This CAREER project develops formal verification and controller synthesis schemes for complex cyber-physical systems (CPS) with unknown closed-form models by embracing ideas from control theory, computer science, and operations research. Emerging examples of such systems include autonomous cars, autonomous transportation networks, smart grids, and integrated medical devices. The main novelty of this project lies in bypassing the model identification phase and directly verifying or synthesizing control software for CPS against complex safety requirements using just data collected from their behaviors. This project also quantifies rigorously a confidence guarantee on the verification outcomes or the correctness of synthesized control software, which can be improved based on the amount of data. Given an acceptable confidence, unfortunately, the required number of data grows rapidly with the size of the system. This is known as the sample complexity. To tackle this issue, particularly, for large-scale CPS, the project finally proposes a divide and conquer strategy by breaking the data-driven verification or controller synthesis problems into semi-independent ones, where solving each subproblem requires a much smaller amount of data. The research outcomes of this project will contribute to the long term education plan of the PI by i) developing unified courses on CPS with an ?end-to-end view,? starting from the foundations of control and discrete systems theory and moving to hardware/software implementations; ii) bringing hands-on learning to those courses by the platforms and benchmarks developed in this project; and iii) finally, improving undergraduate retention rates by leveraging the outreach programs at the University of Colorado Boulder to recruit first generation and underrepresented engineering students and engage them in the platforms used in this project.
This project proposes a scalable data-driven approach for formal verification and synthesis of control software for CPS with unknown models (a.k.a. black-box systems). To do so, given temporal logic requirements (e.g., those expressed as linear temporal logic formulae) for CPS, they will be decomposed into simpler tasks based on the structures of automata representing them. Then, those simpler tasks are tackled by constructing so-called barrier functions using data collected from the systems. Particularly, the conditions over barrier functions for those simpler tasks are first formulated as robust convex programs (RCP) which are technically semi-infinite linear programs. Solving those RCP directly are not tractable due to unknown models. Instead, this project considers a set of data collected from the system and solves scenario convex programs (SCP), which are finite linear programs. Barrier functions resulted by solving SCP are combined to verify the given requirement or to provide a controller enforcing it. The project also quantifies rigorously a confidence (a.k.a. out-of-sample performance guarantee) on the verification outcomes or the correctness of synthesized controllers. To tackle the underlying sample complexity for large-scale CPS, this project proposes an adaptive sampling and a modular data-driven schemes by exploiting the natural structure present in the system. Finally, the proposed algorithms will be implemented into open-source software tools to automate the proposed data-driven techniques and evaluated on Artificial Pancreas systems and a team of scale-model autonomous vehicles.
Autonomous Cyber-Physical Systems (CPS), such as self-driving cars, and drones, powered by deep learning and AI based perception, planning, and control algorithms, are forming the basis for significant pieces of our nation?s critical infrastructure, and present direct, and urgent safety-critical challenges. A major limitation with current approaches towards deploying autonomous CPS is in ensuring that the system operates safely, and reliably in situations that do not happen very often under normal operating conditions and are therefore difficult to gather data on. For instance, a self-driving car trained to follow the ?rules of the road? will perform well most of the time, but it is the unusual conditions, the edge cases, which pose the hardest safety challenges. This project brings forward an innovative idea ? can increasing the agility of an autonomous vehicle improve its safety? This notion is somewhat controversial since agility (like that of race cars) is more frequently associated with decreased safety margins.
Motivated by these challenges underlying real-world testing and safety for autonomous vehicles, the goal of this project is to develop the foundations for autonomous cyber-physical systems along two dimensions: agility, safety, and their interplay. The project is centered on (1) increasing agility for AVs by developing new methods for agile motion planning, so they can maneuver at the limits of their handling and control when it matters most to escape potentially unsafe conditions, (2) automated reasoning about uncertain dynamic situations that may occur during autonomous CPS operation, and (3) developing novel methods for automatically generating testing and edge-case scenarios at design time, to explore scenarios under which the autonomous CPS would fail. The proposed methods will be evaluated on scaled autonomous vehicles testbeds, on photorealistic and high-fidelity simulation platforms, and on full scale AV prototypes. The project will also consider not just safety of an unoccupied AV ? but one in which passengers may be present. This CAREER project includes designing exciting new courses, and initiatives centered around autonomous racing to engage with research and mentoring for K-12, undergraduate, and graduate students. The project aims to ensure that students cultivate a holistic view of cyber-physical systems and autonomous systems by drawing stronger connections between theory, applications, and hands-on platform development. The project will help enhance the capabilities of autonomous cyber-physical systems and facilitate with their safe deployment.
