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pjavanrood/README.md

Hi there, I'm Parshan Javanrood! πŸ‘‹

Welcome to my GitHub profile!


  • πŸŽ“ Education:

    • BASc in Computer Engineering at the University of British Columbia
  • πŸ’‘ Technical Interests:

    • πŸš€ Machine Learning: Designing efficient pipelines, running large-scale distributed training, and exploring model optimization techniques.
    • ☁️ Cloud Computing: Building scalable and sustainable cloud infrastructures, with a focus on carbon-aware systems.
    • πŸ”— Distributed Systems: Implementing fault-tolerant systems, and exploring scalable database design.

πŸ’Ό Experience

  • 🏦 Quantitative Developer Intern at Squarepoint Capital
    • Built end-to-end automated solution for anomaly detection and cleaning of historical data.
    • Developed a real-time Streamlit dashboard for monitoring data quality.
  • πŸ€– Machine Learning Engineer Intern at RBC Borealis AI
    • Developed back-end services for ML models using FastAPI and PostgreSQL.
    • Implemented monitoring dashboards with Grafana and custom metrics using Prometheus.
    • Designed a generic training pipeline using Dagster, dbt, and ClickHouse.
  • 🌱 Research Assistant at UBC Cloud and Infrastructure Lab
    • Worked on Caribou, a project to reduce the carbon footprint of serverless applications.
    • Built a non-linear optimizer for UnFaasener, to reduce serverless billing costs.
  • 🌐 Prev Intern @ Arista Networks and NETINT Technologies

πŸ’» Course & Personal Projects

  • πŸ” StudyBuddy: A messaging app built with Node.js and MongoDB, featuring live chat, and a RAG-based study assistant powered by ChromaDB and ChatGPT-3.5
  • πŸ—ΊοΈ MapReduce in Go: Implemented a simplified version of MapReduce inspired by the Google MapReduce paper.
  • πŸ“¦ Distributed Key-Value Store: Developed a CRUD-capable key-value store in Java using gRPC and Protobuf.
  • πŸ”’ OS/161 Kernel Build: Built synchronization primitives, system calls, and virtual memory systems from scratch.

🌈 Interests

  • πŸš€ Machine Learning Pipelines & Optimized Systems for AI Workloads
  • πŸ–₯️ Building sustainable, carbon-aware infrastructure
  • πŸ“Š Balancing performance trade-offs in distributed systems
  • 🌟 Exploring new frameworks and contributing to open source

πŸ“« Get in Touch


✨ Let's build something awesome together! ✨

Pinned Loading

  1. go-MapReduce go-MapReduce Public

    Go

  2. KVDatabase KVDatabase Public

    Java

  3. SmartHomeAssistant SmartHomeAssistant Public

    CSS

  4. ubc-cirrus-lab/unfaasener ubc-cirrus-lab/unfaasener Public

    A lightweight framework that enables serverless users to reduce their bills by harvesting non-serverless compute resources such as their VMs, on-premise servers, or personal computers.

    Python 29 6

  5. ubc-cirrus-lab/caribou ubc-cirrus-lab/caribou Public

    Caribou is a framework for geo-distributed deployment of serverless workflows to save carbon emissions.

    Python 7

  6. StudyBuddy StudyBuddy Public