"Recent Advances of Industrial AI Augmented Predictive Metrology
and Large Knowledge Model for Resilient Industrial Systems"
Keynote speaker: Dr. Jay Lee
Bio:
" Dr. Jay Lee is Clark Distinguished Professor and Founding Director of Industrial AI Center in the Mechanical Engineering of the Univ. of Maryland College Park. His current research is focused on developing non-traditional machine learning technologies including transfer learning, domain adaptation, similarity-based machine learning, stream-of-x machine learning, as well as industrial large knowledge model (ILKM), etc. In addition, he is leading AI Foundry and Data Foundry which consist of over 30 different machine learning analytic tools and 100 diversified industrial datasets including semiconductor manufacturing, jet engines, wind turbine, EVs, high speed train, machine tools, robots, medical TBI, etc. for rapid development and deployment of AI. Previously, he was the founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (www.imscenter.net) in partnership with over 100 global company members and the Center was selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012. He mentored his students and developed a number of start-up companies including Predictronics through NSF iCorps in 2013. He has developed Dominant Innovation® methodology for product and service innovation design. He is a member of Global Future Council on Advanced Manufacturing and Production of the World Economics Council (WEF), a member of Board of Governors of the Manufacturing Executive Leadership Council of National Association of Manufacturers (NAM), Board of Trustees of MTConnect, as well as a senior advisor to McKinsey. He served as Vice Chairman and Board Member for Foxconn Technology Group (during 2019-2021 and had advised Foxconn business units to successfully receive six WEF Lighthouse Factory Awards. He also served as Director for Product Development and Manufacturing at United Technologies Research Center (now Raytheon Technologies Research Center) as well as Program Director for a number of programs at NSF. He was selected as 30 Visionaries in Smart Manufacturing in by SME in Jan. 2016 and 20 most influential professors in Smart Manufacturing in June 2020, and received SME Eli Whitney Productivity Award and SME/NAMRC S.M. Wu Research Implementation Award in 2022. His new book on Industrial AI was published by Springer in 2020. He is also a working group member for the recent Report on AI Engineering by NSF Engineering Research Visionary Alliance (ERVA) in 2024. He also serves as Editorin-Chef for IOP Science Journal Machine Learning: Engineering. "
Abstract:
" This presentation will introduce the trends and recent advances of Industrial AI for improved resilience of complex and highly connected industrial systems. First, trends of data-centric industrial systems and unmet needs of productivity are introduced. Next, some recent advances of industrial AI and non-traditional machine learning including topological data analytics, stream-of-quality (SoQ) based data analytics, similarity-based machine learning, domain adaptation and transfer learning, etc. for highly connected and complex industrial systems will be introduced with some examples including electronics manufacturing, semiconductor manufacturing, EVs, etc. Furthermore, the development of Industrial Large Knowledge Model for enhanced data-centric engineering education will be discussed. Finally, we will address the training industrial AI skills through data foundry for future workforce and talents. "
"Signal Processing Informed Neural Network for Intelligent Fault Diagnosis"
Keynote speaker: Dr. Ruqiang Yan
Bio:
" Ruqiang Yan is a Full Professor and Director of International Machinery Center at the School of Mechanical Engineering, Xi’an Jiaotong University, China. His research interests include data analytics, AI, and energy-efficient sensing and sensor networks for the condition monitoring and health diagnosis of large-scale, complex, dynamical systems.. Dr. Yan is a Fellow of IEEE (2022) and ASME (2019). He is the recipient of several prestigious awards including the First Prize for Technological Invention in Shaanxi Province in 2020, the First Prize for Natural Science from the Ministry of Education in 2020, the 2019 IEEE Instrumentation and Measurement Society Technical Award, and the 2022 IEEE Instrumentation and Measurement Society Distinguished Service Award. He has led the development of one IEEE standard and published over one hundred papers in IEEE and ASME journals, and other publications. He was the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement, currently serves as an IEEE Instrumentation and Measurement Society Distinguished Lecturer and Associate Editor-in-Chief of Chinese Journal of Mechanical Engineering. "
Abstract:" The conventional process of fault diagnosis involves two main steps: feature extraction and decision-making. However, with the emergence of deep neural networks, a more efficient data- driven approach for feature extraction has become available. Despite their universal approximation capabilities, neural networks present challenges in terms of interpretability and achieving optimal solutions due to their extensive parameter space. To tackle these issues, this talk presents a novel type of neural network called Signal Processing Information Neural Networks (SPINN). By incorporating prior knowledge from signal processing, SPINN represents a promising approach to fault diagnosis by effectively merging the power of deep neural networks with the insights from signal processing, ultimately leading to improved performance and better interpretability. "
