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Achievement: QuickdrawAchievement: Pair ExtraordinaireAchievement: Pull Sharkx2Achievement: YOLO

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hamim23z/README.md

Hello! I'm Hamim Choudhury, a current senior at The City College of New York pursuing a major in Computer Science. 👋

  • 🤖 Graduating in December 2026 with a BS in Computer Science.
  • ⚡ Previous Systems Engineer Intern at Bloom Energy | Prev SWE at NYC DORIS | Prev Web Dev at NYC DDC.
  • 📚 My tech stack consists of Python, JS/TS, Pandas, Seaborn, Docker, XGBoost, and more.
  • 🌱 Currently learning more about Machine Learning (using models and different libraries/tools).
  • 📫 How to reach me: Email at hamimc232@gmail.com or my LinkedIn at https://www.linkedin.com/in/hamimc/

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  1. SpaceSync_v1 SpaceSync_v1 Public

    Forked from b-Karthikeya-reddy/Marl-ar-benchmark

    Multi-agent reinforcement learning system that benchmarks collaborative furniture placement in simulated AR rooms using specialized layout, style, and budget agents trained on real IKEA data.

    JavaScript

  2. SpaceSync_v2 SpaceSync_v2 Public

    A team of AI agents that plans furniture layouts to fit your room, match your style, and stay on budget. Tests whether specialist agents beat one do-it-all agent.

    JavaScript

  3. Ballistic Ballistic Public

    NBA 82-0 season simulator. Draft players from any era, with real stats from 1961 onward merged by franchise, and chase a perfect season. Built with Next.js, Tailwind and Supabase Postgres, using a …

    TypeScript

  4. Grid-Pulse Grid-Pulse Public

    An interactive data story exploring how the U.S. power grid's demand, generation mix, and net load change by hour, season, and year — with a two-period comparison view and statistically grounded tr…

  5. Sensor-Data-Quality Sensor-Data-Quality Public

    A declarative framework that scores sensor telemetry for the failures that quietly corrupt fleet analytics, and publishes a daily quality scorecard per system.

  6. Wherehouse Wherehouse Public

    An LLM agent that answers "where is this data?" over a SQL warehouse — locating tables and columns, explaining joins, and running the query safely if you ask.