MTech RA/RAP Admissions 2026

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Project Title

Project Description

Principal Investigator(s)

Funding

Hybrid drone-ground vehicle logistics for Warehouse and Last-Mile Operations

Hybrid drone–ground vehicle logistics systems for two complementary operational contexts: intra-warehouse logistics and last-mile delivery scenarios. The objective is to understand how aerial and ground robots can coordinate to improve efficiency, flexibility, and responsiveness in modern logistics systems. Inside a warehouse, The research will develop a small experimental platform where drones and ground robots coordinate tasks such as item retrieval, shelf scanning, and delivery to packing stations. In last-mile setting, the system will model a hybrid delivery approach in which a ground vehicle acts as a mobile depot while drones perform short-range deliveries to nearby destinations. The research will experimentally evaluate delivery strategies. [Looking for a student with robotics/ mechatronics/ control/ automation backgroung]

Jayendran V

IITB Fedex ALFA center

AI-Enabled Digital Twin for Intelligent Infrastructure Logistics Systems

Large-scale infrastructure logistics systems such as ports, multimodal freight terminals, and oil and gas transportation networks are becoming increasingly complex due to rising trade volumes, demand variability, infrastructure constraints, and evolving regulatory requirements. These systems operate as tightly coupled physical networks where flow dynamics, capacity limits, storage constraints, and operational workflows interact in non-trivial ways. Despite their importance, decision-making in such environments often remains fragmented and reactive, with limited integration of physics-based modeling and data-driven analytics. This research aims to develop an integrated digital twin framework that combines first-principles, physics-based modeling of infrastructure behavior with advanced simulation and AI-driven analytics. The objective is to create a robust decision-support environment capable of representing material flows, congestion propagation, asset utilization, and network-level interactions under uncertainty. The framework will enable scenario analysis for demand surges, infrastructure expansion, maintenance planning, and disruption response. By bridging physical system modeling with intelligent analytics, the proposed research seeks to improve operational efficiency, throughput, and resilience across critical logistics infrastructure systems. The outcomes are expected to contribute to scalable methodologies for modernizing large-scale trade and energy transport networks.

Saurabh Jain

IITB Fedex ALFA center

Higher-order networks: Dynamics and Inference

This project develops statistical foundations for analyzing higher-order interaction data represented as tensors. We study random hypergraph models to address three core problems: parameter estimation under full and partial observations, inference for temporal dynamics with latent regime switching, and efficient sampling of constrained tensor structures. The work focuses on identifiability, consistency, and computational scalability, combining moment-based methods, probabilistic analysis, and algorithmic design. These contributions aim to enable principled modeling, inference, and hypothesis testing for complex multi-way data, with applications in biological, biomedical, and machine learning systems.

Vinay Kumar B R

Seed Grant

RA for IEOR labs

Responsibilities include assisting IEOR Faculty with developing, updating and supporting UG and PG program labs, curriculum for Digital Enterprise Systems and other labs. Familiarity with digital, electrical and mechanical systems will be preferable. This is an institute RA position, not supported through any other project.

RA for IEOR system administration

Responsibilities include maintaining IEOR computational and web servers, upgrading hardware, installing & updating software, maintaining the department website, supporting students, staff and faculty in using these systems, assisting in maintaining other systems including printers, wifi, scanners and switches etc. The candidate should be comfortable using and managing computers and other hardware. Familiarity with Linux, good communication skills, analytical and debugging abilities, basic knowledge of networking are desirable. This is an institute RA position, not supported through any other project.