"Why Prognostics and Health Management Needs Digital Twins"
Keynote speaker: Mr. Vartan Piroumian
Bio:"Vartan Piroumian is a strategy and technology adviser and enterprise architect. He counsels and advises at all echelons, from the C-level to the technical, on a broad range of technology topics in computer software and systems. His formal training is in computer science and electrical engineering. Prior to his foray into enterprise architecture, Mr. Piroumian worked as a software engineer in a wide range of industries, at all levels of the software stack from OS internals to the end-user application level. Some of his software engineering highlights are the authoring of inertial and celestial navigation software for the US NASA Space Shuttle, creation of software developer tool suites for several Unix variants, and the development of libraries and services for the Java Developer’s Kit (JDK). Mr. Piroumian holds the industry-standard TOGAF-9 certification in enterprise architecture; he also holds a DO-178C certification for real-time systems architecture, design, and software construction. He is the author of two award-winning books on Java software platform technologies, and he is a contributing author to a seminal book on digital twins entitled “The Digital Twin.” He is also the author of several articles in IEEE publications, including two articles on digital twins published in IEEE Computer magazine in the past few years. Mr. Piroumian is recognized as a world authority on digital twin technology. During the past several years he has been an invited speaker and presenter at several international conferences focusing on digital twins, the Internet of Things (IoT), RFID, AI, and other related technologies. Mr. Piroumian is a member of the IEEE Computer Society, Reliability Society, Sensors Council, and Systems Council. "
Abstract:"The author defines a digital twin as “the virtual (i.e., digital) representation of a physical or perceived real-world entity, concept, or notion.” Despite the fact that the US National Institute of Standards and Technology (NIST) Information Technology Laboratory has officially adopted this definition word-forword, it remains rather unpopular in the mainstream digital twin arena. The mainstream message about the primary purpose, vision, and benefits of digital twins is starkly different than the author’s. The broader community sees digital twins as a mechanism to make engineering design work more efficient by enabling scientists and engineers to perform modeling and simulation in software, thus reducing much of the cost of building physical models, mock ups, and prototypes. This key note presentation proffers a different vision: the most important promise for digital twins is its potential to bring seamless interoperability and automation in and across domains, industries, vertical markets, and applications. Although the realization of this achievement would benefit all information systems, the benefit to the domain of prognostics and health management in particular would be significant and vitally important. Humans have become inextricably dependent on the complex systems and ‘things’ that we build—everything from commercial jet airliners to MRI and CT machines to automobiles and countless other objects and systems that we encounter and use everyday. It is crucial that we are able to comprehend these systems deeply such that we can ensure to a very high degree of probability that they are trustworthy, reliable, safe, and secure both in their design and during their operation. Digital twins done right holds great promise to significantly advance prognostics and health management in a world where our highly complex systems are a conglomeration of heterogeneous subsystems often built from incompatible proprietary platforms. The better we can comprehend, design, measure and monitor systems, the better we’ll be able to assess and verify their level of safety, the safer our systems will be, and the safer we’ll be. "
Distinguished Speakers
"Diagnostics, Prognostics, and Optimization for Lithium-ion Battery Systems" "
Distinguished speaker: Dr. Paul Gasper
Bio:
"Dr. Paul Gasper is a Staff Scientist at the National Renewable Energy Lab specializing in battery testing, data analysis, and predictive degradation modeling, with a deep interest in using computational modeling and machine-learning to complement traditional materials science and electrochemical analysis methods. Dr. Gasper’s interest in electrochemistry began during an undergraduate research internship at Worcester Polytechnic Institute on the recycling of lithium-ion batteries, and he proceeded to work on solid oxide fuel cell manufacturing at Saint-Gobain North America and solid oxide fuel cell R&D for his Ph.D. at Boston University. Dr. Gasper joined NREL as a post-doctoral researcher in the fall of 2019, developing new tools for battery data analysis for several DOE research consortia as well as battery testing and modeling for industrial partners and international organizations. Dr. Gasper’s long-term research vision is to consolidate traditional materials science methods with modern data science approaches to accelerate scientific advancement."
