Speakers, bios and talks

Abstract: Robust control techniques enable a clear way to quantify a system's vulnerability to destabilization attacks.  This talk will discuss how to measure a system's Intrinsic Destabilization Vulnerability with respect to dynamic coordinated or MIMO attacks.  Unlike the single link, or SISO case, MIMO attacks require an additional step of threat modeling, characterizing communication constraints that may or not restrict an attacker's capabilities.  When certain types of communication constraints are part of the threat model, we show how measuring Intrinsic Destabilization Vulnerability relies on mu-analysis.  We illustrate these ideas on a power system model, the IEEE 14-Bus System, and we demonstrate the impact using a software tool designed to enable utility managers to study their system's vulnerabilities to destabilization attacks under various threat models. 

Bio: Benjamin Francis is the Chief Scientist at Achilles Heel Technologies, USA. Ben received a Ph.D. in Physics from BYU and a B.S. in Physics from BYU-Idaho.  He spent a year and a half as a postdoctoral fellow at BYU, where he worked on two projects: one developing model reduction methods for complex systems, and the other working in superconductivity theory.  Ben later joined Achilles Heel Technologies (AHT) to lead a DOE-funded project to develop mathematical techniques to analyze models of electrical power systems and illuminate where these systems are vulnerable and need protection.  He currently is the Chief Scientist at AHT.

Abstract: Robust control is fundamentally concerned with techniques for designing controllers that guarantee stability in the presence of uncertainty.  These methods, however, also reveal the smallest perturbations that will destabilize a given system.  Viewing such perturbations as potential attacks that could systematically be levied against the system, this measure of the size of the smallest destabilizing attack then becomes an effective way to quantify the Intrinsic Destabilization Vulnerability of a system.  This talk introduces a system's Intrinsic Destabilization Vulnerability with respect to single link or SISO attacks.  Linearization is presented as a vulnerability-preserving model-simplification technique with respect to these destabilization attacks, and Dynamical Structure Functions are used to visualize the attack surface of the system under various threat models, or assumptions about an attacker's capabilities.  The technique is then demonstrated on a variety of system models from different critical infrastructure sectors.

Bio: Emma Reid  is a Research Associate at Oak Ridge National Laboratory. She completed her PhD in Applied Mathematics at Purdue University in August of 2021. Her PhD research consisted of developing flexible super resolution techniques that incorporate data fusion across image domains. Her current research interests include computational imaging, data fusion, explainability of neural networks, and robust control methods for studying the security and resilience of cyber-physical-human systems. 

Abstract: Utility networks, such as those for electricity and water supply, are rapidly aging and are increasingly susceptible to malfunctions. Detecting, pinpointing, and mending these faults is typically complex, slow, and expensive, requiring substantial hands-on effort. Concurrently, there is a rise in the use of flow and potential meters to oversee these systems. In this presentation, we explore using such monitoring data to locate faults within extensive utility networks. We examine two distinct scenarios. First, we consider a 3-phase medium-voltage electrical network, and we outline the framework for a fault localization method that utilizes both data and models. In the second scenario, we address a comparable issue within a water distribution network, noting the significant contrasts like the reduced frequency of sensor readings and the network's more complex, nonlinear flow resistance. We provide a theoretical foundation for distinguishing between various leaking pipes using limited measurement data.

Bio: Henrik Sandberg is a Professor at the Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden. He received the M.Sc. degree in engineering physics and the Ph.D. degree in automatic control from Lund University, Lund, Sweden, in 1999 and 2004, respectively. From 2005 to 2007, he was a Postdoctoral Scholar at the California Institute of Technology, Pasadena, USA. In 2013, he was a Visiting Scholar at the Laboratory for Information and Decision Systems (LIDS) at MIT, Cambridge, USA. He has also held visiting appointments at the Australian National University and the University of Melbourne, Australia. His current research interests include security of cyber-physical systems, power systems, model reduction, and fundamental limitations in control. Dr. Sandberg was a recipient of the Best Student Paper Award from the IEEE Conference on Decision and Control in 2004, an Ingvar Carlsson Award from the Swedish Foundation for Strategic Research in 2007, and a Consolidator Grant from the Swedish Research Council in 2016. He has served on the editorial boards of IEEE Transactions on Automatic Control and the IFAC Journal Automatica. He is Fellow of the IEEE. 

