Anik Majumdar
Software Engineer · Backend + AI/ML
UC Davis Computer Science student building production software across distributed systems, AI/ML, and full-stack applications.
- Python
- Go
- PyTorch
- FastAPI
- PostgreSQL
- Kubernetes
Two products, built end to end.
Two production systems spanning backend, AI/ML, computer vision, geospatial, and full-stack engineering.
CourtCheck
From 20+ hours of weekly film review to a ~5-minute per-match summary.
Full-stack computer vision platform built with UC Davis Women's Tennis to automate match analysis and scouting.
- Built a multi-stage computer vision pipeline using TrackNet v2, YOLOv8m-pose, CatBoost, and PyTorch for ball tracking, player pose, bounce classification, and stroke recognition.
- Deployed GPU-accelerated inference on Modal and integrated 5+ ML/CV models into a production analytics pipeline.
- Built a FastAPI + PostgreSQL/Supabase backend and Next.js frontend, reducing ~20 hours of manual film review to ~5 minutes per match.
- Python
- PyTorch
- OpenCV
- TrackNet v2
- YOLOv8m-pose
- CatBoost
- FastAPI
- Next.js
- TypeScript
- PostgreSQL
- Supabase
- Modal
Computer Vision · AI/ML · Full Stack
- 5+
- CV / ML Models
- 3×
- Throughput
- 5K+
- Repository Clones
AeroRoute
Geospatial flight-route optimization across 85,000+ airports with weather-aware ML predictions.
Full-stack flight route optimization platform combining geospatial search, weather data, machine learning, and route-specific explanations grounded in a Gemini-powered RAG system.
- Built a geospatial routing engine over 85,000+ airports using PostGIS spatial queries, weather data, and diversion-airport analysis.
- Built scikit-learn models for flight-delay and estimated-time-enroute prediction and integrated their outputs into route optimization.
- Built a Gemini-powered RAG system that provides route-specific explanations and decision support.
- Built an asynchronous FastAPI backend with JWT authentication, rate limiting, REST APIs, and persistent route storage.
- FastAPI
- PostGIS
- PostgreSQL
- scikit-learn
- Gemini
- Next.js
- React
- TypeScript
- Supabase
Backend · Full Stack · AI/ML · Data
- 85K+
- Airports Searched
- 2
- ML Prediction Models
A timeline of building and shipping.
Where I've built production-facing software, from distributed ground systems at Turion Space to a computer-vision platform used by UC Davis coaches.
Project Manager
UC Davis Aggie Sports Analytics
Leading project management for CourtCheck, the UC Davis women's tennis analytics platform.
Software Engineering Intern
Turion Space
Designed and built production-facing distributed ground software for spacecraft command, telemetry, and real-time data workflows.
- Implemented an end-to-end command-and-telemetry pipeline across a distributed Go microservice architecture with UDP communication and real-time processing.
- Built a real-time image-retrieval feature using Flask + REST that returns captured images in a single API request, reducing payload size by 25%.
- Developed automated validation and debugging workflows across Kubernetes clusters (Python, Bash, Docker) to verify command routing, telemetry ingestion, and service health.
- Go
- Python
- gRPC
- Protocol Buffers
- NATS
- Kubernetes
- Docker
- Flask
- Bash
Software Engineer
UC Davis Aggie Sports Analytics
Built CourtCheck, a computer-vision and AI tennis analytics platform for the UC Davis women's tennis program.
- Built and deployed a full-stack computer vision and AI platform used by UC Davis women's tennis coaches to convert match footage into ball tracking, court detection, bounce classification, stroke recognition, heatmaps, shot maps, and scouting reports.
- Reduced coach film review from 20+ hours/week to a 5-minute per-match summary by building video upload workflows, FastAPI processing APIs, Supabase PostgreSQL/Auth/Storage, signed URLs, a Next.js analytics dashboard, and GPT-powered AI summaries.
- Engineered a GPU-accelerated Modal inference pipeline integrating 5+ ML/CV models, improving video processing throughput by ~3x and attracting 5,000+ GitHub clones.
- Python
- FastAPI
- Next.js
- Supabase
- PyTorch
- Modal
Software Engineering Intern
Xiphi.ai
Built a graph-based recommendation engine and backend data pipelines for an events platform.
- Developed a graph-based recommendation engine generating personalized recommendations for sessions, booths, exhibitors, and attendee discovery.
- Built ETL pipelines ingesting users, interests, sessions, booths, and interactions from PostgreSQL into Neo4j, modeled for Cypher traversal and ranking.
- Implemented asynchronous FastAPI endpoints and refactored graph schemas and indexes on high-frequency nodes, reducing recommendation latency by 10%.
- Python
- FastAPI
- Neo4j
- PostgreSQL
B.S. Computer Science
University of California, Davis
Foundations across algorithms, systems, and applied mathematics.
- Data Structures & Algorithms
- Algorithm Design & Analysis
- Software Development
- Object-Oriented Programming
- Computer Organization
- Discrete Mathematics
A toolkit spanning the full stack.
From model training to production infrastructure: the technologies I use to design, build, and ship reliable software.
Languages
Python and Go anchor my recent engineering work.
- Python
- Go
- C++
- Java
- JavaScript / TypeScript
Backend & APIs
Services built for real-time, distributed workloads.
- FastAPI
- REST APIs
- gRPC
- Node.js
AI / ML
Applied CV and ML pipelines behind CourtCheck and AeroRoute.
- PyTorch
- OpenCV
- scikit-learn
- CatBoost
Data
Relational, geospatial, and graph storage.
- PostgreSQL
- PostGIS
- Neo4j
- ClickHouse
Infrastructure
Containerized apps shipped and scaled.
- Docker
- Kubernetes
- Linux
- Modal
- Git
Hi, I'm Anik, a Computer Science student at UC Davis graduating in December 2027. I've worked at startups including Turion Space, where I built mission control software for spacecraft operations, and XIPHI.AI, where I worked with vector databases and backend systems.
On campus, I'm involved with Aggie Sports Analytics, where I've helped build computer vision and machine learning tools for the UC Davis tennis team. I enjoy working on projects where software connects to a real-world problem, whether that's spacecraft telemetry, sports analytics, or geospatial routing.
Outside of computer science, I enjoy going to the gym, running, swimming, spending time outdoors, and hanging out with friends. I'm from Orange County, California, so I especially enjoy being near the beach. I'm also working toward my private pilot license and hope to complete it after graduation.
Looking ahead, I want to work on challenging problems and build technology that has a meaningful real-world impact. I also hope to give back to the communities that have shaped me, including organizations like the Boy Scouts of America and my temple.
The full story, on one page.
Experience, projects, and technical depth in one place. Click through to read the full document.
PDF · Updated October 2026