Abstract:
" Health management of lithium-ion battery systems presents a host of challenges due to their complex physics, large numbers of components, and a wide variety of degradation behaviors across different battery types. Dr. Paul Gasper will present on research from the Electrochemical Energy Storage Group at the National Renewable Energy Lab on Lithium-ion battery diagnostics, prognostics, and optimization. Diagnostics research, including state-estimation via machine-learning from electrochemical impedance spectroscopy and DC pulses as well as continuous state-estimation via Kalman filters, will highlight the ongoing challenges for accurately measuring the state of batteries without performing time-consuming characterization tests. NREL’s industry-recognized battery prognostics work, which predicts real-world battery degradation by identifying degradation rate models from accelerated aging data using statistical modeling and machine-learning, will be used to demonstrate the critical impact of battery controls, thermal management, and operating strategy on durability and lifetime. Finally, the use of prognostic models for financial or lifetime optimization will be discussed."
"Cybersecurity and Reliability in the Era of Agentic AI" "
Distinguished speaker: Dr. Angelos Stavrou
Bio:
" Dr. Angelos Stavrou is a Virginia Tech Innovation Campus founding Professor and the Entrepreneurship activities lead. He is also a member of the Bradley Department of Electrical & Computer Engineering at Virginia Tech. Dr. Stavrou is a serial entrepreneur and the founder of Quokka, Kryptowire Labs, Aether Argus, and Impedyme Inc. Quokka is a VC-baked Mobile Security company with a more than 200M valuation. In addition, Dr. Stavrou has served as a principal investigator on research awards from NSF, DARPA, IARPA, DHS, AFOSR, ARO, ARL, and ONR. He has written more than 150 peer-reviewed conference and journal articles. Stavrou received his M.Sc. in Electrical Engineering, M.Phil., and Ph.D. (with distinction) in Computer Science, all from Columbia University. Stavrou is an Associate Editor of IEEE Transactions on Computers, IEEE Security & Privacy, and IEEE Internet Computing magazine, and part of the governing board of the IEEE Blockchain initiative. He is a senior member of the ACM, USENIX, and IEEE. In 2013, he received the IEEE Reliability Society Engineer of the Year award. His team was awarded the DHS Cyber Security Division’s "Significant Government Impact Award" in 2017 and the “Bang for the Buck Award” in 2019."
Abstract:
" The rise of agentic AI—autonomous systems capable of setting goals, planning actions, and adapting to dynamic environments—marks a transformative shift in how artificial intelligence integrates into critical infrastructure, decision-making, and cyber operations. However, as these systems gain more autonomy and operational latitude, they introduce novel challenges to cybersecurity and reliability. Unlike traditional automated systems, agentic AI can independently interpret context, revise objectives, and execute long-term strategies across interconnected platforms. This behavioral complexity expands the attack surface, introduces uncertainty in system responses, and makes predicting or auditing decisions difficult. Ensuring cybersecurity in this new paradigm requires moving beyond static threat models toward dynamic, context-aware defenses capable of adapting in real-time—much like the agents they protect. .Reliability also becomes a dual concern: hese systems must maintain technical uptime and functional correctness and uphold alignment with human intent and ethical boundaries, even under adversarial manipulation or environmental ambiguity. Techniques such as explainable AI, robust planning under uncertainty, adversarial resilience, and secure model deployment are critical to building trustworthy agentic systems."
Tutorials
"Introduction to PHM Theory and Practice"
Tutorial speaker: Dr. Stephen Johnson
Bio:
" Dr. Stephen Johnson is the President of Dependable System Technologies, LLC, and the general editor for System Health Management: with Aerospace Applications (2011). His PHM experience includes being the control system fault protection engineer on the Magellan deep space probe to Venus in the 1980s, the head of Martin Marietta Astronautics Vehicle Health Management research in the early 1990s, a co-founder and head of engineering for a small PHM business in the 1990s, a faculty member of the Space Studies Department at the University of North Dakota from 1997 to 2005, the Analysis Lead for Mission and Fault Management on NASA Marshall Space Flight Center’s Space Launch System program and its precursors from 2005 to 2023, and numerous small PHM and systems engineering R&D contracts with government and industry from 2005 to the present. He has authored and co-authored numerous articles and books on system health management and fault management theory and practice, and other topics including systems engineering, space history and economics, and the philosophy of technology.
