AIAA Intelligent Systems Technical Committee

The AIAA Intelligent Systems Technical Committee (ISTC) is concerned with the application of Intelligent System (IS) technologies and methods to aerospace systems, the verification and validation of these systems, and the education of the AIAA membership in the use of IS technologies in aerospace and other technical disciplines.

Welcome to the ISTC!

The Intelligent Systems Technical Committee addresses the application of Intelligent System (IS) technologies and methods to aerospace systems, the verification and validation of these systems, and the education of the AIAA membership in the use of IS technologies in aerospace and other technical disciplines.

ISTC Focus: Commercial and military aerospace systems, and those ground systems that are part of test, development, or operations of aerospace systems. Technologies which enable safe and reliable operation of complex aerospace systems or sub-systems with minimal or no human intervention (autonomy), or collaborative synthetic-human agent teams are of interest. These include, but are not limited to: autonomous and expert systems, discrete planning/scheduling algorithms, intelligent data/image processing, learning and adaptive techniques, data fusion and reasoning, and knowledge engineering.

Members of the ISTC have experience in developing and managing aerospace systems involving knowledge engineering, knowledge acquisition, verification/validation of knowledge based systems, neural networks, and expert systems, as well as the use of artificial intelligence concepts/techniques to support natural language interfaces, image understanding, planning/scheduling, and data fusion.

If you would like more information, or are interested in joining the ISTC, please see the Our Mission page!

2024 Intelligent Systems Workshop – University of Nebraska, June 25-26, 2024 (save the date!)

The 9th annual Intelligent Systems Workshop will take place on June 25-26, 2024 at the University of Nebraska-Lincoln, in Lincoln, NE. The workshop is an important part of the AIAA Intelligent Systems Technical Committee (ISTC) annual activities and provides an informal, unclassified, international forum for the exchange of ideas and information on intelligent systems.

This year’s workshop theme: Safety in Intelligent Aerospace Systems

Confirmed keynote speakers include Dr. Jim Paunicka (Technical Fellow, Autonomy Capabilities Team, Boeing CTO Office) and Dr. Steve Chien (JPL Fellow, Supervisor of the Artificial Intelligence Group).

Registration is closed – we hope you all enjoyed the workshop!

Please see the UNL website for the agenda, workshop registration, and the most up-to-date information! https://nimbus.unl.edu/aiaa-istc-workshop

Announcements:

News: 2024 Oct 21, 3:22pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Dimitra Panagou’s seminar Tuesday October 29 at 12:00pm ET on Zoom!

Speaker: Dimitra Panagou, Ph.D.
Associate Professor
Department of Robotics
Department of Aerospace Engineering
University of Michigan

Date/time: Tuesday, October 29th, 2024 – 12:00pm-1:00pm Eastern time

Title: Towards safe and resilient autonomy using synergistic control, observation and learning

Meeting link: https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09&omn=89153417424
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Abstract: Enabling autonomy for robotic and cyber-physical systems with provable safety and resilience guarantees has been an ongoing area of research. Despite significant progress over the years, there are still open challenges due to constraints (e.g., safety and time specifications; sensing, computation and communication limitations), and environmental uncertainty. This talk will present some of our recent results and ongoing work on a framework that interconnects control, planning and learning methods towards provably-correct safety-critical systems under constraints and uncertainty.

Bio: Dimitra Panagou received the Diploma and PhD degrees in Mechanical Engineering from the National Technical University of Athens, Greece, in 2006 and 2012, respectively. In September 2014 she joined the Department of Aerospace Engineering, University of Michigan as an Assistant Professor. Since July 2022 she is an Associate Professor with the newly established Department of Robotics, with a courtesy appointment with the Department of Aerospace Engineering, University of Michigan. Prior to joining the University of Michigan, she was a postdoctoral research associate with the Coordinated Science Laboratory, University of Illinois, Urbana-Champaign (2012-2014), a visiting research scholar with the GRASP Lab, University of Pennsylvania (June 2013, Fall 2010) and a visiting research scholar with the University of Delaware, Mechanical Engineering Department (Spring 2009). Her research program spans the areas of nonlinear systems and control; multi-agent systems, autonomy and control; and aerospace robotics. She is particularly interested in the development of provably-correct methods for the safe and secure (resilient) operation of autonomous systems in complex missions, with applications in robot/sensor networks and multi-vehicle systems (ground, marine, aerial, space) under uncertainty. She is a recipient of the NASA Early Career Faculty Award, the AFOSR Young Investigator Award, the NSF CAREER Award, the George J. Huebner, Jr. Research Excellence Award, and a Senior Member of the IEEE and the AIAA.

