Plenary Speakers

Pramod Khargonekar

University of California, Irvine

Pramod Khargonekar

Pramod Khargonekar was Chair of the Department of Electrical Engineering and Computer Science from 1997 to 2001 and held the position of Claude E. Shannon Professor of Engineering Science at the University of Michigan. From 2001 to 2009, he was Dean of the College of Engineering and Eckis Professor of Electrical and Computer Engineering at the University of Florida till 2016. After serving briefly as Deputy Director of Technology at ARPA-E in 2012-13, he served the head of the Directorate of Engineering at the National Science Foundation from 2013 till June 2016. He was Vice Chancellor for Research and Distinguished Professor of Electrical Engineering and Computer Science at the University of California, Irvine, from 2016 to 2025, where he is currently Distinguished Professor of Electrical Engineering and Computer Science.

Khargonekar’ research has spanned fundamental control theory and applications to manufacturing, energy systems, climate change mitigation, adaptation, and resilience. He is currently working on the integration of AI and ML into cyber-physical-human systems. In his leadership roles, he has enabled science and engineering research communities to pursue important multidisciplinary research aimed at major societal problems. He has received numerous honors and awards including IEEE Control Systems Award, IEEE Baker Prize, IEEE Control Systems Society Bode Lecture Prize, IEEE Control Systems Society Axelby Award, NSF Presidential Young Investigator Award, AACC Eckman Award, Distinguished Alumni and Distinguished Service Awards from IIT Bombay, and Inaugural Hall-of-Fame Inductee, Electrical and Computer Engineering Department, University of Florida. He is a Fellow of IEEE, IFAC, and AAAS.

Schedule: Day 1 – Tuesday 23 June 2026
Location: G41, Frederick Douglass Centre, Newcastle

It is not an exaggeration to say that we are witnessing dramatic developments in machine learning and artificial intelligence technologies. Control theory has had and continues to have common goals and intersections with machine learning and artificial intelligence. A major intellectual and technological challenge for the future is how we can the best of what these various fields can offer.  In this talk, I will address this question in the setting of cyber-physical systems (CPS). I will describe notions of cognitive CPS as well as Physical AI. I will discuss how we may think about concepts of automation, autonomy, and they can offer guidelines on the future of intelligent machines. 

Naira Hovakimyan

University of Illinois Urbana-Champaign (UIUC)

Naira Hovakimyan

Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 500 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award, the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019 recipient of AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. In 2015 and 2023 she was awarded the UIUC Engineering Council Award for Excellence in Advising. In 2024 she was recognized as the winner of the College Award for Excellence in Translational Research, and in 2025 she was recognized for Excellence in Graduate Student Mentoring. She is Fellow of AIAA, IEEE, ASME, IFAC, and senior member of National Academy of Inventors. She has been named a Distinguished Lecturer for IEEE CSS for 2026-2028. She is a co-founder and chief scientist of Intelinair. Her work in robotics for elderly care was featured in the New York Times, on Fox TV, CNBC, and her recent NASA ULI award on flying cars led her to a live interview on Cheddar Innovates and many other media platforms. Her research interests are in control and optimization, autonomous systems, machine learning, neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care.

Schedule: Day 3 – Thursday 25 June 2026
Location: G41, Frederick Douglass Centre, Newcastle

Learning-based control paradigms have seen many success stories with autonomous systems in recent years. A typical architecture in these systems involves layers for perception, planning and control, wherein each of these layers uses different tools and metrics for assessing robustness and performance. For example, the planners — that use vision-based sensors to update the navigation and motion planning — operate largely relying on distributionally robust stochastic optimal control, whereas the low-level controller can be a deterministic controller with its conventional gain and phase (time-delay) margin. We present a new analysis framework for addressing this ontology challenge inherent to autonomous systems. We derive distributional robustness guarantees for deterministic L1 adaptive controllers that can be used by any stochastic planner without facing a language barrier. The combined planner-controller framework can serve as foundation for development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications.  

Information about the second plenary speaker will be announced shortly.