Hello, world!

Shayan
Haque

Third-year CS @ Johns Hopkins

I build data-driven software and AI systems across sports analytics, forecasting, and product engineering. Currently a third-year Computer Science student at Johns Hopkins, focused on machine learning, backend systems, and clean user-facing tools.

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Shayan Haque

About Me

I am a third-year Computer Science student at Johns Hopkins University from the NYC area, with internship experience at the Baltimore Ravens and PE Solutions. My strongest work combines modeling, data pipelines, and software interfaces that help people make better decisions.

Recently, I built NFL matchup models over player-tracking data, shipped a production electricity-demand forecasting service, and founded LineupOptimization.com, a baseball analytics platform used by Atlantic League front offices.

I am especially interested in internships and early-career roles across software engineering, machine learning, sports analytics, AI product engineering, and data-intensive backend systems.

JHU B.S. Computer Science
2 Recent internships
5 Featured projects
2028 Expected graduation

Skills

Languages

Python Java Kotlin C/C++ JavaScript TypeScript SQL Go

Frameworks

React Next.js Node.js Express FastAPI Flask Jetpack Compose Material UI

Data & ML

pandas NumPy scikit-learn XGBoost PyTorch Matplotlib Seaborn

Platforms & Tools

AWS GCP Docker GitHub Actions Firebase MongoDB PostgreSQL Linux

Projects

Selected work from GitHub and recent builds, focused on real analytics products and applied engineering.

View all on GitHub

Jan. 2026 - Present

Live site

LineupOptimization.com

Founded and operate a production baseball analytics platform adopted across the Atlantic League, helping front offices construct data-driven batting orders.

  • Evaluates all 362,880 batting-order permutations against a proprietary BRP metric.
  • Uses NumPy vectorization and memoized baserunner-state transitions for sub-second query latency.
  • Built with React, TypeScript, FastAPI, PostgreSQL, AWS, CI/CD, authentication, and team analytics.
Python FastAPI React TypeScript PostgreSQL AWS

2025

GitHub

Ravens Beyond Receiving Yards

A player-tracking analytics system for evaluating WR/CB matchups beyond box-score production.

  • Built a three-stage pipeline for data cleaning, coverage classification, and matchup outcome prediction.
  • Trained XGBoost models using separation, release burst, route context, and game-situation features.
  • Produced metrics used to reason about receiver effectiveness and shutdown coverage.
Python Jupyter XGBoost scikit-learn NFL tracking data

2025

GitHub

NCAA Free Throw Optimization

A decision tool that finds when a team should intentionally foul in NCAA basketball endgames.

  • Models endgame scenarios as a Markov Decision Process with Monte Carlo validation.
  • Implements NCAA bonus rules, player-specific free throw inputs, and win-probability visualizations.
  • Ships as a FastAPI and React app with CSV import, exports, and scenario controls.
Python FastAPI React MDP Monte Carlo

2025

GitHub

Politics Trade Market Tracker

Native Android app for browsing live prediction-market contracts, tracking favorites, and reviewing price history.

  • Built with Kotlin, Jetpack Compose, ViewModel, LiveData, Room, WorkManager, and Retrofit.
  • Uses offline-first architecture with background refresh, cached market data, and explicit loading states.
  • Includes reusable Compose cards, charts, filter chips, and watchlist interactions.
Kotlin Compose Room Retrofit WorkManager

Aug 2024 - Present

Startup

ReadRight

Co-founded an EdTech startup and built a gamified reading web app with real-time progress tracking, badges, and interactive quizzes.

  • Built cross-platform app flows with React, TypeScript, Next.js, Firebase Auth, and Firestore.
  • Supported backend quiz and progress mechanics using Node.js and MongoDB.
React TypeScript Next.js Firebase MongoDB

Experience

Work

Baltimore Ravens

Aug. 2025 - Dec. 2025

Sports Analytics Intern

Baltimore, MD

  • Shipped a Python evaluation framework adopted by the Ravens' quantitative staff for WR/CB performance analysis.
  • Engineered ETL over NFL Next Gen Stats player-tracking data, automating per-game feature generation.
  • Trained Random Forest and XGBoost models for Wide Receiver Effectiveness and Shutdown Index metrics.
Python SQL pandas NumPy scikit-learn XGBoost

PE Solutions

June 2026 - Aug. 2026

Software Engineering & AI Intern

New York, NY

  • Shipped a production electricity-demand forecasting service that cut MAE by 20% versus the legacy model.
  • Containerized an XGBoost forecasting pipeline and exposed it as a FastAPI REST microservice on AWS.
  • Added tests, CI checks, uncertainty visualizations, and a deployment runbook for future model releases.
Python FastAPI Docker AWS CI/CD XGBoost

Available for internship and early-career conversations

Want to talk software, sports analytics, or applied AI?

I am looking for teams where I can ship production software, learn quickly, and bring a data-driven builder's mindset to hard problems.

Email Me LinkedIn GitHub