"
Abstract:
" This one-day short course introduces the core concepts and practices of Prognostics and Health Management / System Health Management. The class provides an introductory overview of the following: history of and motivation for PHM; core concepts and terminology; fault management functions; goals and requirements; architecture and design issues and strategies; technical performance metrics; and analysis methods. This course can be taken stand-alone or as part of the PHM Standards conference track.
"
"SPC methodologies for monitoring and optimizing HFC networks"
Tutorial speakers: Dr. Maher Harb and Nader Foroughi
Bio:
" Maher Harb is a Distinguished Engineer at Comcast working on problems at the intersection of Data Science, Machine Learning, and Telecommunications Networks. His research interests include applying Reinforcement Learning to optimize network performance, developing graph-based algorithms for network design & management, building deep neural network models for detection of RF impairments, and developing statistical methods for anomaly detection & root case identification. Prior to Comcast, Maher was an Assistant professor of Physics at Drexel University where he led an experimental Condensed Matter Physics laboratory investigating laser-matter interactions. Maher has a PhD in Physics from the University of Toronto and he held a postdoctoral fellowship at the Swedish National Synchrotron (Max-lab)."
Bio:
" Nader is a Distinguished Engineer at Comcast, where he is responsible for access network evolution, artificial intelligence and automation. Prior to joining Comcast, Nader was the Chief Technology Officer of Americas at Technetix, responsible for technology strategy and AI. His career also includes significant contributions at Shaw Communications, where he was responsible for access architecture and technology in conjunction with data sciences, making key advancements in proactive network maintenance, profile management application, and DOCSIS 4.0 development. Nader has a background in mathematics, engineering and systems architecture, with numerous white papers published spanning from DOCSIS 4.0 to applications of deep reinforcement learning in telecommunications."
Abstract:
" Statistical Process Control (SPC) and control charts, long-established quality control methodologies in manufacturing and process industries, have recently emerged as powerful tools for monitoring and optimizing Hybrid Fiber-Coaxial (HFC) networks, particularly in Full Duplex DOCSIS environments. Building upon decades of successful implementations in sectors ranging from semiconductor fabrication to pharmaceutical production, this paper presents an adaptation of SPC methodologies to cable network monitoring. Traditional network monitoring approaches relied on global thresholds for network parameters, limiting the ability to detect device-specific anomalies and performance degradation. Our implementation leverages individualized control charts for network devices, enabling dynamic threshold computation based on historical device behavior rather than system-wide metrics. By analyzing device-specific patterns and variations, the methodology demonstrates improved anomaly detection precision compared to global threshold approaches. The individualized control limits facilitate more accurate correlation between network events and performance degradation, particularly in identifying upstream channel impairments and RF interference patterns. Furthermore, the refined granularity of device-level control charts provides higher quality training data for backend machine learning algorithms, reducing false positives and enhancing predictive maintenance capabilities. This approach demonstrates significant potential for improving network reliability and maintenance efficiency in next-generation cable networks through data-driven, device-specific monitoring strategies.
"
"Hybrid Diagnostic & Prognostic Frameworks for Aerospace Health Management: Integrating Model-Based and Data-Driven Approaches"
Tutorial speakers: Dr. Afshin Rahimi
Bio:
" Dr. Afshin Rahimi is an Associate Professor in the Department of Mechanical, Automotive & Materials Engineering at the University of Windsor. He earned his PhD in Aerospace Engineering from Toronto Metropolitan University in 2017, after completing his MASc and BSc in the same field. His research centers on developing practical model-based and data-driven techniques for fault detection, diagnostics, and prognosis in complex systems, with a particular focus on aerospace applications. Dr. Rahimi has contributed to numerous peer-reviewed journals and conference papers and worked in the industry at Pratt & Whitney Canada. As a Professional Engineer (PEng) and a Senior Member of IEEE, he remains dedicated to continuous learning, sharing his insights and working collaboratively to advance the field of Prognostics and Health Management."
Abstract:
" This tutorial discusses hybrid frameworks for aerospace health management by integrating classical model-based techniques (e.g., Kalman filtering) with modern data-driven methods. Attendees will explore how these complementary approaches enable effective fault diagnosis and prognosis in complex aerospace systems, with some case studies. The session provides actionable insights to enhance system reliability and safety through robust, integrated PHM strategies.
