About

I'm a student at Boston University pursuing a double major in Computer Science and Economics. I'm primarily interested in distributed systems, with my past internship and current research focusing on stateful, fault-tolerant stream processing engines. I'm also interested in distributed infrastructure for AI/ML systems.

In economics, I enjoy thinking about macroeconomic growth, productivity, and the aggregate impacts of technological change and automation.

In my free time, I'm likely playing or watching soccer (an Arsenal fan, sadly), strength training, or running.

Work

Experience

Grepr

SWE Intern • San Francisco, CA • Summer 2025

  • ●Assisted in building Grepr's new distributed tracing product, handling the query and storage layer for unprocessed traces while maintaining constraints in a distributed systems environment built with Apache Flink, Apache Iceberg, and a React/TypeScript frontend
  • ●Built both the OpenTelemetry log and trace integrations, implementing efficient serialization/deserialization with minimal garbage collection, sustaining 10,000+ records/sec across a Kubernetes-orchestrated AWS infrastructure, ensuring low latency and fault tolerance at scale
  • ●Wrote and executed database migration scripts on production clusters, ensuring schema consistency and safe rollouts
  • ●Built a comprehensive integration test suite in JUnit validating interoperability with Datadog, Splunk, OpenTelemetry, Sumo Logic, and AWS S3

Projects

Distributed File System (DFS)↗

Peer-to-Peer Network • Go

  • ●A decentralized, peer-to-peer distributed file system implementing Content-Addressable Storage (CAS) with AES encryption
  • ●Features automatic file replication, fault tolerance, and modular architecture supporting custom transport protocols and storage backends
  • ●Built with TCP transport layer and SHA-1 cryptographic hashing for secure file operations across network peers

PhishSchool (BostonHacks 2025)↗

FastAPI • Supabase • Vite • Gemini Flash 2.5 • SendGrid

  • ●Interactive platform that helps users learn to spot and avoid phishing with Learn, Detect, and Campaign modules
  • ●AI-assisted detection analyzes .eml emails or any image format to provide a giving users a phishing score, risk level, and key indicators
  • ●Sends custom generate phising emails to users on chosen frequency, keeping track and learning of user failures
  • ●Vite: React/TypeScript + Tailwind (frontend) and FastAPI/Node/Supabase (backend), integrating Google Gemini and Twilio's SendGrid; deployed on Vercel

Blogs

Trying to improve my writing. Pieces on whatever I find intresting.

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    Pilot
    1 min read