Abstract: State estimation is ubiquitous in control systems, especially for cyber-physical systems, which are ‘physical’ systems that communicate over the ‘cyber’ domain. In today’s increasingly connected world, cyber-physical systems permeates critical infrastructure such as energy systems. The introduction of the ‘cyber’ layer has an important consequence: the transmitted data is susceptible to corruption with malicious intent. This has created a line of works that addresses state estimation when some of the sensor measurements are subject to attacks. The algorithm is called ‘secure’ when the bound on the estimation error is independent of the attacks. In this talk, I will present recent results on secure state estimation for cyber-physical systems where measurements are transmitted over a packet-based network in an asynchronous manner. We guarantee that our state estimation algorithm is secure for any inter-transmission intervals. I will then illustrate the application of this work in the secure monitoring of a low-voltage power distribution system, which is part of project RESili8, under the ERA-Net Smart Energy Systems scheme. I will then outline my vision for secure estimation of cyber-physical systems.

Bio: Michelle Chong is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, the Netherlands. Michelle received the Bachelor of Engineering degree in Electrical Engineering, and the Ph.D. degree in mathematical control theory from the Department of Electrical and Electronic Engineering, the University of Melbourne, in 2008 and 2013, respectively. From 2013 to 2015, she was a postdoctoral researcher at the Department of Electrical and Computer Engineering, the University of California Santa Barbara, USA. From 2015 to 2017, she was a postdoctoral researcher at the Department of Automatic Control, Lund University, Sweden and from 2018 to 2019, at the Division of Decision and Control Systems, KTH Royal Institute of Technology, Sweden. Since 2020, she is an Assistant Professor at the Department of Mechanical Engineering, TU Eindhoven, the Netherlands. Michelle was the 2013 recipient of the American Australian Association's postdoctoral fellowship, and won the best paper award at the 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS). She is currently serving on the editorial board of the IEEE CSS Letters, and the IFAC Journal Nonlinear Analysis: Hybrid Systems, as well as the conference editorial board of the IEEE CSS conferences. Her research interest lies in the secure estimation and control for hybrid systems. 

Abstract: Correct-by-construction synthesis represents a transformative approach at the intersection of formal methods and control theory, particularly in the design of safety-critical systems. Unlike the traditional, iterative (re)design-verify-validate cycle, this methodology advocates for the continuous refinement of formal specifications, interconnected through rigorous chains of formal proofs. This paradigm ensures that systems are inherently correct by design. Significant progress has been made in broadening the scope of correct-by-construction synthesis, especially for cyber-physical systems (CPS) that integrate discrete-event control with continuous dynamics. This expansion has been driven by the integration of symbolic techniques with principled state-space reduction methods, enhancing the applicability of correct-by-construction synthesis to complex control systems. However, the landscape shifts dramatically when considering security-critical control systems. Security properties are often verified retrospectively, which fundamentally conflicts with the proactive nature of the correct-by-construction paradigm. To realize the full potential of correct-by-construction synthesis in security-critical domains, security considerations must be elevated to the same level of importance as safety requirements. Recent advancements in understanding opacity—a key subclass of security properties—and the emerging framework of hyperproperties, which unify security and safety concerns, provide a timely opportunity to address this challenge. In this talk, I will outline our vision for secure-by-construction synthesis, highlighting recent breakthroughs that lay the groundwork for a unified, holistic approach to the design of secure and safe CPS.

Abstract: In this talk, we discuss synchronization of heterogeneous pulse-coupled oscillators (PCOs), where some oscillators might be faulty or malicious. This is relevant to applications in clock synchronization for wireless sensor networks. The oscillators interact through identical pulses at discrete instants and evolve continuously with different frequencies otherwise. Heterogeneity refers to the clocks having different frequencies, which have not been studied much for pulse-based coupled oscillators in the literature. Despite the presence of misbehaviors, benign oscillators aim to reach synchronization. To achieve this objective, we develop a resilient synchronization protocol by adapting the real-valued mean-subsequence reduced (MSR) algorithm to pulse-based interactions. In the protocol, each normal oscillator periodically counts the received pulses to detect possible malicious behaviors. By disregarding suspicious pulses from its neighbors, the oscillator updates both its phases and frequencies.

Bio: Hideaki Ishii is a Professor and IEEE Fellow at the Department of Information Physics and Computing, University of Tokyo, Japan.  