News: 2024 July 21, 8:18pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Melkior Ornik’s seminar Tuesday July 23 at 2:00pm ET on Zoom!

Speaker: Melkior Ornik, Ph.D.
Department of Aerospace Engineering
University of Illinois Urbana-Champaign

Date/time: Tuesday, July 23rd, 2024 – 2:00pm-3:00pm Eastern time

Title: Control of Unknown Systems in Unlearnable Environments: Fundamental Limits of Knowledge

Meeting link: https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09&omn=89524782672
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Abstract: High-level autonomy in previously unseen or abruptly changed conditions faces a critical conceptual challenge: if the controller has no comprehensive system model and no prior opportunity to collect data and train its strategy, how can we form guarantees about system safety or performance? In fact, how do we know whether the system’s task is even feasible? Indeed, standard approaches of learning-based control require a wealth of available data, while classical methods of robust and adaptive control respond to highly structured uncertainties. Additionally, attempting to determine a control strategy to complete a predetermined task does not reflect the limits of the system’s capabilities: the system might be unable to perform the old task in novel conditions. Instead, an intelligent planner should understand which tasks can certifiably be completed given the current knowledge, and then formulate appropriate control laws. To move towards that goal, in this talk I will present an emergent twin efforts of design-time guaranteed resilience and mission-time guaranteed performance. Combining methods of optimal control, reachability analysis, and differential geometry, these approaches compute a set of tasks completable under all system dynamics consistent with the planner’s partial knowledge, and synthesize appropriate control laws using online learning and adaptation. In describing this framework, this talk will briefly present several applications to aerial and space vehicles, identifying promising future directions of research such as safety-assured reachability, verifiable performance with faulty sensing, and data-driven incremental certification.

Bio: Melkior Ornik is an assistant professor in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign, also affiliated with the Coordinated Science Laboratory, as well as the Discovery Partners Institute in Chicago. He received his Ph.D. degree from the University of Toronto in 2017. His research focuses on developing theory and algorithms for control, learning and task planning in autonomous systems that operate in uncertain, changing, or adversarial environments, as well as in scenarios where only limited knowledge of the system is available. He is a senior member of AIAA and IEEE, his recent work has been extensively funded by NASA grants and Department of Defense programs, and he has been awarded the 2023 Air Force Young Investigator Program grant.

News: 2024 May 8, 10:00am EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Jun Chen’s seminar Tuesday May 14 at 4:00pm ET on Zoom!

Speaker: Jun Chen, Ph.D.
Department of Aerospace Engineering
San Diego State University

Date/time: Tuesday, May 14th, 2024 – 4:00pm-5:00pm Eastern time

Title: Safety-Assured Online Planning for Large-scale Autonomy under Uncertainty

Meeting link: https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09&omn=82685514209
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Abstract: With the booming of artificial intelligence and autonomy in a new era, the field of systems and control has recently been facing newly emerged research in the control and optimization of large-scale networked autonomous systems, most of which heavily rely on the fidelity of the models and efficient computational techniques to execute optimized control actions. Meanwhile, the physical autonomous systems are inherently subject to uncertainties and disturbances. This seminar will present a suite of modeling, optimization, and computation algorithms and tools that can efficiently support safety-assured real-time decision-making for large-scale autonomous systems, with a focus on Unmanned Aerial Vehicle (UAV) autonomy and Advanced Air Mobility (AAM) applications under dynamic and uncertain environments.

