Prabhat M
Mathematics & Statistics at Purdue
Building toward quantitative research, with work in finance, sports analytics, and applied ML
About
I'm a Math and Statistics senior at Purdue, graduating May 2027. My work sits at the intersection of quantitative finance, sports analytics, and applied machine learning. I'm most interested in building rigorous models that explain why something happens, not just predict that it will.
Over the past two years I've self-built an implementation library covering the core syllabus of a graduate-level quantitative finance program - option pricing through exotics, credit risk models, factor models, portfolio optimization, time series analytics. More recently, I've been developing QuantF1: a quantitative performance attribution framework for Formula 1 that applies risk-adjusted finance metrics to driver evaluation. The methodology paper will be on arXiv in August 2026.
Co-founder of UltraRice, a registered 501(c)(3) non-profit working on rice fortification for malnutrition in India, funded by Tyler Cowen's Emergent Ventures India program. Selected participant in the 2026 IMC Trading US Chess Academy.
Featured Research: QuantF1
A quantitative framework for understanding Formula 1 driver performance under uncertainty. QuantF1 applies risk-adjusted finance metrics to driver evaluation and performance attribution.
QuantF1 Series (5 articles)
Most recent first:
- The Physics of Failure: Measuring Drawdown and Recovery in Formula 1
- The Alpha of a Driver: Separating Skill from Machinery in Formula 1
- The Execution Profile of a Driver: Measuring Performance Beyond the Results Table
- The Sortino Ratio of a Driver: Measuring Controlled Aggression in F1
- The Sharpe Ratio of a Driver: What Quant Finance Can Teach Us About Formula 1 Performance
Six-Step Analytical Framework
The framework, in order:
Sharpe Ratio
Efficiency: How much pace does a driver extract per unit of chaos?
Sortino Ratio
Controlled Aggression: Which mistakes actually matter?
Execution Profile
Behavior: How is performance delivered?
Regime Sensitivity
Context: When does a driver's execution work?
Drawdown and Recovery
Resilience: What happens when things go wrong?
Consistency
Repeatability: Is this structural skill or situational brilliance?
Quantitative Finance
Deep, implementation-focused work spanning derivatives, portfolio construction, systematic trading, and applied prediction markets.
Quant-Finance
End-to-end research and implementation library covering Black-Scholes, Monte Carlo for Asian/Barrier/Lookback/Rainbow exotics, Merton credit model, factor models, portfolio optimization, and time series analytics.
Implementation depth includes: Black-Scholes and Monte Carlo for Asian/Barrier/Lookback/Rainbow exotics; credit risk via Merton, Black-Cox, and Jarrow-Turnbull; factor models including CAPM, Fama-French 3/5, Carhart, and AQR style premia; portfolio optimization via Markowitz, Black-Litterman, and reinforcement learning; full sentiment-driven trading pipeline with VADER + FinBERT; backtesting engine with realistic transaction costs; live paper trading bot.
- Financial Models
- Portfolio Management
- Trading Strategy
- Time Series
- Live Paper Trading
TSA Forecasting for Kalshi
Contributor on team project building a production forecasting system for Kalshi's weekly TSA checkpoint contracts. Owned the ARIMA/SARIMAX baseline track and walk-forward benchmark harness. Implemented quarter-Kelly position sizing. System paper-trading since March 2026 via GitHub Actions.
Explore RepositoryMachine Learning & Neurosymbolic AI
Applied ML systems, neurosymbolic reasoning, and focused research artifacts.
Disease Classification and Triage
Hybrid neurosymbolic system for disease prediction from patient symptoms. Combines rule-based clinical reasoning with neural pattern recognition.
Explore RepositoryNeural Network Robustness
Focused study of adversarial attacks (FGSM, PGD, C&W) and defense mechanisms for deep learning models.
Explore RepositoryMovie Recommendations with Deep Learning
Hybrid neural network combining collaborative and content-based filtering using TensorFlow Recommenders.
Cited in Lavreniuk and Potapova (2025), Applied Information Technologies, Vasyl' Stus Donetsk National University.
Explore RepositoryBeyond Deep Learning: The Rise of Neurosymbolic AI
Introduction to hybrid symbolic-neural systems, written March 2023. Predates the recent neurosymbolic AI wave by about 18 months.
Read EssayWriting
Medium articles across three tracks: QuantF1 research, quantitative finance, and applied ML/neurosymbolic systems.
- QuantF1 research series focused on risk-adjusted driver evaluation
- Quantitative finance breakdowns of models and market structure
- Applied ML and neurosymbolic AI systems in practice
Experience
Data Science / ML Intern — Hexaware Technologies
Life sciences practice.
Research Assistant — Purdue College of Liberal Arts Research Academy
Built a Python-based multi-agent simulation framework modeling decision-making under partial information. Designed agent orchestration logic, experiment pipelines, and structured data logging for thousands of reproducible simulations.
Software Engineering Intern — Hotkeys Holding
Production WebSockets + RabbitMQ event-driven messaging system, sub-200ms latency under concurrent load. Production RAG service using Gemini APIs and vector databases (90% lookup time reduction). Low-latency caching layer for Vertex AI inference (95% response time reduction).
Background
UltraRice (501(c)(3) non-profit), 2023-present. Co-founded at 17 to develop ultrasonic rice fortification technology targeting India's 250M+ malnourished. Selected by Emergent Ventures India (Tyler Cowen, Mercatus Center, George Mason University) for non-dilutive grant capital. Public financials at bank.hackclub.com/ultrarice.
The Knowledge Society (TKS), 2022. Globally selective youth innovation program (alumni at Microsoft, Neuralink, IBM). UltraRice originated here as an end-of-year moonshot project.
Chess. Tamil Nadu state representative, CICSE Nationals 2022. Selected for IMC Trading US Chess Academy 2026.
Let's Connect
I'm always interested in discussing research, collaborating on projects, or exploring new opportunities in quantitative finance, machine learning, and AI.