I build stochastic models of service systems — hospitals, warehouses, call centres and human–AI teams — and use optimisation and reinforcement learning to decide how they should be run.
I am a Postdoctoral Researcher at the Centre of Digital Transformation, TUM School of Management, Technical University of Munich, working with Prof. Dr. Jingui Xie.
My research sits at the interface of applied mathematics, Strageic behaviour, and operations management. I work on stochastic processes and queueing theory, stochastic and multi-objective optimisation, Markov decision processes and reinforcement learning, and the game-theoretic analysis of how people behave strategically in congested systems.
I earned my Ph.D. in Mathematics at IIT Roorkee, then held postdoctoral positions at IIT Hyderabad and Amrita Vishwa Vidyapeetham, and a visiting research fellowship at KEDGE Business School, France. At TUM I teach English-taught master's courses in Deep Reinforcement Learning, Reinforcement Learning and Python for Data Analysis, and supervise master's and bachelor's theses.
I collaborate with researchers in India, Germany and France, and I'm always happy to discuss research collaborations and student projects.
Retrial, multi-server and phase-type queues; stationary, transient and nonlinear-dynamics analysis of service systems.
Markov chains, phase-type distributions and self-exciting Hawkes processes for modelling bursty, state-dependent demand.
Deep and explainable RL for admission control, preemption, routing and berth allocation in stochastic systems.
NSGA-II, PSO, grey-wolf and other metaheuristics combined with simulation for cost–service trade-offs.
Equilibrium joining strategies, Wardrop equilibria and customer behaviour in congested and hybrid systems.
Routing, trust and queue management when human servers and AI agents share the same workload.
BirdVision's system shuts down wind turbines based on detected bird size classes. The student team is building an application that identifies which size classes and species trigger shutdowns, and proposes settings that cut unnecessary shutdowns without weakening species protection.