M.Sc. Mathematics @ IIT Delhi ยท Operations Research & Supply Chain
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",
};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 |
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 |
Languages & Data
AI / ML
Optimization & Scientific Computing
Dev Tools
