Prabhat M

I am a

About Me

I am a student researcher with a passion for the intersection of data science and finance. My work focuses on applying quantitative analysis and machine learning to uncover structural insights from complex, noisy data. I am driven by the challenge of building robust models that explain, rather than just predict, performance under uncertainty.

QuantF1

A quantitative framework for understanding Formula 1 driver performance under uncertainty.

View on GitHub

Analytical Arc

1. Sharpe Ratio

Efficiency: How much pace does a driver extract per unit of chaos?

2. Sortino Ratio

Controlled Aggression: Which mistakes actually matter?

3. Execution Profile

Behavior: How is performance delivered?

4. Regime Sensitivity

Context: When does a driver’s execution work?

5. Drawdown & Recovery

Resilience: What happens when things go wrong?

6. Consistency

Repeatability: Is this structural skill or situational brilliance?

Other Projects

Quantitative Finance Research

A news sentiment-based trading strategy using VADER and FinBERT, backtested with a custom engine.

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Adversarial Attacks on NNs

A repository showcasing adversarial attacks (FGSM, PGD, C&W) on neural networks.

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Neurosymbolic AI for Disease Classification

A hybrid AI model for enhancing disease prediction and triage management.

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Skills

Quantitative Analysis
  • Financial Modeling
  • Algorithmic Trading
  • Risk Management
  • Time Series Analysis
Programming
  • Python (Pandas, NumPy)
  • R
  • SQL
  • MATLAB
Technologies
  • Machine Learning (Scikit-learn)
  • Deep Learning (TensorFlow, PyTorch)
  • Big Data (Spark)

Get In Touch

I'm always open to discussing new projects, research, or opportunities.

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