Abstract: The emergence of new communication technologies allows us to expand our understanding of distributed control and consider collaborative decision-making paradigms. With collaborative algorithms, certain local decision-making entities (or agents) are enabled to communicate and collaborate their actions with one another to attain better system behavior. By limiting the amount of communication, these algorithms exist somewhere between centralized and fully distributed approaches. In this talk, we explore this collaborative paradigm and identify potential opportunities associated with collaborative decision-making in distributed resource allocation problems. In particular, we will provide a characterization for how the level and structure of collaboration impacts the efficiency of the emergent collective behavior. Note that having such a characterization is essential for identifying whether or not the communication cost necessary to facilitate the desired collaborations is worth the gains in system welfare.

Bio: Jason R. Marsden is a Full Professor in the Department of Electrical and Computer, Engineering at the University of California, Santa Barbara. Jason received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA, under the supervision of Jeff S. Shamma, where he was awarded the Outstanding Graduating PhD Student in Mechanical Engineering. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2015, Jason joined the Department of Electrical and Computer Engineering at the University of California, Santa Barbara.  Jason is a recipient of the ONR Young Investigator Award (2015), NSF Career Award (2014), the AFOSR Young Investigator Award (2012), the American Automatic Control Council Donald P. Eckman Award (2012), and the SIAM/SGT Best Sicon Paper Award (2015).  Furthermore, Jason is also an advisor for the students selected as finalists for the best student paper award at the IEEE Conference on Decision and Control (2011, 2016, 2017) and American Control Conference (2020). Jason's research interests focus on game theoretic methods for the control of distributed multiagent systems.

Abstract: Achieving climate-resilient, least-cost investment strategies for joint electricity and gas infrastructure is critical for decarbonization. We present an approach to integrate supply-demand uncertainties at desirable spatial and temporal resolutions in planning models, addressing the challenges of complex energy vector interactions and variability in renewable energy. Our approach uses machine-learning assisted adaptive robust optimization to navigate these uncertainties effectively. By employing deep generative learning, we identify realistic adversarial scenarios, enhancing the robustness of infrastructure planning. Additionally, we leverage spatiotemporal aggregation to streamline decision-making, ensuring both cost-effectiveness and adaptability to weather-driven uncertainties. This framework demonstrates significant potential to improve decarbonization efforts by coordinating renewable energy investments and optimizing energy storage systems, providing a robust toolkit for future energy grid resilience.

Bio: Saurabh Amin is an Associate Professor at the Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, USA. 

Abstract: Achieving climate-resilient, least-cost investment strategies for joint electricity and gas infrastructure is critical for decarbonization. We present an approach to integrate supply-demand uncertainties at desirable spatial and temporal resolutions in planning models, addressing the challenges of complex energy vector interactions and variability in renewable energy. Our approach uses machine-learning assisted adaptive robust optimization to navigate these uncertainties effectively. By employing deep generative learning, we identify realistic adversarial scenarios, enhancing the robustness of infrastructure planning. Additionally, we leverage spatiotemporal aggregation to streamline decision-making, ensuring both cost-effectiveness and adaptability to weather-driven uncertainties. This framework demonstrates significant potential to improve decarbonization efforts by coordinating renewable energy investments and optimizing energy storage systems, providing a robust toolkit for future energy grid resilience.

Abstract: In this talk, we will explore counterfactual explainability, a key subfield of machine learning focused on interpreting model predictions by identifying the minimal changes to an input required to alter the model's output. We will begin by introducing the foundational concepts of this field and then delve into its practical applications, particularly its relevance to detecting vulnerabilities in complex cyber-physical systems. Finally, we will present recent advancements in data-driven algorithms for CPHS that have been inspired by this growing intersection between explainability and system security.

Bio: Cristian R. Rojas was born in 1980. He received the M.S. degree in electronics engineering from the Universidad Técnica Federico Santa María, Valparaíso, Chile, in 2004, and the Ph.D. degree in electrical engineering at The University of Newcastle, NSW, Australia, in 2008. Since October 2008, he has been with the Royal Institute of Technology, Stockholm, Sweden, where he is currently Professor of Automatic Control at the School of Electrical Engineering and Computer Science. His research interests lie in system identification, signal processing and machine learning. Prof. Rojas is a Senior Member of IEEE, of the IEEE Technical Committee on System Identification and Adaptive Processing (since 2013), and of the IFAC Technical Committee TC1.1. on Modelling, Identification, and Signal Processing (since 2013). He has been Associate Editor for the IFAC journal Automatica and for the IEEE Control Systems Letters (L-CSS).