Keshawn Smith

Keshawn Smith is a Visiting Assistant Professor in the College of Engineering, Technology, and Architecture (CETA) and a Jackie McLean Fellow at the University of Hartford, where he teaches undergraduate courses in electrical and computer engineering. He is also a PhD candidate in Electrical and Computer Engineering at the University of Connecticut.

His teaching philosophy emphasizes active learning, problem-solving, and mentorship, with the goal of helping students develop both strong technical foundations and the confidence to apply engineering concepts to real-world challenges. He is committed to creating an engaging classroom environment that encourages curiosity, collaboration, and lifelong learning.

Smith’s research focuses on the intersection of robotics, autonomous systems, artificial intelligence, and control theory. His work investigates robust and safe multi-agent reinforcement learning, resilient autonomous vehicle coordination, cyber-physical system security, communication-aware decision making, and sim-to-real policy transfer for autonomous systems. He is particularly interested in integrating modern machine learning techniques with classical control methods to develop autonomous systems that remain reliable under uncertainty, communication delays, and adversarial conditions.

Beyond his research and teaching, Smith is passionate about undergraduate research, student mentorship, and expanding access to engineering education. He actively works with students through research opportunities, mentoring initiatives, and outreach programs designed to encourage the next generation of engineers.

Research Interests

My research seeks to develop intelligent autonomous systems that are both safe and resilient when operating in complex, uncertain, and dynamic environments. I am particularly interested in combining machine learning with modern control theory to improve the reliability and performance of multi-agent robotic systems.

Primary Research Areas

• Autonomous Systems
• Robotics
• Multi-Agent Reinforcement Learning (MARL)
• Artificial Intelligence and Machine Learning
• Safe Reinforcement Learning
• Control Systems
• Connected and Autonomous Vehicles (CAVs)
• Multi-Robot Coordination
• Human–AI Collaboration
• Cyber-Physical System Security
• Communication-Aware Autonomous Systems
• Sim-to-Real Transfer
• Intelligent Transportation Systems

Teaching Interests

• Electrical Engineering
• Computer Engineering
• Robotics
• Control Systems
• Embedded Systems
• Artificial Intelligence
• Machine Learning
• Autonomous Vehicles
• Programming for Engineers
• Engineering Design
• Undergraduate Research Mentorship