Speakers / Panelists

Prof. Andrea BELZ

Abstract: Intuition can be defined as the ability to acquire knowledge without reasoning. In recent years, the field of behavioral finance has grown dramatically as scholarly attention has focused on human decision-making that bypasses typical reasoning pathways. The explosive growth of artificial intelligence has led to novel decision systems that mimic and accelerate reasoning, but intuition is harder to emulate. In this talk we consider the challenging problem of managing a research portfolio as a laboratory to explore the opportunities and limitations of both artificial intelligence and artificial intuition.

Biography: Andrea Belz is the inaugural Vice Dean of Transformative Initiatives at USC Viterbi, Research Director at the Information Sciences Institute, Director of the Center for Research in Space Technologies (CREST), and Professor of Practice in Industrial and Systems Engineering, where she specializes in engineering policy and technology strategy. She is on the leadership team of the California Defense Ready Electronics and Microdevices Superhubs (California DREAMS), funded by the Defense Department as a Microelectronics Commons Hub, where she serves as Director of Translational Strategy. She currently serves as President of the IEEE Technology and Engineering Management (TEMS) Society. From 2019 to 2022, Andrea served as Division Director at the National Science Foundation, where she oversaw the agency's principal applied research activities and a $300+ M annual budget, including roughly 300 startups annually. She has held prior faculty appointments at USC Marshall, USC Iovine/Young Academy (where she was on the founding faculty), and Caltech, where she served as Visiting Professor of Engineering. Previously she spent ten years serving as a consulting systems engineer at NASA JPL and has consulted to venture capital firms, startups, and well-known organizations such as Avery Dennison, BP, Caltech, HRL (formerly Hughes Research Laboratory), the National Academies, Occidental Petroleum, and UCLA. A veteran of the Los Angeles startup scene, she was the first woman to represent the Pasadena Angels on a portfolio company board, serving as a Director of Caltech laser spinoff Ondax from 2010 until its acquisition. She holds a PhD in physics from Caltech and a BS in physics from the University of Maryland at College Park.

Prof. Jin Song DONG

Abstract: Sports analytics encompasses the utilization of data science, artificial intelligence (AI), psychology, and Internet of Things (IoT) devices to enhance sports performance, strategy, and decision-making. This process involves the collection, processing, and interpretation of cloud-based data from a variety of sources, such as video recordings and performance metrics. The resulting insights aid in evaluating player and team performance, preventing injuries, and supporting coaches and team managers in making well-informed decisions to optimize resources and achieve superior outcomes. One widely recognized formal method, Probabilistic Model Checking (PMC), has traditionally been employed in reliability analysis for complex safety-critical systems. For instance, the reliability of an aircraft can be determined by evaluating the reliability of its components, including the engine, wings, and sensors. We apply PMC to a new application domain: Sports Analytics. As an example, the reliability (winning percentage) of a tennis player can be ascertained from the reliability (success rate) of their specific sub-skill sets (e.g., serve, forehand, backhand, etc). In this presentation, we will discuss our recent research work, which involves the application of PMC, machine learning, LLM, and computer vision to sports analytics. The reasoning capabilities of LLM will also be discussed. At the end of the presentation, we will present the vision of a new international sports analytics conference series.

Biography: Jin Song Dong is a professor and deputy head of the Computer Science Department at the National University of Singapore. His research interests include formal methods, safety and security systems, model checking, sports analytics, and trusted machine learning/LLM reasoning. He co-founded the commercialized PAT verification system which has garnered thousands of registered users from over 150 countries. Jin Song also co-founded the commercialized trusted machine learning system Silas (www.depintel.com). He has received numerous best paper awards, including the ACM SIGSOFT ICSE Distinguished Paper Award. He served on the editorial board of ACM Transactions on Software Engineering and Methodology and ACM Formal Aspects of Computing. He has successfully supervised 29 PhD students, many of whom have become tenured faculty members at leading universities worldwide. He is also a Fellow of the Institute of Engineers Australia. In his leisure time, Jin Song developed Markov Decision Process (MDP) models for tennis strategy analysis using PAT, assisting professional players with pre-match analysis (beating the world's best). He is a Junior Grand Slam coach and coached tennis to his three children, all of whom have reached the #1 national junior ranking in Singapore/Australia. Two of his children have earned NCAA Division 1 full scholarship, while his second son, Chen Dong, played #1 singles for Australia in the Junior Davis Cup World Final and participated in both the Australian Open and US Open Junior Grand Slams.

