👋 Hi, I'm
Jiantang Ma
Data Scientist · Applied Mathematician
MS in Data Science @ UPenn · BS in Applied Math @ UCLA
About Me
I'm a data scientist and applied mathematician who loves building systems at the intersection of machine learning, quantitative finance, and distributed computing. I turn complex data into meaningful decisions — whether that's predicting esports match outcomes, pricing insurance products, or modeling ideological dynamics.
Experience
External Consultant, Health Division · Shanghai
Market forecasting, competitive analysis, and risk assessment of life and health insurance products. Developed a pricing model incorporating market dynamics and customer behavior.
Part Time Assistant · Remote
Industry and competitor research supporting ongoing projects. Market sizing, time series analysis, and client presentation design.
Intern, Quantitative Department · Zhengzhou
Back-tested Triple Moving Average and Grid Trading strategies. Extracted insights and presented comprehensive reports to senior management.
Featured Projects
See all →Distributed Web Crawler and Search Engine
Aug 2025Built an end-to-end distributed search engine in Java with a custom HTTP server, distributed key-value store, Flame-style compute layer, robust crawler, inverted indexer, PageRank ranking, and frontend search interface.
- Custom web server, KVS, crawler, indexer, PageRank, and search frontend
- Robots.txt handling, rate limiting, blacklist filtering, and URL normalization
League of Legends Match Outcome Prediction
Nov 2025Built a time-series machine learning pipeline to predict League of Legends match outcomes from Riot API timeline data. Engineered temporal player/team features and compared deep learning models with tree-based baselines across multiple game-time checkpoints.
- Processed 128K+ in-game frames from 4,546 matches
- Engineered 256 temporal features with leakage-aware train/validation/test splits
Mathematical Modeling of Idea Intrusion
Jul 2023Models of ideological conflict incorporating committed minorities and deradicalization factors. Applied bifurcation and stability analyses to study opinion fragmentation and group dynamics.
- Modeled interaction dynamics between mainstream and committed minority groups
- Studied stability, bifurcation behavior, and deradicalization strategies