Bio: Dr. Jun Chen is an Assistant Professor in the Department of Aerospace Engineering at San Diego State University. Dr. Chen’s research area includes dynamics, control, machine learning, and artificial intelligence, particularly in data-driven modeling, control, and optimization for large-scale networked dynamical systems, with applications in mechanical and aerospace engineering such as air traffic control, traffic flow management, and autonomous air/ground vehicle systems. His research spans theory and practice, including both algorithm development and real-world field tests. Dr. Chen’s research has been supported by NSF, FAA, and NASA. Dr. Chen earned his Ph.D. and M.S. degrees in Aerospace Engineering from Purdue University and a B.S. degree in Aeronautics Engineering from Beihang University. He is a recipient of the Purdue College of Engineering Outstanding Research Award in 2018.

News: 2024 Apr 9, 7:40pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Naira Hovakimyan’s seminar Tuesday April 23 at 5:00pm ET on Zoom!

Speaker: Naira Hovakimyan, Ph.D.
W. Grafton and Lillian B. Wilkins Professor
Mechanical Science and Engineering
University of Illinois Urbana-Champaign

Date/time: Tuesday, Apr 23th, 2024 – 5:00pm-6:00pm Eastern time

Title: Safe Learning in Autonomous Systems

Meeting link: https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09&omn=85452117558
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Abstract: Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external disturbances is going to be vital to ensure mission success. We introduce a class of distributionally robust adaptive control architectures that ensure robustness to distribution shifts and enable the 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.

Bio: 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. She is Fellow of AIAA, IEEE, ASME, and senior member of National Academy of Inventors. She is cofounder and chief scientist of Intelinair. Her work was featured in the New York Times, on Fox TV and CNBC.

News: 2024 Jan 29, 7:19pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Yan Wan’s seminar tomorrow (this Tuesday) at 3:00pm EDT on Zoom!

Speaker: Yan Wan, Ph.D.
Distinguished University Professor
Electrical Engineering
University of Texas at Arlington

Date/time: Tuesday, Jan 30th, 2024 – 3:00pm-4:00pm Eastern time

Title: Uncertainty Modeling and Evaluation for Cyber-Physical Control Systems

Meeting link: https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09
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Abstract: Uncertainties often modulate modern cyber-physical system dynamics in a complicated fashion. They lead to challenges for real-time control, considering the significant computation load needed for uncertainty evaluation. We introduce scalable uncertainty models and uncertainty evaluation methods that break the curse of dimensionality for optimal control, learning control and differential games. Applications include UAV networking, UAV traffic management, air traffic management, and autonomous driving.

Bio: Yan Wan is a Distinguished University Professor in the Electrical Engineering Department at the University of Texas at Arlington (UTA). She received her Ph.D. degree in Electrical and Computer Engineering from Washington State University and then did Postdoctoral training in the Institute for Collaborative Biotechnologies at the University of California Santa Barbara. Her research interests lie in the modelling, evaluation, and control of large-scale dynamical networks, cyber-physical systems, stochastic networks, decentralized control, learning control, networking, uncertainty analysis, algebraic graph theory, and their applications to urban aerial mobility, autonomous driving, robot networking, air traffic management, microgrids, and edge computing. She received research grants from federal agencies such as NSF, ONR, ARO, NIST, and DOE as well as industry support from Ford Motors, Toyota Motors, Lockheed Martin, Dell Technologies, and MITRE Corporation as subcontracts from the FAA. Her research has led to over 230 publications and technology transfer outcomes. For her work, she has been recognized with several prestigious awards, including the NSF CAREER Award, RTCA William E. Jackson Award, U.S. Ignite and GENI demonstration awards, IEEE WCNC and ICCA Best Paper Awards, UTA Outstanding Research Achievement or Creative Accomplishment Award, UNT Early Career Award for Research and Creativity, UTA STARS Award, Lockheed Martin Aeronautics Excellence in Teaching Award, and Tech Titan of the Future – University Level Award. She was a Board of Governors (BOG) member of the IEEE Control Systems Society and is currently a BOG member of the IEEE Systems, Man, and Cybernetics Society. She also serves in the Technical Committees of AIAA Intelligent Systems, IEEE CSS Nonlinear Systems and Control, and IEEE CSS Networks and Communication Systems. She is an Associate Fellow of AIAA and a Senior Member of IEEE.

