Faculty and Staff

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Kamran Iqbal obtained his BE in Aeronautical Engineering from the NED University (Pak), his MSEE, MBA, and Ph.D. in Electrical Engineering degrees from the Ohio State University, Columbus, Ohio. He has served at multiple national and international institutions including Northwestern University, UC Riverside, UC Irvine, CSU Fullerton, and CSU Los Angeles. He is currently employed as Professor and Program Coordinator of Electrical and Computer Engineering at the University of Arkansas at Little Rock. His research interests include sensorimotor control of human movement, biomedical signal and image processing, data-driven control systems, renewable energy, and machine learning. He is a senior life member of IEEE (USA) and member of IET (UK), ASEE, and past member of American Society for Mechanical Engineers (ASME), and Sigma Xi (past president of the Central Arkansas chapter). He is a licensed professional engineer in the state of Arkansas. He serves as program evaluator (PEV) for ABET Engineering Accreditation Commission (EAC). He has co-authored three monographs, ten book chapters, fifty journal papers, and over one hundred peer-reviewed conference publications (ORCID ID: 0000-0001-8375-290X).

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Dr. Shengyi Wang is an assistant professor of Electrical Engineering at the University of Arkansas at Little Rock. He received his Ph.D. in Electrical Engineering from Temple University, Philadelphia, in 2022. His research centers on Artificial Intelligence for power and energy systems, with particular emphasis on large language model (LLM) applications for grid intelligence, power system and data center flexibility aggregation, virtual inertia scheduling and control for inverter-based resource (IBR)-dominated power systems, and data-driven optimization theory and computational approaches. His work integrates learning, optimization, and control to enable reliable, efficient, and intelligent grid operations, advancing AI and energy innovations for next-generation power systems.

Dr. Yunxiang Zhang is an Assistant Professor of Electrical and Computer Engineering at the University of Arkansas at Little Rock. He received his Ph.D. from SUNY Binghamton University in 2025. His research focuses on FPGA-based acceleration for AI in edge and wearable devices, including hardware–software co-design for AI accelerators, neural signal transmission and compression, multiplier-less models for neural architecture search, and in-memory computing.

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Dr. Shweta U. Dabetwar is an Assistant Professor of Mechanical Engineering at the University of Arkansas at Little Rock and the Principal Investigator of the Material Intelligence and Prognostics (MIP) Lab. Her pioneering research resides at the intersection of traditional mechanics and advanced Artificial Intelligence, focusing on developing “physics-informed AI” to enhance the reliability of renewable energy systems and civil infrastructure. By integrating deep learning with non-destructive evaluation (NDE) techniques like infrared thermography, she creates frameworks that allow critical components—such as wind turbine blades and Li-ion batteries—to self-diagnose and predict failure.
A dedicated “Teacher-Scholar,” Dr. Dabetwar is a certified practitioner of Scientific Teaching, employing active learning to mentor award-winning engineering students. She holds a Ph.D. from Texas Tech University and serves as the 4th Vice Chair of the ASME Safety Engineering and Risk Analysis Division. Her innovative approach to intelligent prognostics recently earned her the 2026 Rising Faculty Award, cementing her role as a leader in creating safer, more resilient engineering systems.

Dr. Sujan Ghosh is an Assistant Professor of Mechanical Engineering at the University of Arkansas at Little Rock and Director of the Advanced Materials and Tribology Laboratory. He earned his Ph.D. in Mechanical Engineering from the University of Arkansas, Fayetteville, and brings over 10 years of research and academic experience in materials science, surface engineering, and tribology. His research focuses on the design and characterization of high-performance ceramics and polymer-based composites for extreme and demanding environments, with particular emphasis on mechanical reliability, surface modification, coatings development, and tribological performance. Dr. Ghosh’s work integrates advanced experimental characterization, applied mechanics, and interdisciplinary engineering approaches to develop durable, lightweight materials for aerospace, energy, automotive, and advanced manufacturing applications.
He has authored 21 peer-reviewed journal publications and contributed to multiple collaborative research initiatives addressing challenges in lightweight materials, wear-resistant coatings, solid lubrication, and sustainable material solutions. As Director of the Advanced Materials and Tribology Laboratory, he leads innovative projects that bridge fundamental materials science with scalable engineering applications, fostering collaborations with industry partners, government agencies, and academic institutions. Dr. Ghosh is deeply committed to student mentorship, guiding undergraduate, master’s, and doctoral researchers in high-impact projects while promoting hands-on learning and professional development. Through his research, teaching, and service, he strives to advance next-generation materials technologies, enhance system performance and sustainability, and prepare a diverse and future-ready engineering workforce.

Dr. Weijun Shen is an Assistant Professor of Mechanical Engineering at the University of Arkansas at Little Rock. He received his Ph.D. in Industrial and Systems Engineering from the University of Wisconsin–Madison in 2025, with a focus on Advanced Manufacturing and a minor in Mechanical Engineering. Prior to this, he was in the Ph.D. program at Iowa State University, co-majoring in Mechanical Engineering and Industrial and Manufacturing Systems Engineering, and was affiliated with the Center for Nondestructive Evaluation, before transferring to UW–Madison.
Dr. Shen’s research focuses on Design for Additive Manufacturing and Additive Manufacturing Processes, with particular emphasis on achieving functional performance through lattice and cellular structure manipulation. His work also involves non-destructive evaluation methods, including 3D scanning and point cloud data characterization, to enable accurate geometric assessment and process-informed design.

Dr. Nalaka Amarasiri is an Assistant Professor of Mechanical Engineering at the University of Arkansas at Little Rock. He received his PhD in Systems Engineering (Mechanical Engineering concentration) from the University of Louisiana at Lafayette in 2024. His research lies at the intersection of dynamics, control theory, and intelligent systems, with core interests in robotics, smart manufacturing, and healthcare applications. He integrates physics-based modeling and control strategies with artificial intelligence to develop robust and adaptive autonomous systems. He also investigates the analysis and control of thermal and energy systems. Through his work, he aims to bridge theoretical development with real-world applications in advanced engineering systems.