Dr. Madhur Behl is an Associate Professor in the departments of Computer Science, and Systems and Information Engineering, and a member of the Cyber-Physical Systems Link Lab at the University of Virginia.
He received his Ph.D. (2015) and M.S. (2012), in Electrical and Systems Engineering, both from the University of Pennsylvania; and his bachelor's degree (2009) in ECE from PEC University of Technology in India.
He is the team principal of the Cavalaier Autonomous Racing team. Behl is also the co-founder, organizer, and the race director for the F1/10 (F1tenth) International Autonomous Racing Competitions. He is an associate editor for the SAE Journal on Connected and Autonomous Vehciles, and a guest editor for the Journal of Field Robotics. He also serves on the on the Academic Advisory Council of the Partners for Automated Vehicle Education (PAVE) campaign, to help promote public understanding about autonomous vehicles and their potential benefits. Dr. Behl is an IEEE Senior Member and the recipient of the NSF CAREER Award (2021).
The goal of this project is to achieve high-bandwidth underwater wireless communication using a flock of small Autonomous Underwater Vehicles (AUVs) that relay a laser beam from the seabed to the surface of the ocean. The approach is advanced control of specially-designed AUVs, along with prediction of ocean currents, so that each AUV unit can reliably receive the signal from a unit at a lower depth, amplify the signal and send it to the next unit above, until the signal reaches the surface where it can easily reach satellites and hence anywhere in the world. Underwater wireless data communication is one of the most important outstanding problems in ocean engineering, impeding nearly all major research expeditions and inhibiting industrial development. This is because radio waves are heavily absorbed by water (e.g. no cell phones, Wi-Fi, or Global Positioning System (GPS) underwater), and acoustic waves have low data-transfer rates. A real-time seabed monitoring technology, as proposed here, gives researchers and engineers a novel and unique tool to carefully perform, watch, and assess deep ocean explorations and operations.
The key technical objective is to demonstrate the first proof-of-concept of wireless high-bandwidth underwater data communication via a flock of AUVs. Maximum range (minimum absorption) of electromagnetic waves in water is obtained for visible light. Therefore, pointing precision and agility of AUV units are the key challenges to success. The proposed controlled swarm motion uses a hierarchical control architecture comprising a combination of centralized and decentralized controllers to maximize the communication line's autonomy, reliability, and robustness. The fabricated AUV units feature a three-layer stabilization system that provides the needed agility, attitude accuracy, and stability for each unit.
Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies. On the other hand, physics-model-based engineering methods provide analyzable behaviors and verifiable properties, but do not match the performance of DNN systems. These considerations motivate the work in this project which aims at physics-model-based neural networks (NN) redesign, yielding HyPhy-DNN: a hybrid self-correcting physics-enhanced DNN framework. HyPhy-DNN will provide the performance of DNNs and the analyzability and verifiability of physical models, thus providing a foundation for verifiably safe self-driving cars, drones, and other CPS systems. Moreover, the HyPhy-DNN will fundamentally advance the integration of deep learning and robust control to enable safety- and time-critical CPS to safely operate with high performance in unforeseen and dynamic environments.
The HyPhy-DNN will make three innovations in redesigning NN architecture: (i) Physics augmentations of NN inputs for directly capturing hard-to-learn physical quantities and embedding Taylor series; (ii) Physics-guided neural network editing, such as removing links between independent physics variables or fixed weights on links between certain physics variables to maintain the known physics identity such as in conservation laws; and (iii) Time-frequency-representation filtering-based activations for filtering out noise having dynamic frequency distribution. The novel architectural redesigns will empower the HyPhy-DNN with four targeted capabilities: 1) controllable and provable model accuracy; 2) maximum avoidance of spurious correlations; 3) strict compliance with physics knowledge; and 4) automatic correction of unsafe control commands. Finally, the safety certification of any DNN will be a long-term challenge. Hence, the HyPhy-DNN shall have a simple but verified backup controller for guaranteeing safe and stable operation in dynamic and unforeseen environments. To achieve this, the research team will integrate HyPhy-DNN with an adaptive-model-adaptive-control (AMAC) framework, the core novelty of which lies in fast and accurate nonlinear model learning via sparse regression for model-based robust control. The HyPhy-DNN and AMAC are complementary and will be interactive at different scales of system performance and functionalities during the safety-status-cycle, supported by the Simplex software architecture, a well-known real-time software technology that tolerates faults and allows online control system upgrades.