News: 2023 Nov 20, 1:06pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. John Valasek’s seminar today at 4:00pm EDT on Zoom!

Speaker: Dr. John Valasek
Texas A&M University

Date/time: Monday, Nov 20th, 2023 – 4:00pm-4:50pm Eastern time

Title: Preparing for AIAA Fellow Nomination and Selection

Abstract: In this talk I will discuss how you can assess your preparation for nomination to AIAA Fellow. The talk will focus on evaluating your career readiness, what is required for a nomination packet, who to ask for what, and how to helpfully provide information to your nominator and references. I will also present the timeline of the nomination and selection process, and what the ISTC can do to help you.

Bio: John Valasek is Director, Vehicle Systems & Control Laboratory (https://vscl.tamu.edu), Thaman Professor of Undergraduate Teaching Excellence, Professor of Aerospace Engineering, and member of the Honors Faculty at Texas A&M University (TAMU), which he joined in 1997. He has been actively conducting autonomy and flight controls research of Unmanned Air Systems (UAS) in both Industry and Academia for 36 years. He began his career as a Flight Control Engineer for the Northrop Corporation, Aircraft Division where he worked in the Flight Controls Research Group, and on the AGM-137 Tri-Services Standoff Attack Missile (TSSAM) program. He is co-inventor on a patent for Autonomous Air Refueling (AAR) of UAS and a patent for the design of a UAS. John is the TAMU Site Director for the NSF Center for Autonomous Air Mobility and Sensing (CAAMS), and the TAMU Site Director for the FAA Partnership to Enhance General Aviation Safety, Accessibility and Sustainability (PEGASAS). He has conducted more than 600 fixed-wing and rotorcraft UAS test flights on 33 funded research programs over a 23 year period at TAMU.

John is co-author of the book Nonlinear Multiple Time Scale Systems in Standard and Non-Standard Forms: Analysis and Control, (SIAM, 2014), and editor of the books Morphing Aerospace Vehicles and Structures (Wiley, 2012), Advances in Intelligent and Autonomous Aerospace Systems (AIAA, 2012), and Advances in Computational Intelligence and Autonomy for Aerospace Systems (AIAA, 2018).

John is a Fellow of AIAA, member of the AIAA Autonomy Task Force, Chair of the AIAA Intelligent Systems Technical Committee, and Associate Editor of the Journal of Guidance, Control, and Dynamics.

News: 2023 Oct 23, 2:00pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Justin Bradley’s seminar tomorrow (this Tuesday) at 3:00pm EDT on Zoom!

Speaker: Dr. Justin Bradley
Richard L. and Carol S. McNeel Associate Professor
School of Computing
University of Nebraska-Lincoln

Date/time: Tuesday, Oct 24th, 2023 – 3:00pm-4:00pm Eastern time

Title: Preparing for AIAA Associate Fellow Nomination and Selection

Abstract: In this talk I will discuss how you can assess your preparation for nomination to AIAA Associate Fellow. The talk will focus on evaluating your career readiness, what is required for a nomination packet, who to ask for what, and how to helpfully provide information to your nominator and references. I will also present the timeline of the nomination and selection process, and what the ISTC can do to help you.