Prof. Naira HOVAKIMYAN

Fellow of IEEE

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.

Biography: Naira Hovakimyan received her MS degree in Theoretical Mechanics and Applied Mathematics in 1988 from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics in 1992 from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow, majoring in optimal control and differential games. Before joining the faculty of UIUC in 2008, she spent time as a research scientist at Stuttgart University in Germany, French Institute for Research in Computer Science and Automation (INRIA) in France, Georgia Institute of Technology and she was on faculty of Aerospace and Ocean Engineering of Virginia Tech during 2003-2008. She is currently a W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC, funded through NASA University Leadership Initiative (ULI) award. She has co-authored two books, eleven patents, and more than 500 refereed publications. She was the recipient of the SICE International scholarship for the best paper of a young investigator in the VII ISDG Symposium (Japan, 1996), 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 the 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. She is Fellow of AIAA, IEEE, ASME, and senior member of National Academy of Inventors. 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.

Prof. Yanfu LI

Prof. Lei MA

Abstract: In recent years, data-driven AI software systems have made remarkable progress, powering a surge in advanced intelligent applications. This growth and its real-world impact have been amplified by the advent of large foundation models (e.g., LLM, Stable Diffusion). Yet, the rapid evolution of these AI systems often proceeds without comprehensive quality assurance and engineering support. This gap is evident in the integration of standards for quality, reliability, and safety assurance, as well as the need for mature toolchain support that provides systematic and explainable feedback of the development lifecycle. In this talk, I will present a high-level overview of our team's ongoing initiatives to lay the groundwork for Trustworthy Data-Driven Intelligent System Engineering and its industrial applications, e.g., including (1) AI software testing and analysis at the unit level, (2) our latest trustworthiness assurance efforts for AI-driven Cyber-physical systems with an emphasis on sim2real transition, and (3) risk and safety assessment for large foundational models, including those akin to vision transformers, and large language models.

Biography: Lei Ma is currently an Associate Professor with The University of Tokyo; as well as an associate professor with University of Alberta. He is honorably selected as a Canada CIFAR AI Chair and Fellow at Alberta Machine Intelligence Institute (Amii). His recent research centers around the interdisciplinary fields of software engineering (SE) and trustworthy artificial intelligence (AI) with a special focus on the quality, reliability, safety and security assurance of machine learning and AI Systems. His recent research interests follow a similar direction and focus more on Trustworthy and AI Safety assurance of the large language (foundational) model-enabled systems. Many of his works in this direction were published on top-tier software engineering and AI venues (e.g., TSE, TOSEM, ICSE, FSE, ASE, CAV, ICML, NeurIPS, ICLR, ACM MM, AAAI, IJCAI, ICRA). Over 10 prestigious awards from top-tier international conferences and journals further highlight his academic excellence, including four ACM SIGSOFT Distinguished Paper Awards (at ASE 15, ASE 18, ASE 19, and ECSE/FSE 23) and IEEE TSE 2022 annual best paper award. Please refer more details to https://www.malei.org

Prof. Ajith PARLIKAD

Prof. Hoang PHAM

Fellow of IEEE

Dr. Feng-Bin Frank SUN

Abstract: Product reliability is a function of effective usage and applied stress (both operational and environmental) conditions. The so-called usage here can manifest in different formats depending on the underlying failure governing processes, such as total miles driven for vehicle tires, cumulative charge-and-discharge cycles for batteries, total number of on/off cycles for power switches, total number of reads and writes for hard disk drives, etc. The applied stress conditions include mechanical load, electrical load, environmental temperature and humidity, solar radiation, chemical exposure, road vibration, etc. In real life, both usage ad stress levels within a specified mission time, t, are not fixed, but random variables; i.e., they are statistically distributed. During engineering life test design, people often pick the high percentiles for population usage and stress level as the field reference for the sake of conservativeness and safety margin, such as 90th percentile, 95th percentile, or even 99th percentile. This often yields very ambitious and even unrealistic test sample size and/or test duration requirements because the majority (say 90% or higher) of users in the field are assumed to be operating under extreme usage and stress conditions. The question often comes up as to which usage and stress percentile is the appropriate choice so that the overall population reliability is assured. Is it mean, median, or some other percentile (such as 60%, 70%, etc.)?

