled data-driven pace decisions
  • integrated powertrain and energy telemetry for a 2-motor bldc platform; surfaced actionable testing signals

finance software intern

takenaka partners

march 2025 – june 2025 · los angeles, ca

  • shipped a sql-backed analytics platform for 500 japanese m&a deals; accelerated ownership lookups for pitches
  • built a react dashboard standardizing comparable financial analysis; reduced validation issues by 80%
  • re-architected capital iq ingestion using dbt and postgres materialized views; improved query latency by 70%

undergraduate researcher

stanford pincs lab

august 2025 – present · stanford, ca

  • improved ui reliability through aria roles, keyboard navigation, and bounded sliders to prevent invalid states
  • authored a quick-start guide to speed instructor setup and reduce classroom friction on low-spec lab machines

recent work

rondo: ai music teacher for classical performance

python, fastapi, docker, midi, musicxml

a pipeline that ingests a score and recording, aligns performance through machine learning, flags mistakes, and recommends targeted drills.

see project

trump dump: market signal pipeline

typescript, aws, sqlite, selenium

real time ingestion and scoring that turns public statements into a 0 to 100 signal, classified into market verticals and mapped to likely tickers.

see project

daily apple: news gamification

swift, swiftui, spritekit, combine

a mobile app with quizzes and mini games, built around predictable state flow.

see project

get in touch

always open to interesting conversations and collaborations.

yameens [at] stanford [dot] edu