Prabhat M
I am a
Quantitative Researcher | Machine Learning Engineer | Neurosymbolic AI Developer
About Me
I'm a quantitative researcher and machine learning engineer passionate about solving real-world problems through the intersection of finance, AI, and sports analytics. My work combines rigorous statistical modeling with modern deep learning to uncover structural insights from complex, noisy data.
I specialize in building hybrid AI systems that merge symbolic reasoning with neural networks, developing sophisticated financial models, and applying quantitative principles to unconventional domains like Formula 1 racing. I believe in creating models that not only predict accurately but also explain the underlying mechanisms driving performance.
Currently, I'm focused on quantitative finance research, neurosymbolic AI applications, and developing reproducible, well-documented analysis pipelines. I actively share insights and research findings through technical writing on Medium and open-source contributions on GitHub.
Areas of Expertise
Quantitative Finance
Financial modeling, algorithmic trading, risk management, performance attribution, and statistical arbitrage
Machine Learning
Deep learning, adversarial robustness, recommender systems, time series forecasting, and model interpretability
Data Engineering
Data pipelines, statistical analysis, data visualization, and reproducible research workflows
Neurosymbolic AI
Hybrid AI systems combining symbolic reasoning with neural networks for healthcare and domain-specific applications
QuantF1: Quantitative Analysis in Motorsports
A quantitative framework for understanding Formula 1 driver performance under uncertainty, published as a research series on Medium. Applying financial metrics and statistical modeling to analyze driver efficiency, risk management, and adaptability.
Six-Step Analytical Framework
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 & Recovery
Resilience: What happens when things go wrong?
Consistency
Repeatability: Is this structural skill or situational brilliance?
Recent Research & Writing
I actively publish research and technical articles on Medium exploring quantitative finance, AI, and their applications.
The Physics of Failure: Measuring Drawdown & Recovery in Formula 1
Analyzing driver resilience through financial risk metrics applied to motorsports performance data.
Read ArticleThe Alpha of a Driver: Separating Skill from Machinery in Formula 1
Decomposing driver performance into skill (alpha) and machinery (beta) components using regression analysis.
Read ArticleEnhancing Triage Management in Healthcare with Neurosymbolic AI
Hybrid approaches combining symbolic reasoning and deep learning for improved disease prediction and medical decision-making.
Read ArticleAdversarial Attacks on Neural Networks
Comprehensive analysis of adversarial attack methods (FGSM, PGD, C&W) and defense strategies for robustness.
Read ArticleCreating Hybrid Neural Networks for Movie Recommendations
Combining collaborative and content-based filtering using TensorFlow Recommenders for enhanced accuracy.
Read ArticleRevolutionizing Mocktail Creation with Neurosymbolic AI
Building personalized systems that generate novel mocktail recipes using hybrid symbolic and neural approaches.
Read ArticleFeatured Projects
A selection of my most impactful open-source projects and research implementations.
QuantF1: Quantitative Finance in Motorsports
Statistical modeling of Formula 1 driver performance using financial metrics (Sharpe/Sortino ratios, alpha-beta decomposition). Published research on Medium.
Explore RepositorySentiment-Based Trading Strategy
News sentiment analysis using VADER and FinBERT with a custom backtesting engine. Demonstrates practical application of NLP in algorithmic trading.
Explore RepositoryDisease Classification & Triage
Hybrid AI system combining symbolic reasoning with deep learning for medical diagnosis and patient triage optimization.
Explore RepositoryNeural Network Robustness
Comprehensive 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 framework.
Explore RepositoryML Algorithms Documentation
Comprehensive repository documenting various machine learning algorithms with detailed implementations and practical applications.
Explore RepositoryLet's Connect
I'm always interested in discussing research, collaborating on projects, or exploring new opportunities in quantitative finance, machine learning, and AI.