Bio: Justin Bradley is a Richard L. and Carol S. McNeel Associate Professor in the School of Computing at the University of Nebraska-Lincoln. He holds a B.S. in computer engineering (2005) and M.S. in electrical engineering (2007) from Brigham Young University, and M.S. (2012) and Ph.D. (2014) degrees in aerospace engineering from the University of Michigan. He has worked with Unmanned Aircraft Systems (UAS) for over 17 years, starting at the Multi-AGent Intelligent Coordination and Control (MAGICC) lab at BYU, the A2Sys lab at the University of Michigan, and most recently as a co-director of the Nebraska Intelligent MoBile Unmanned System (NIMBUS) lab since 2015. He is a recipient of a 2021 NSF CAREER award, an AIAA Associate Fellow, and chair-elect of the AIAA ISTC. Justin’s research lies at the intersection of computing, control, and aerospace disciplines.

News: 2023 Sept 19, 10:57pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Kerianne Hobbs’s seminar tomorrow (this Wednesday) at 3:00pm EDT on Zoom!

Speaker: Kerianne Hobbs, Ph.D.
Safe Autonomy and Space Lead
Autonomy Capability Team (ACT3)
Air Force Research Laboratory

Date/time: Wednesday, Sept 20th, 2023 – 3:00pm-4:00pm Eastern time

Title: Safe Autonomy

Abstract: How do you know an autonomous system will do what you want? How do you know an autonomous system won’t do what you don’t want? What if the autonomous system is based on Machine Learning? This talk introduces the machine learning autonomy projects at the AFRL ACT3 Research Team, and the research by the AFRL Safe Autonomy Research Team over the last four years to analyze and assure safety of learning-based autonomous systems.

Bio: Dr. Kerianne Hobbs is the Safe Autonomy and Space Lead on the Autonomy Capability Team (ACT3) at the Air Force Research Laboratory. There she investigates rigorous specification, analysis, bounding, and intervention techniques to enable safe, trusted, ethical, and certifiable autonomous and learning controllers for aircraft and spacecraft applications. Her previous experience includes work in automatic collision avoidance and autonomy verification and validation research. Dr. Hobbs was selected for the 2020 AFCEA 40 Under 40 award and was a member of the team that won the 2018 Collier Trophy (Automatic Ground Collision Avoidance System Team), as well as numerous AFRL Awards. She serves on the AIAA Intelligent Systems Technical Committee, the NASA Formal Methods Program Committee, the IEEE Aerospace Conference Committee, and the IEEE Space Mission Challenges for Information Technology - IEEE Space Computing Conference Program Committee. Dr. Hobbs has a BS in Aerospace Engineering from Embry-Riddle Aeronautical University, an MS in Astronautical Engineering from the Air Force Institute of Technology, and a Ph.D. in Aerospace Engineering from the Georgia Institute of Technology.

News: 2023 June 12, 2:30pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Prof. Srikanth Saripalli’s seminar this Wednesday at 5:00pm EDT on Zoom!

Speaker: Professor Srikanth Saripalli
Mechanical Engineering department
Texas A&M University

Date/time: Wednesday, June 14th, 2023 – 5:00pm-6:00pm Eastern time

Title: High Speed Off-Road Autonomy: Perception and Control in the Wild

Abstract: The talk focuses on perception and planning algorithms for autonomous vehicles in off-road situations. A particular emphasis is on why off-road vehicles are different than on-road vehicles and how can we solve autonomy in the off-road domain. A major portion of the talk will be on applications of the above algorithms to real vehicles and the lessons that we have learned i.e. what worked and what didn’t and how we should go about building such systems. I will also briefly touch on our work on Autonomous Landing and Obstacle Avoidance for UAVs.

Bio: Srikanth Saripalli is a Professor in Mechanical Engineering department and the Director for Center for Autonomous Vehicles and Sensor Systems (CANVASS) at Texas A&M University. He holds the J. Mike Walker ’66 Professorship. His research focuses on robotic systems: particularly in air and ground vehicles and necessary foundations in perception, planning, control and system integration for this domain. He is currently leading several efforts in off-road autonomous ground vehicles. He has also led several long-term (> 6 month) on-road deployments of autonomous 18 wheeler trucks and slow-moving shuttles in Texas. He is currently interested in developing and deploying Autonomous Shuttles on campus and in cities. He is also interested in developing such autonomous shuttles for mobility challenged and para transit applications.

