Jonah Rothman

About Me

Hi, I'm Jonah Rothman. I'm studying Computer Science (BA/MS) with a minor in Economics at Boston University. Most of my time goes to web apps, AI, and test automation. I've done three technical internships. Most recently I created an AI test automation pipeline at Major League Baseball in the summer of 2026, after engineering roles at PanelClaw and ClearWatt. I also built The Task App, The Study Buddy, and InternAtlas. I'm looking for full-time software engineering roles for 2027. I like making things the people around me will actually use.

Programming
Java Python JavaScript TypeScript C/C++ Kotlin Swift OCaml
Web & App
React Next.js React Native Expo Node.js Express.js FastAPI Tailwind CSS Jetpack Compose
Tools
GitHub Actions Firebase Azure PostgreSQL MongoDB Docker Prisma Vercel DigitalOcean
Testing & QA
Playwright Cypress Jest Maestro Azure DevOps CI/CD
Major League Baseball (MLB)
Jun 2026 – Aug 2026
Software Engineer Intern, Test Engineering
New York, NY

Architected a multi-agent AI automation pipeline to generate PR-ready Playwright tests, ensuring ticketing reliability for 70M+ annual fans.

PanelClaw
May 2025 – Aug 2025
Junior Front-End Developer Intern
North Andover, MA

Led Cypress QA automation, cutting regression test time from a full day to about an hour.

ClearWatt
Feb 2025 – Apr 2025
Software Development Intern
London, UK

Built the first automated testing suite with Maestro and wired it into Azure DevOps pipelines.

First Cambridge Realty Corporation
May 2022 – Aug 2022
Information Technology Intern
Cambridge, MA

Installed a new office network, refreshed hardware, and lifted throughput ~50% with optimized fiber configurations.

Boston University
2022 – Dec 2026
MS & BA Computer Science · Minor in Economics
GPA 3.75 Dean's List every term
Thayer Academy
2018 – 2022
High School Diploma
Braintree, MA

Technical Internships

Deployed Apps

All Projects

Personal
Jul 2026 – Present

Rezzy

Multi-agent LangGraph pipeline for AI resume tailoring that preserves the applicant's own wording.
  • Designed a multi-agent AI pipeline in Python/FastAPI using LangGraph, with dedicated resume tailoring agents
  • Built an evaluation harness that validates generated LaTeX PDF output for formatting errors and auto-recompiles
  • Engineered prompts to prevent metric and skill hallucination, grounding generated content against a verified source bank
  • Containerized the backend with Docker and deployed to Azure Container Apps, with a Next.js/Firebase frontend and auth
Class
Jan – May 2026

Iroko Connector — BU Spark!

AI-powered schema matching engine that maps third-party SaaS data into a unified enterprise risk platform — no training data or labeled mappings required.
  • Built ReMatch, a 3-stage LLM pipeline using text-embedding-3-large and gpt-4o-mini to auto-map third-party schemas to a unified ontology with confidence-scored rankings
  • Developed a Next.js + TypeScript frontend with React Flow for interactive schema visualization and live SSE-streamed mapping results
  • Partnered with Iroko Technologies across 4 agile sprints to deliver a production-ready connector for the OneScor enterprise risk platform
Collaborators: Sean Tomany, Abidul Islam, Vanshika Chaddha, Vincent Lin
Class: CS/DS 519 — Software Engineering (BU Spark!) — Spring 2026
Class
Jan – May 2026

Robust Cross-View 3D Pose Tracking

Classical upgrades to multi-camera 3D human pose estimation that cut joint error 11× under occlusion — no neural networks in the evaluated pipeline.
  • Added IRLS robust triangulation, uncertainty-aware Mahalanobis affinity, and a per-joint Kalman filter — all classical, neural-network-free upgrades
  • Cut joint error under 20% occlusion from 219 mm → 20 mm (11×) and flipped tracking score (MOTA) from −0.76 → +0.87
  • Built a stress-test harness across four corruption types and multi-seed evaluation on two benchmarks; also shipped a real-time MediaPipe exercise feedback demo
Collaborators: Sean Tomany, Jigar Kanakhara, Bhavya Bavishi, Harsha Basavaraj Beth
Class: CS 585 — Image and Video Computing — Spring 2026
Class
Jan – May 2026

