PhD Candidate in Mechanical Engineering (Systems & Controls) at the University of Tennessee, Knoxville — building the decision-making intelligence that makes autonomous vehicles safer.
I am a PhD candidate and Graduate Research & Teaching Assistant at the University of Tennessee, Knoxville, working at the intersection of reinforcement learning, data-driven control, and connected & automated vehicles (CAVs).
My dissertation research, conducted in collaboration with Oak Ridge National Laboratory (ORNL), develops optimal control frameworks that enable CAV platoons to cooperate intelligently and avoid chain collisions in real mixed-traffic environments. I successfully defended my PhD proposal in April 2026.
I hold an M.S. in Mechanical Engineering from UTK (2025) and an M.S. in Soft Computing & Intelligent Systems from Universiti Malaysia Pahang (2022), where I received the Excellent Publication Award. My research has generated over 832 citations with an h-index of 14, with international collaborations spanning the UK, Japan, Spain, and Saudi Arabia.
Before pursuing my doctoral studies, I spent eight years as a lecturer and educator in Bangladesh — an experience that deeply shapes how I mentor students and communicate complex research to broad audiences.
Designing cooperative control systems for CAV platoons operating in mixed human-machine traffic, with a focus on chain-collision prevention and safe following behavior at highway and urban speeds.
Applying deep RL algorithms — including PPO, SAC, and custom multi-agent architectures — to real-time vehicle decision-making problems where classical control methods fall short in dynamic environments.
Building data-driven frameworks that replace or augment model-based controllers, enabling vehicles to learn safe and energy-efficient behaviors directly from interaction with high-fidelity simulation environments.
Investigating how vehicle-to-vehicle and vehicle-to-infrastructure communication can be leveraged to improve traffic flow efficiency, reduce energy consumption, and enhance safety at scale.
Extending CAV decision-making methodologies to broader autonomous systems contexts, including multi-robot coordination, route tracking, and real-time threat assessment in dynamic environments.
Collaborating with ORNL on the intersection of CAV control optimization and energy efficiency — exploring how intelligent driving strategies can reduce fleet-level energy consumption in electrified transport.
Full publication list on Google Scholar · ResearchGate · ORCID
Full academic curriculum vitae including complete publication list, research experience, teaching history, awards, and references.
I am actively seeking tenure-track faculty positions and research scientist roles beginning Spring 2027, with interests in autonomous systems, intelligent transportation, and AI-driven control. I welcome collaboration inquiries, speaking invitations, and general correspondence.