In the meantime, it is a common phenomenon in real world that a product under a common input stressing environment, such as elevated ambient temperature, thermal cycling in a chamber, random vibration on a shake table, etc., may experience different stress response (such as junction temperature, physical displacement and acceleration, etc.) at different component locations. This can be caused by localized variation of stress-governing factors, such as power consumption, location, geometry, structure resonance, and material uniformity, etc. For life test design of such a product, people often pick the component (or location) with the highest stress as the reference point to calculate the desired sample size and test duration, and ignoring all other components. This will certainly yield a conservative test design with longer test time, and/or more sample size, due to underestimated acceleration factor. Oftentimes, it ends up with something unaffordable. The argument or question from engineering community often includes

Product reliability target is for the whole system, not just for those high-stress components.
ProductAre we over testing those components with less stress responses?
Can we leverage the stress levels of the other components to come up with something more reasonable?

Based on the numerous research papers published by the speaker in the last few years, this talk will first introduce the so-called reliability-equivalence principle for the above scenarios. Then, the corresponding derived reliability-equivalent reference usage and stress models will be summarized. Numerical examples are given to illustrate the application of the derived results in product life test design and resource saving with respect to the traditional practice, especially for high-reliability and expensive product in modern industry.

Biography: Dr. Frank (Feng-Bin) Sun is currently a Technical Lead and Principal Reliability Engineer at Tesla Inc. with over 30 years of industry and academia experience. He has published two books and more than 50 papers in various areas of reliability, maintainability and quality engineering. Dr. Sun served in the editorial board as an Associate Editor for the IEEE Transactions on Reliability from 1999 to 2003 and from 2016 to 2023, Program Chair of ISSAT International Conference on Reliability and Quality in Design since 2014, and a committee member and session moderator of numerous international conferences on reliability. He received his Ph.D. in Reliability from University of Arizona and is a senior member of ASQ and the President of Society of Reliability Engineers Silicon (SRE) Valley Chapter. Dr. Sun received the RAMS 2013 A.O. Plait Best Tutorial Award, RAMS 2020 P.K. McElroy Best Paper Award, and RAMS 2024 Doug Ogden Best Paper Award by an SRE Member.

Prof. Kishor S. TRIVEDI

Fellow of IEEE

Abstract: Stochastic models are often employed to study dependability of critical systems and assess various hardware and software fault-tolerance techniques. These models take into account the randomness in the events of interest (aleatory uncertainty) and are generally solved at fixed parameter values. However, the parameter values themselves are determined from a finite number of observations and hence have uncertainty associated with them (epistemic uncertainty). This talk discusses methods for computing the uncertainty in output metrics of dependability models, due to epistemic uncertainties in the model input parameters. Methods for epistemic uncertainty propagation through dependability models of varying complexity are presented with illustrative examples. The distribution, variance and expectation of model output, due to epistemic uncertainty in model input parameters are derived and analyzed to understand their limiting behavior.

Biography: Kishor Trivedi holds the Fitzgerald Hudson Chair in the Department of Electrical and Computer Engineering at Duke University, Durham, NC. He has a 1968 B.Tech. (EE) from IIT Mumbai and MS’72/PhD’74 (CS) from the University of Illinois at Urbana-Champaign. He has been on the Duke faculty since 1975. He is the author of a well-known text entitled, Probability and Statistics with Reliability, Queuing and Computer Science Applications, originally published by Prentice-Hall; a thoroughly revised second edition of this book has been published by John Wiley. The book is recently translated into Chinese. He has also published two other books entitled, Performance and Reliability Analysis of Computer Systems, published by Kluwer Academic Publishers and Queueing Networks and Markov Chains, John Wiley. His latest book, Reliability and Availability Engineering is published by Cambridge University Press in 2017. He is a Life Fellow of the Institute of Electrical and Electronics Engineers and a Golden Core Member of IEEE Computer Society. He has published over 600 articles and has supervised 48 Ph.D. dissertations. He is the recipient of IEEE Computer Society’s Technical Achievement Award for his research on Software Aging and Rejuvenation. His research interests are in reliability, availability, performance and survivability of computer and communication systems and in software dependability. His h-index is 113. He works closely with industry in carrying our reliability/availability analysis, providing short courses on reliability, availability, and in the development and dissemination of software packages such as HARP, SHARPE, SREPT and SPNP.