http://publish.illinois.edu/cpsintegrationlab/people/lui-sha/
This project seeks to develop low-interference mitigation options for cybersickness; that is motion sickness like symptoms in response to virtual reality use. With several new virtual reality (VR) systems such as the Oculus rift, Google VR, and HTC Vive now available to the general population with increased usage in education and training, the need for cybersickness mitigation options has dramatically increased. The main purposes of the research are to develop a test suite to allow for rapid testing of these new cyberphysical systems, and to explore a potential set of low interference cybersickness mitigation options. Currently, there are very few effective mitigation options and most of these options are intrusive (e.g., severely limiting duration or motion-sickness medication) which can make VR training and education applications unusable. This project pursues to low-interference methods of by exploring color overlays, contrast, and realism. Color because human eyes respond differently to different colors. Contrast because human eyes respond differently to high versus low contrasts objects. Lastly, realism due to highly realistic or very low realistic VR applications in the past noting less cybersickness than average. The longer-term goal will be the in the development of a cybersickness model so that individual users can use to determine if, and how many, mitigation options would be needed for a particular cyberphysical system application and headset.
This proposal seeks to develop low-interference mitigation options for cybersickness and a standardized test suite for new cyberphysical systems for later ease of testing and comparisons. Cybersickness is defined here as motion sickness-like symptoms occurring in individuals as they interact with video displays. Current estimates indicate, on average, that there is a mere 15 minutes of safe usage before cybersickness starts to occur in population groups, and in as little as 30 seconds for some users. To be effective in training and education, the systems must not make users ill. Several aspects of the research involve the identification of possible mitigation options that will not interfere with the primary purpose of an application. Therefore, the objectives of the research are 1) to develop a test suite to allow for rapid testing of new VR systems and methods to provide consistency in the results, 2) to analyze a possible set of low-interference mitigation options (specifically, components of realism), and 3) to integrate the results into a method to quickly predict levels cybersickness. Currently, there are very few effective mitigation options and most of these options are intrusive (e.g., severely limiting duration of use or motion-sickness medication). Past research suggests realism may be a strong factor, but the ?why? and ?what? factors of this research are currently unknown. Since realism has less restriction of content, experiments are designed to identify objective components of realism that affect cybersickness and include hue, blur, contrast, and stabilizing components. The results of these studies, such as the realism mitigation options, can be used directly in the creation of new applications for sensitive users. The test suite will allow for a means to test new cybersickness features in a consistent manner. The long-term goal of the predictive models can be used to advise individual users on appropriate use of VR, rather than generic warnings. The methods involved are building a test environment and examining the effect different realism features to determine their effect on cybersickness. The test environment will be built by reviewing past publications for interaction and current VR application environments to ensure a wide set of environment types can be considered. The mitigation experiment will be run in parallel and following this development to create of ?plug-in? ability to then test the mitigation options of color, contrast, and realism. Each of these mitigation experiments will be run in a with-in subject design if possible. The results of all three experiments will be analyzed to filter the components of realism that affect cybersickness. The results of all experiments with be merged with past studies of cybersickness to further develop a predictive model.
The objective of this CAREER project is to develop a theoretical and computational framework for the co-design of information and incentive mechanisms targeted at humans in Societal-Scale Cyber-Physical Systems (SCPS) in order to encourage efficient shared resource consumption while mitigating unintended consequences. The application focus is on intelligent transportation systems, a prototypical SCPS with humans in the loop, rapid technology adoption, and emerging mobility markets. CPS and internet of things (IoT) infrastructure is pervasive in the mobility landscape, allowing operators and intelligently augmented humans to make decisions rapidly as they interact with one another and engage with the system. Market mechanisms that support interactions occur on multiple timescales, are constrained by CPS dynamics, and are exposed to exogenous uncertainties, information asymmetries, and behavioral aspects of human decision-making. Through the design of algorithms with guarantees for learning in and shaping of SCPS, this project will address two fundamental components missing in the state-of-the-art: (i) models that capture the interactions and learning processes of different SCPS stakeholders, and (ii) certifiable algorithms with high-probability guarantees for the co-design of adaptive information and incentive mechanisms that achieve measurable improvement in system-level performance while ensuring individual-level quality of service and avoiding discriminatory policies. The validation approach uses a data-informed experimental platform with simulation and living lab components. The research agenda will aid in revising the design of operational mechanisms for both private and public CPS-enabled mobility platforms to include efficiency and measurable fairness as valued criteria. The proposed agenda includes an integrated research and education plan: (i) course development leveraging the experimental platform; (ii) undergrad research in which students aid in building out the experimental platform, and engage with municipal/industry partners; (iii) development of Girls In Research Labs (GIRLs), a week-long summer program in which middle school girls explore research labs across campus through hands-on projects.