Flyer: https://drive.google.com/file/d/1xVeI9XYPGJJbaFWfz6zfmXD9gm3c6mpj/view?usp=sharing

News: 2023 May 24, 1:45pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Woong-Je Sung’s seminar today(/Wednesday) at 3:00pm EDT on Zoom!

Speaker: Woong-Je Sung, Ph.D.
Research Engineer, School of Aerospace Engineering
Georgia Institute of Technology

Date/time: Wednesday, May 24th, 2023 – 3:00pm-4:00pm Eastern time

Title: Deep Learning Strategy for Aerodynamics

Meeting link (UPDATED): https://aiaa.zoom.us/j/2481320441?pwd=djhZZVJITi9ja0UzRGZJM0tRN3ptZz09
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Abstract: The recent progress in deep learning and generative AI provides active challenges as well as remarkable opportunities in aerodynamics research where the highly non-linear flow phenomena (e.g., shock waves and flow separations) are not uncommon and the available data are not always abundant considering the high dimensionality of flow boundary conditions. In this talk, first, the applications of deep learning techniques are briefly reviewed in the context of surrogate modeling and dimensionality reduction and, second, several research snapshots are discussed including a CFD (Computational Fluid Dynamics)-WTT (Wind Tunnel Test) data fusion using deep representation learning, a geometric deep learning for 3-D mesh, and an aerodynamic shape optimization using deep reinforcement learning.

Bio: Dr. Woong-Je Sung Studied CFD, FEM, and MDO in Seoul National Univ (BS/MS in Aerospace Engineering). He worked on experimental and computational aerodynamics in Agency for Defense Development (1999-2004, Korea). He studied meta-modeling with neural network in Georgia Tech (PhD in Aerospace Engineering, 2012). Dr. Sung has worked on various projects on M&S and ML/AI as Post-Doc and Research Engineer in Georgia Tech (2012-Present).

News: 2023 March 28, 12:11pm EDT (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Junfei Xie’s seminar this coming Wednesday at 5:00pm EDT on Zoom!

Speaker: Junfei Xie, Ph.D.
Associate Professor, Department of Electrical and Computer Engineering
San Diego State University

Date/time: Wednesday, March 29th, 2023 – 5:00pm-6:00pm Eastern time

Title: Networked Airborne Computing: Empowering Reliable and Efficient Computing in the Skies

Meeting link: https://aiaa.zoom.us/j/85610194482?pwd=M1dwZGlCMWFIN0ROZXhWaVRCVDRBdz09
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Meeting ID: 856 1019 4482
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Abstract: Unmanned aerial vehicles (UAVs) have emerged as a crucial technology in various civilian and commercial applications. While most UAV applications involve a single UAV, many new applications are expected to demand cooperative computing capabilities among multiple UAVs. This trend presents opportunities for researchers to address fundamental challenges across a range of disciplines, from aerospace to control, communication, networking, and computing. With multiple UAVs sharing computing resources, the networked multi-UAV system can also provide on-demand computing services to ground users, essentially functioning as a flying cloud. However, enabling reliable and efficient networked airborne computing requires overcoming many formidable challenges such as 3-dimensional node mobility, highly uncertain operating environment, and strict safety requirements. In this talk, this new and cross-disciplinary area will be explored, and recent research results we have developed to enable networked airborne computing will be presented.

Bio: Dr. Junfei Xie is an Associate Professor in the Department of Electrical and Computer Engineering at San Diego State University (SDSU). She received her B.S. degree in Electrical Engineering from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2012. She received her M.S. degree in Electrical Engineering in 2013 and Ph.D. degree in Computer Science and Engineering in 2016 from University of North Texas (UNT), Denton, TX. Prior to joining SDSU, she was an Assistant Professor in the Department of Computing Sciences at Texas A&M University-Corpus Christi (TAMUCC). Dr. Xie’s research interests span several areas, including large-scale dynamic system design and control, unmanned aerial systems, networked airborne computing, mobile edge computing, air traffic flow management, uncertainty quantification, spatiotemporal data analysis, and complex information systems. She is the recipient of multiple prestigious awards such as the NSF CAREER Award, SDSU Presidential Research Faculty Fellow Award, Top 50 Women of Influence in Engineering by San Diego Business Journal, etc. She currently serves as the Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics – Systems, Associate Editor for IEEE Transactions on Circuits and Systems II: Express Briefs, and Guest Editor for Unmanned Systems.

