Skip to content
View bhavishjain7133's full-sized avatar
  • IIT Delhi
  • Delhi

Highlights

  • Pro

Block or report bhavishjain7133

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please donโ€™t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this userโ€™s behavior. Learn more about reporting abuse.

Report abuse
bhavishjain7133/README.md

Bhavish Jain

M.Sc. Mathematics @ IIT Delhi ยท Operations Research & Supply Chain

views followers stars

๐Ÿง  Who I Am

const bhavish = {
  title: "M.Sc. Mathematics @ IIT Delhi",
  focus: ["Operations Research", "Stochastic Optimization", "Supply Chain Resilience"],
  languages: ["Python", "C", "C++", "R", "MATLAB", "SQL", "LaTeX"],
  optimization: ["IBM ILOG CPLEX", "Gurobi", "Wolfram Mathematica"],
  mlStack: ["PyTorch", "TensorFlow", "scikit-learn", "NumPy", "Pandas"],
  launchedProjects: [
    "learning-augmented-last-mile-routing",
    "quasi-causal-delivery-delay-analysis",
  ],
  currentlyResearching: [
    "Resilient multi-echelon supply chain network design under disruption",
    "Causal inference & interpretable routing in logistics",
  ],
  status: "Turning optimization + data into robust decisions",
};

๐Ÿš€ Featured Projects

๐Ÿ“ฆ Interpretable Learning-Augmented Last-Mile Routing

Interpretable learning-augmented routing on the 2021 Amazon Last-Mile Routing Challenge data โ€” cut the median official route-deviation score by ~40% while keeping travel time within a strict guardrail.

Layer Technology
Language Python
Domain Operations Research, Vehicle Routing
Modelling Interpretable ML, learning-augmented optimization
Evaluation Official Amazon scorer, leakage-safe splits, bootstrap CI

Code

๐Ÿ“Š Quasi-Causal Analysis of Dispatch Delays & Late-Delivery Risk

Cross-fitted, doubly-robust (AIPW) causal study of how missing a seller's dispatch deadline affects late-delivery risk across 81,941 Olist orders, with a preregistered-style protocol and falsification tests.

Layer Technology
Language Python
Method Causal Inference, 5-fold Cross-fitted AIPW
Data Olist relational e-commerce dataset
Rigor Reproducible notebooks, automated tests, GitHub Actions CI

Code

๐Ÿ› ๏ธ Tech Stack

Languages & Data

languages

AI / ML

ml

Optimization & Scientific Computing

IBM ILOG CPLEX Gurobi Wolfram Mathematica NumPy Pandas

Dev Tools

dev tools

๐Ÿค Connect

LinkedIn Google Scholar Portfolio Email

Popular repositories Loading

  1. MonteCarlo-Quant-Risk-Model MonteCarlo-Quant-Risk-Model Public

    Jupyter Notebook

  2. HMM-Regime-Detection-Quant HMM-Regime-Detection-Quant Public

    Hidden Markov Model based regime detection and risk modelling for Indian equity returns.

    Jupyter Notebook

  3. Mean-CVaR-Portfolio-Optimization Mean-CVaR-Portfolio-Optimization Public

    Jupyter Notebook

  4. Optimal-Trade-Execution-Stochastic-Control Optimal-Trade-Execution-Stochastic-Control Public

    Jupyter Notebook

  5. stochastic-supply-chain-optimization stochastic-supply-chain-optimization Public

    A stochastic supply chain inventory optimization project using Markov Decision Processes, Monte Carlo simulation, and policy analysis.

    Python

  6. learning-augmented-last-mile-routing learning-augmented-last-mile-routing Public

    Interpretable learning-augmented routing on 6,112 Amazon last-mile routes with leakage-safe evaluation and official scoring.

    Jupyter Notebook