Contributions to the science of CPS will be made through the study of intelligent infrastructure, with a special focus on behavior unique to human-in-the-loop CPS, and applications to multi-modal transportation systems. The technical plan is based on fundamental methods in decision sciences (control theory, game theory, behavioral economics, and mechanism design), statistics, and online learning. The research agenda is organized along two key thrusts: (i) algorithms for learning in SCPS and (ii) algorithms for shaping SCPS via incentives and information. The proposed tool set will enable analysis of multi-timescale decision-making of autonomous agents, including humans, coupled with CPS infrastructure in resource constrained environments, and will allow for the certifiable design of algorithms for learning and control (e.g., co-design of slow policy changes and real-time control). The modeling, synthesis, and validation approach will provide a principled, scientific basis for SCPS engineering design and operations, and supports CPS education by providing a platform for future engineers to discover realities associated with real-world implementation (e.g., socio-technical constraints). The unique perspective of co-designing information and incentives will also lead to new tools for modeling risk and uncertainties and thus, expose potentially new approaches to resilience in the engineering of CPS.
In the US, agricultural drainage infrastructure benefits >22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains. Climate change and agricultural intensification are causing farmers to increase the extent and intensity of drainage leading to a pressing need to balance productivity, profitability, and environmental quality when making drainage decisions. Further, because drainage systems include individually-owned and community-owned drains, decision-making involves complex techno-economic social issues together with understanding biophysical processes and requires balancing the needs of individual farmers, drainage communities, and surrounding regions. This project will develop an integrated decision-making platform to facilitate community decision making for precise prediction and management of drainage effects on water flow, crop production, farm net returns, and nutrient loss. The platform data will be made possible by new agricultural sensors and robots, innovations in behavioral economics and analytics tools. Development of the drainage decision-making platform will be guided by farmer stakeholders?including, the Iowa and Illinois Drainage Districts Associations, a national-level agricultural drainage management coalition, and directly with farmers?forming a continuous learning environment across scientists and farmers that fosters adoption of new technologies and transfer of the research process to the next generation of scientists, engineers, and agricultural professionals.
The project will build upon a suite of biophysical and social science advances in multiple areas, including bioinspired robotic snake sensors, in-situ soil nutrient sensors, computational modeling, and socioeconomics. The snake sensors will navigate through agricultural drainage networks to generate a high spatial resolution data stream about flow rates and nitrate concentrations throughout the belowground network. The soil sensors will enable continuous monitoring of nitrate dynamics. Process-based ecohydrological models, subsurface water transport models, and multiple spatiotemporal sensor outputs will be integrated to obtain high-resolution information about distributions of water and nitrate. Biophysical scenario analyses will assist decision-making for different agricultural management scenarios to balance resource use efficiency, profitability, and environmental performance. Socioeconomic science innovations will be integrated by learning how current systems are managed in the context of various heterogeneities across individuals and drainage districts, such as demographics, farm size, and presence of wetlands, and how new information provided by the proposed infrastructure interacts with human incentives and choices and consequent policy making.
Liang Dong is an associate professor of electrical and computer engineering at Baylor University. His research interests include Digital Communications and Signal Processing, Green Wireless Networks, Cyber-Physical System and Security, Social Internet of Things, and E-health Applications.
Liang Dong is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the American Physical Society (APS), and a member of the American Society for Engineering Education (ASEE). He served on the executive board of IEEE West Michigan Section from 2006 to 2011 and the executive board of ASEE North Central Section from 2007 to 2008. He also served as a TPC member for IEEE HealthCom 2015, IEEE GlobalSIP 2015 and IEEE GlobalSIP 2016, and a session chair for IEEE WCNC 2013 and IEEE GlobalSIP 2016. He is a member of Sigma Xi, Phi Kappa Phi, and Tau Beta Pi, and a faculty advisor of Eta Kappa Nu.