Predicting Movie Ratings from YouTube Trailer Comments

Can pre-release YouTube comments predict a film's eventual Letterboxd rating? A 5-class NLP pipeline across 173 movies and 236K comments.
  • Scraped TMDB, YouTube Data API, and Letterboxd to build a 173-movie, 236K-comment dataset with strict pre-release filtering
  • Benchmarked six models; TF-IDF + LogReg won at 0.423 accuracy and 0.401 macro F1, beating frozen and fine-tuned DistilBERT variants
  • Engineered 25 features (VADER sentiment, hype/spam heuristics, credibility weighting) and confirmed via temporal ablation that late-window comments carry the strongest signal
Collaborator: Sean Tomany
Class: CS 505 — Natural Language Processing — Spring 2026
Personal
January 2026

The Task App

AI-powered task manager combining natural language reminders, Eisenhower Matrix prioritization, and calendar/iCal integration.
  • Built a Next.js task management app with Firebase Authentication and Firestore for real-time task storage and sync across devices
  • Integrated Azure Foundry GPT-5 for AI-powered natural language parsing—type "remind me Friday" and the app schedules with priority and group
  • Implemented drag-and-drop Eisenhower Matrix for instant task prioritization across Do First, Schedule, Delegate, and Eliminate quadrants
  • Built calendar view with iCal feed subscription for seamless integration with external calendar apps
Personal
Dec 2025 – Jan 2026

InternAtlas

Search thousands of tech company job boards in one place with live filters and real-time stats.
  • Built a production job board that crawls 2,500+ company ATS boards and normalizes 600,000+ listings into PostgreSQL with full-text search and filters
  • Automated large-scale crawling via GitHub Actions on a 12-hour cadence, feeding Vercel-hosted UI backed by DigitalOcean PostgreSQL
  • Shipped live experience with split-pane UI, advanced filtering, application tracking, and Google OAuth/Firebase sync
Class
December 2025

MealMap

Plan meals, discover recipes, auto-generate shopping lists, log via barcode/planner/custom, and track calories on a dashboard.
  • Built a Jetpack Compose Android app for smart meal planning, nutrition tracking, and shopping list generation using Material 3 design
  • Integrated TheMealDB and OpenFoodFacts APIs with Retrofit, Room, and StateFlow to power recipe discovery, barcode scanning, and persistent weekly meal plans
  • Delivered a real-time nutrition dashboard, onboarding flow, and robust error handling across camera permissions, offline states, and data entry
Collaborator: Abidul Islam
Class: CS501 Mobile Application Development (Fall 2024)
Personal
November 2025

The Study Buddy

Upload notes, generate AI flashcards, play study games, and chat with a course-aware assistant.
  • Deployed an AI study platform using React, Vite, TypeScript, and Express backend, running across Azure app services, DigitalOcean, and Google Firebase supporting 50+ users
  • Developed a scalable Express.js backend using Azure Blob Storage for PDF handling and Azure OpenAI to generate flashcards and context-aware subject-specific chat responses
  • Designed a MongoDB Atlas schema with 7 collections, compound indexes, and repository pattern architecture supporting 100+ notes, 50+ flashcard sets, and 200+ chat messages
  • Implemented CI/CD with GitHub Actions for automated builds and deployments
Collaborator: Sean Tomany
Class
November 2025

NBA Team Outcome Prediction

Predicts team scores and winners with a linear regression pipeline on nba_api data, halving error and reaching 0.818 accuracy.
  • Modeled NBA team scores and winners using nba_api game logs with a reproducible Makefile-driven pipeline
  • Engineered features for rest days, home/away context, and opponent stats to train and benchmark regression and classification models
  • Achieved strong accuracy and visualized trends with notebooks that output cleaned datasets, predictions, and validation plots
Collaborators: Alim Ackura, Ash Payal, Justin Liu, Shawn Xiang, Jonah Rothman
Class: CS506 Data Science and Applications (Fall 2025)
Personal
August 2025

VibeScape

Interactive song map to compare playlists with friends, explore clusters, and generate AI-curated blends and top picks.
  • Built a Next.js and React powered interactive Spotify song map that visualizes a user's library as clusters of songs and playlists with the ability to compare music with friends and create custom blended playlists
  • Created an AI-powered playlist chooser via prompt and designed a unique ranking model for Top Songs
  • Harnessed SOTA AI tools like OpenAI Codex CLI to accelerate development time and enhance the UI/UX
Class
Sep 2024 – Dec 2024

Java Terminal Game Engine

Play everything from Tic-Tac-Toe to turn-based RPGs in a scalable, pattern-driven terminal engine.
  • Developed a terminal-based game engine ranging from simple games to complex turn-based gameplay, including DnD-style mechanics
  • Implemented 50+ Java classes to build a scalable, object-oriented architecture
  • Utilized Strategy, Factory, and Singleton design patterns to enhance flexibility and reduce code duplication
Class: CS411 Software Principles (Fall 2024)
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Contact Me

jonahrothman@me.com

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