Prof. Jianping WANG

Fellow of IEEE

Prof. Yingxu WANG

Fellow of IEEE

Prof. W. Eric WONG

Abstract: Studies have suggested that combinatorial testing (CT) can be effective in detecting faults in software systems in real-world settings. However, the actual fault detection effectiveness depends on the input model used, which often relies on the users’ domain knowledge of the system under test. If the input model used for generating the CT test suite does not include the fault triggering values, the generated test cases will not be able to detect the corresponding faults regardless of the CT test generation algorithms and combinatorial strength used. To overcome this challenge, we propose a novel approach to construct input models that consider multiple testing aspects, including functional and non-functional requirements, execution paths, etc. Constraint analysis will also be used to determine possible values for each parameter in the input model. Test cases generated not only can test the software under normal conditions (what the system should do) but also verify whether the system does not end up with an undesired state when it fails (what the system should not do).

Biography: Dr. W. Eric Wong received his Ph.D. degree in computer science from Purdue University. He is a full professor, the Director of Software Engineering Program, and the founding director of the Advanced Research Center for Software Testing and Quality Assurance in Computer Science Department at the University of Texas at Dallas (UTD), which was one of the major sites of a Security and Software Engineering Research Center sponsored by the U.S. National Science Foundation under the Industry/University Cooperation Research Program. He also has an appointment as a guest researcher at the National Institute of Standards and Technology, an agency of the U.S. Department of Commerce. Prior to joining UTD, Dr. Wong was with Telcordia Technologies (formerly Bellcore) as a senior research scientist and the project manager in charge of Dependable Telecom Software Development. In 2014, he was named the IEEE Reliability Society Engineer of the Year. His research focuses on software testing, debugging, risk analysis/metrics, safety, and reliability. He received the Most Influential Paper Award from ICST (IEEE International Conference on Software Testing) and JSS (Journal of Systems and Software) in 2020. Dr. Wong was the Editor-in-Chief of IEEE Transactions on Reliability from 2016 to 2022. He has also been a Senior Associated Editor of Elsevier’s JSS since January 2016. He has published more than 180 papers and co-edited three books: 1) Mutation Testing for the New Century, 2) Adaptive Control Approach for Software Quality Improvement, and 3) Handbook of Software Fault Localization: Foundations and Advances, which is jointly published by IEEE and Wiley in May 2023.

Prof. Zijiang James YANG

Prof. Xin YAO

Fellow of IEEE

Abstract: Trustworthiness is a critical issue in artificial intelligence (AI), especially for real-world applications. It is impossible to apply AI in the real-world without its being trustworthy. However, the connotation and extension of trustworthiness are not entirely clear to the scientific community. There has not been a single definition that is accepted by all researchers. Nevertheless, the vast majority of researchers agree that AI trustworthiness should include at least accuracy, reliability, robustness, safety, security, privacy, fairness, transparency, controllability, maintenability, etc. First, this talk reviews very briefly AI ethics, which is closely related to AI trustworthiness. Second, the talk examines the fairness and explainability issues of machine learning models. It is argued that many aspects of trustworthiness, such as fairness and explainability, are inherently multi-dimensional. In other words, there are many dimensions to properties like fairness and explainability. They cannot be defined comprehensively using any single numerical measure. This talk advocates multi-objective evolutionary learning as an approach to enhance AI trustworthiness. Fairness and explainability are used as two examples to demonstrate how multi-objective evolutionary learning can be used to improve fairness and explainability of learned models. Third, the talk also illustrates how constrained learning could be used to regulate models that must meet certain criteria, e.g., safety. Finally, the talk ends with some concluding remarks.

Biography: Xin Yao is Vice-President (Research and Innovation) and Tong Tin Sun Chair Professor of Machine Learning at the Lingnan University, Hong Kong SAR. He is an IEEE Fellow and was a Distinguished Lecturer of the IEEE Computational Intelligence Society (CIS). He served as the President (2014-15) of IEEE CIS and the Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation. His major research interests include evolutionary computation, neural network ensemble learning, and real-world applications. Recently, he has been working on trustworthy AI, especially on fair machine learning and explainable AI, and AI ethics. His work won the 2001 IEEE Donald G. Fink Prize Paper Award; 2010, 2016 and 2017 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards; 2011 IEEE Transactions on Neural Networks Outstanding Paper Award; 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist); and other best paper awards at conferences. He received a 2012 Royal Society Wolfson Research Merit Award, 2013 IEEE CIS Evolutionary Computation Pioneer Award and the 2020 IEEE Frank Rosenblatt Award.

Last modified on 23 May, 2024