The goal of the project is to develop a methodology, supported by tools, that uses formal methods to gain clarity into the standards of network protocols and test the conformance of implementations to these standards. The work will be demonstrated on QUIC, a new complex protocol that is currently carrying about 10% of the internet traffic and is likely to carry much more of it in the near future. The standardization of QUIC is ongoing under the IETF. The software and the experimental results developed under this project will be stored in GitHub. The methodology itself will be widely disseminated by the usual means (publications, talks, etc.). The experimental results will be shared with the developers of QUIC implementations and the IETF. The project will be used to train graduate students who will be exposed to state-of-the-art techniques as well as collaboration between academia (UIC) and industry (Microsoft). The material of the course will be integrated in UIC classes. UIC is a Minority Serving Institution (MSI), Asian American and Native American Pacific Islander-Serving Institution (AANAPISI) and an Hispanic Serving Institution (HSI).
Network protocol standards are described by RFCs: English-language documents that provide extensive guidance for those implementing the protocol, but are nonetheless ambiguous and broadly open to interpretation. The primary mechanism for resolving these ambiguities and validating the protocol design is to produce multiple independent implementations, and to test these implementations for interoperability which often renders the RFCs obsolete and the implementations the de-facto standard. The goal of the project is to inject formal methods into the standardization process. The approach is based on a new methodology for specification-based testing that allows to rigorously specify and test complex Internet protocols. A primary objective is to demonstrate the practical value of formal specifications in the standardization process, and in particular to show that formal specifications have practical value beyond simply expressing the standard in a more rigorous way. This has a good chance to gain entry for formal methods in the Internet protocol development process, and thus ultimately to improve the reliability, security and maintainability of Internet services. The project will develop new methodologies, accompanied by tools, to apply lightweight formal methods in the Internet protocol domain. The work will also develop a formal specification of an emerging Internet standard of significant importance --- the QUIC secure transport protocol.
With the growing world population and diminishing agricultural lands, it becomes imperative to maximize crop yield by protecting crop health and mitigating against pests and diseases. Though there are decades-old practices still in place, there is also growing adoption of so-called precision agriculture solutions, which employ emerging technologies in sensing, automation, and analytics in daily farmland operations. As farmers gain real-time access to critical data (e.g., land and weather conditions) and can quickly share any untoward findings with others, farmland operations are morphing into full-fledged cyber-physical systems. To this end, this project seeks to develop, implement and evaluate a multi-robot agricultural information collection system that is autonomous, efficient and secure.
This project led by the University of North Florida (UNF) and supported by the University of Central Florida (UCF) has two main goals: (i) develop and implement novel information collection techniques for autonomous mobile robots that collect, store and share data in an efficient yet secure manner using blockchain,and (ii)and to train undergraduate and graduate students to conduct basic and applied research while closely working with local farmland partners in north-east Florida. Current technologies already use robots for agricultural purposes, but they typically have a high maintenance cost and do not necessarily consider issues related to security and data integrity. The primary objective is to design and deploy a set of autonomous robots that communicate wirelessly and navigate through planned paths in order to collect valuable data. This project will also consider the threat of security attacks by which collected data can be corrupted; seeking new distributed blockchain-based consensus protocols that mitigate the adversarial influence of such attacks. This project also contains a significant research and education component leveraging the leadership of UNF in the context of a primarily undergraduate institution (RUI). Being predominantly an undergraduate institution, there is a lack of opportunity for pursuing higher degrees in the Jacksonville area. This project aligns with an established Memorandum of Understanding (MoU) between UNF and UCF to provide a conduit for computing/engineering students to pursue M.S. degrees at UNF that feed seamlessly into Ph.D. programs at UCF. Students will benefit from the new robotics course to be developed at UNF and the ones being offered at UCF. Research progress will be showcased via technical workshops at both institutions to be held annually. Developed solutions are expected to transfer to other cyber-physical system applications, including search and rescue, patrolling, advanced manufacturing, among others. Most broadly, this project will raise awareness among today's teenagers and young adults of the impending agricultural crisis if worldwide food production falls even further behind meeting demands of an increasing global population.