Flyer: https://drive.google.com/file/d/1rXQI5mf5B_iEYujxSooeAn-t7h6f6-tD/view?usp=share_link

News: 2023 February 11, 2:54pm EST (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Stanley Bak’s seminar this coming Wednesday at 3:00pm EST on Zoom!

Speaker: Stanley Bak, Ph.D.
Assistant Professor, Department of Computer Science
Stony Brook University

Date/time: Wednesday, February 15th, 2023 – 3:00pm-4:00pm Eastern time

Title: Safe Autonomy through Surrogate Verification

Meeting link: https://aiaa.zoom.us/j/86978612062?pwd=ZFJXTlVJaTE0K1Q0VWNzMVVjTWF6dz09
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Abstract: Using autonomy within safety-critical applications demands strong assurances the system will not misbehave. Rather than direct analysis and verification, which can be hard, we instead explore using surrogate modeling. With surrogate modeling, we can create models that closely approximate system behaviors while being more amenable to formal verification methods that can prove system safety. We explore this strategy in two contexts: one for closed-loop neural network control system verification and one for approximating nonlinear dynamical systems using Koopman Operator approximations.

Bio: Stanley Bak received his PhD from the the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) in 2013 and then worked for several years at the Air Force Research Laboratory (AFRL) in the Verification and Validation (V&V) group of the Aerospace Systems Directorate. In 2020, he received the AFOSR Young Investigator Research Program (YIP) award. He is currently an assistant professor in the Department of Computer Science at Stony Brook University.

News: 2022 December 13, 9:30pm EST (by Cat McGhan)

ISTC Technical Seminar Series

Don’t miss Dr. Chetan S. Kulkarni’s seminar on Wednesday at 4:00pm EST on Zoom!

Speaker: Chetan S. Kulkarni, Ph.D.
Staff Researcher, Prognostics Center of Excellence and the Diagnostics and Prognostics Group
Intelligent Systems Division, NASA Ames Research Center

Date/time: Wednesday, December 14th, 2022 – 4:00pm-5:00pm Eastern time

Title: Hybrid Approaches for Systems Health Management and Prognostics

Abstract: To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system’s components health solving a calibration problem in the deep learning approach.

Bio: Chetan S. Kulkarni is a staff researcher at the Prognostics Center of Excellence and the Diagnostics and Prognostics Group in the Intelligent Systems Division at NASA Ames Research Center. His current research interests are in Systems Diagnostics, Prognostics and Health Management. Specifically focused in the area of developing physics-based and hybrid modeling approaches for diagnosis and prognosis of complex systems. He completed his MS (09), Ph.D. (13) from Vanderbilt University, TN where he was a Graduate Research Assistant with the Institute for Software Integrated Systems and Department of Electrical Engineering and Computer Science. He completed his BE (`02) from the University of Pune, India. Prior to joining Vanderbilt, he was a Research Fellow at the Department of Electrical Engineering, IIT-Bombay, where his research work focused on developing low-cost substation automation system monitoring and control devices and partial discharge of high voltage transformers. Earlier to that he was a member of the technical team of the Power Automation group at Honeywell, India where he was involved in turnkey power automation projects and product development in the area of substation automation. He is KBR Technical Fellow, AIAA Associate Fellow and Associate Editor for IEEE, SAE, IJPHM Journals on topics related to Prognostics and Systems Health Management. He has been Technical Program Committee co-chair at PHME18, PHM20 and PHM21. He co-chairs the Professional Development and Education Outreach subcommittee in the AIAA Intelligent Systems Technical Committee.

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