Every experience adds another layer to the way we understand complex problems.
Professional Summary
I am a postgraduate researcher pursuing an M.Sc. in Mathematics at IIT Delhi, with research interests spanning operations research, stochastic optimisation, supply-chain analytics, game theory, and graph-based learning. My work focuses on developing mathematically rigorous and computationally implementable approaches for decision-making under uncertainty.
Education
M.Sc. in Mathematics
2025–Present
Indian Institute of Technology Delhi
Department of Mathematics
CGPA: 7.89
Thesis: Hypergraph Neural Networks and Graph-based Learning for Biomedical Applications · Advised by Prof. Aparna Mehra
B.Sc. in Mathematics and Computing
2022–2025
Birla Institute of Technology, Mesra
CGPA: 8.88
Thesis: A Study on Gradients of Non-Differentiable Functions · Advised by Dr. Syeda Darakhshan Jabeen
Research Experience
Research Intern
Indian Institute of Management Calcutta
Operations Management Group
Supervisor: Prof. Ayesha Arora
Studying digital supply-chain innovation in e-commerce, with a focus on AI-powered chatbots, consumer engagement, and channel dynamics, and developing a game-theoretic model of strategic interactions and equilibrium outcomes under AI-driven service innovation.
Thesis on Hypergraph Neural Networks and Graph-based Learning for Biomedical Applications — investigating hypergraph neural networks and graph-based learning methods for modelling higher-order relationships in biomedical data.
Hypergraph neural networksGraph-based learningHigher-order relationshipsBiomedical data modelling
Research Intern
Indian Institute of Management Mumbai
Operations & Supply Chain Division
Supervisor: Prof. Vijaya Kumar Manupati
Developed a two-stage stochastic mixed-integer linear programming model for resilient multi-echelon FMCG supply-chain network design under supplier and warehouse disruptions, and designed classical and accelerated Benders decomposition approaches to solve it.
Two-stage stochastic MILPResilient multi-echelon FMCG network designFacility location, production & inventory decisionsCross-docking & lateral transshipmentClassical & Accelerated Benders DecompositionValid inequalities, optimality & feasibility cutsLazy-constraint callbacksSample Average Approximation with Latin Hypercube SamplingPython & IBM ILOG CPLEXBenchmarking against CPLEX branch-and-cut
Teaching Assistantship
Teaching Assistant
Indian Institute of Technology Delhi
MTL2003: Optimization Methods and Applications
Supervisor: Prof. Harmender Gahlawat
Teaching assistant for MTL2003: Optimization Methods and Applications, under the course instructor Prof. Harmender Gahlawat.
Research Manuscripts & Thesis
Resilient Multi-Echelon FMCG Supply Chain Network Design under Uncertainty
Research Manuscript · Manuscript completed
A resilient multi-echelon FMCG supply-chain network design model under supplier and warehouse disruptions, formulated using two-stage stochastic mixed-integer linear programming.
AI-Driven Service Innovation in E-Commerce Supply Chains
Research Manuscript · Manuscript in preparation
A game-theoretic study of AI-powered chatbot adoption in e-commerce supply chains, focusing on consumer engagement, channel dynamics, strategic interactions, and equilibrium outcomes under AI-driven service innovation.
Hypergraph Neural Networks and Graph-based Learning for Biomedical Applications
M.Sc. Thesis · Ongoing
An ongoing study of hypergraph neural networks and graph-based learning methods for modelling higher-order relationships in biomedical data.
Independent Research Project · Completed Project · November 2025 – December 2025
An interpretable, learning-augmented approach to last-mile delivery routing that combines operations research with interpretable machine learning and hierarchical zone-preference modelling, evaluated on real Amazon last-mile delivery routes using the official challenge scoring.
Reduced aggregate median official route score by 39.9% compared with nearest-neighbour routing
Improved 88.1% of test routes
Limited median travel-time increase to 4.83%
Reduced median zone re-entries from 21 to 0
Quasi-Causal Analysis of Dispatch Delays and Late-Delivery Risk in E-Commerce
Independent Research Project · Completed Project · December 2025 – January 2026
A quasi-causal analysis of how dispatch-deadline breaches relate to late-delivery risk in e-commerce, using augmented inverse probability weighting with five-fold cross-fitting on real Olist order data.
Estimated a 14.11 percentage-point higher adjusted late-delivery risk