Increasing wildfire costs---a reflection of climate variability and development within wildlands---drive calls for new national capabilities to manage wildfires. The great potential of unmanned aerial systems (UAS) has not yet been fully utilized in this domain due to the lack of holistic, resilient, flexible, and cost-effective monitoring protocols. This project will develop UAS-based fire management strategies to use autonomous unmanned aerial vehicles (UAVs) in an optimal, efficient, and safe way to assist the first responders during the fire detection, management, and evacuation stages. The project is a collaborative effort between Northern Arizona University (NAU), Georgia Institute of Technology (GaTech), Desert Research Institute (DRI), and the National Center for Atmospheric Research (NCAR). The team has established ongoing collaborations with the U.S. Forest Service (USFS) in Pacific Northwest Research Station, Kaibab National Forest (NF), and Arizona Department of Forestry and Fire Management to perform multiple field tests during the prescribed and managed fires. This proposal's objective is to develop an integrated framework satisfying unmet wildland fire management needs, with key advances in scientific and engineering methods by using a network of low-cost and small autonomous UAVs along with ground vehicles during different stages of fire management operations including: (i) early detection in remote and forest areas using autonomous UAVs; (ii) fast active geo-mapping of the fire heat map on flying drones; (iii) real-time video streaming of the fire spread; and (iv) finding optimal evacuation paths using autonomous UAVs to guide the ground vehicles and firefighters for fast and safe evacuation.
This project will advance the frontier of disaster management by developing: (i) an innovative drone-based forest fire detection and monitoring technology for rapid intervention in hard-to-access areas with minimal human intervention to protect firefighter lives; (ii) multi-level fire modeling to offer strategic, event-scale, and new on-board, low-computation tactics using fast fire mapping from UAVs; and (iii) a bounded reasoning-based planning mechanism where the UAVs identify the fastest and safest evacuation roads for firefighters and fire-trucks in highly dynamic and uncertain dangerous zones. The developed technologies will be translational to a broad range of applications such as disaster (flooding, fire, mud slides, terrorism) management, where quick search, surveillance, and responses are required with limited human interventions. This project will also contribute to future engineering curricula and pursue a substantial integration of research and education while also engaging female and underrepresented minority students, developing hands-on research experiments for K-12 students.
This project is in response to the NSF Cyber-Physical Systems 20-563 solicitation.
Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5-year degree, equivalent to a Master of Science) in Electronic and Computer Engineering from the Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States of America, he studied at The University of Texas at Arlington with Frank L. Lewis as his advisor, and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. From May 2011 to January 2012, he was working as an Adjunct Professor and Faculty Research Associate at the University of Texas at Arlington and at the Automation and Robotics Research Institute. During the period from 2012 to 2016 he was project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018.
He currently serves as the Dutton-Ducoffe Endowed Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His expertise is in reinforcement learning, control theory, game theory, cyber-physical security, bounded rationality, and safe/assured autonomy.
Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, a 2018 DoD Minerva Research Initiative Award, a 2021 GT Chapter Sigma Xi Young Faculty Award and his work has been recognized with best paper nominations and several international awards including the 2016 International Neural Network Society Young Investigator (INNS) Award, the Best Paper Award for Autonomous/Unmanned Vehicles at the 27th Army Science Conference in 2010, the Best Presentation Award at the World Congress of Computational Intelligence in 2010, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. He is a member of Tau Beta Pi, Eta Kappa Nu, and Golden Key honor societies and is listed in Who's Who in the World, Who's Who in Science and Engineering, and Who's Who in America. He has also served on various international program committees and has organized special sessions, workshops, and tutorials for several international conferences. He currently is a member of the Technical Committee on Intelligent Control of the IEEE Control Systems Society, a member of the Technical Committee on Adaptive Dynamic Programming and Reinforcement Learning of the IEEE Computational Intelligence Society, a member of the IEEE Control Systems Society Conference Editorial Board, an Associate Editor of: Automatica; IEEE Transactions on Automatic Control; IEEE Transactions on Neural Networks and Learning Systems; IEEE Computational Intelligence Magazine; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions on Artificial Intelligence; Neurocomputing; Journal of Optimization Theory and Applications; and of Frontiers in Control Engineering-Adaptive, Robust and Fault Tolerant Control. He had also served as a Guest Editor for, IEEE Transactions on Automation Science and Engineering (Special issue on Learning from Imperfect Data for Industrial Automation); IEEE Transactions on Neural Networks and Learning Systems (Special issue on Reinforcement Learning Based Control: Data-Efficient and Resilient Methods); IEEE Transactions on Industrial Informatics (Special issue on Industrial Artificial Intelligence for Smart Manufacturing); and IEEE Transactions on Intelligent Transportation Systems (Special issue on Unmanned Aircraft System Traffic Management). He is also a registered Electrical/Computer engineer (PE), a member of the Technical Chamber of Greece, an Associate Fellow of AIAA, and a Senior Member of IEEE.