"
"Design and reliability of LED based UVC light disinfection systems"
Tutorial speakers: Dr. Nicola Trivellin
Bio:
" Nicola Trivellin is an Associate Professor at the Department of Industrial Engineering at the University of Padua, where he was born in 1983. He obtained his bachelor's degree in Information Engineering in 2005 and his master's degree in Electronic Engineering in 2007, followed by a PhD in Information Science and Technology in 2010 at the same university. From 2011 to 2019, he served as a research fellow at the Department of Information Engineering and was the General Director of the spin-off company LightCube SRL. He has published over 60 scientific articles and holds 10 patents. Trivellin is a Guest Editor for the journals MDPI Materials and MDPI International Journal of Molecular Sciences. He teaches the courses "Microcontrollers & DSP" and "Automotive and Domotics" for the master's degree in Electronic Engineering, and "Photovoltaic Science and Technologies" for the master's degree in Energy Engineering. His research spans from the characterization of optoelectronic devices to innovative lighting for biomedical, industrial, and space applications."
Abstract:
" This tutorial provides a comprehensive review of recent advancements in the design and reliability of ultraviolet (UVC, between 100 nm and 280 nm) disinfection systems, specifically examining how design parameters and reliability factors impact overall system performance. UVC disinfection has become increasingly important due to the global COVID-19 pandemic, driving research into effective methods of pathogen inactivation, particularly against viruses such as SARS-CoV-2. This tutorial identifies and explains key factors essential to developing efficient and reliable disinfection technologies, from the optical design point of view to durability. The tutorial begins by thoroughly exploring innovative system design strategies aimed at maximizing UVC irradiation uniformity and efficacy. A particular focus is placed on a spherical irradiation system employing 275 nm UV-C LEDs. This innovative design exemplifies rapid and uniform pathogen inactivation, demonstrating the ability to achieve a 99.9% reduction of SARS-CoV-2 viral particles within just one minute at a dose of 83.1 J/m². The discussion includes an in-depth examination of optical analyses and simulation studies conducted to optimize the system’s configuration. Important considerations such as the spatial arrangement of LEDs, reflective material properties, and the geometric design of the irradiation chamber are critically assessed to illustrate their direct impact on the effectiveness and consistency of disinfection outcomes, for different target shape and surfaces area. Following the exploration of system design, the tutorial addresses critical reliability concerns associated with the use of UVC LEDs. The reliability of LEDs is identified as a fundamental constraint limiting broader commercial and clinical adoption. Stress testing and detailed reliability studies of commercially available UVC LEDs are reviewed, highlighting prevalent issues such as reductions in optical output power, efficiency degradation, and the emergence of parasitic luminescence pathways over the operational lifetime of the devices. These degradation phenomena are primarily attributed to increased non-radiative recombination through defect states within the LED’s active regions, resulting in diminished optical performance and compromised disinfection efficacy over time. Further, the tutorial synthesizes the interconnectedness between effective design principles and device reliability, emphasizing the critical importance of considering both factors concurrently during the development of UVC disinfection systems. Recommendations and insights are provided for improving the robustness and commercial viability of these systems. Special attention is directed toward understanding the underlying physical mechanisms responsible for LED degradation and integrating design strategies aimed at mitigating these reliability challenges. By comprehensively reviewing both the current state-of-the-art in system design and the inherent reliability issues affecting UVC LED technologies, the tutorial aims to provide a balanced perspective necessary for the informed development of advanced UVC-based disinfection solutions. The objective is to equip researchers, engineers, and practitioners with the knowledge required to enhance design methodologies, optimize disinfection efficacy, and overcome reliability hurdles, ultimately contributing to the wider adoption of this promising technology in various public health and clinical applications."
Panels
"PHM Standards, Past Present and Future"
Panel Moderator: Rex A. Sallade, Lockheed Martin Corp., Northrop Grumman Corp. (Retired), PHM Systems Engineer; F-35/JSF, CH-53K, etc.
Abstract:
"This presentation will cover a brief overview of some of the standards associated with PHM from the initial concepts of testability, Built In Test (BIT), diagnostics and PHM. The overview will briefly describe the original use and current relevance of standards."
Panel Speaker: Lou Gullo, US Army, Honeywell, Raytheon and Northrop Grumman (Retired), IEEE Reliability Society Standards Committee Chair, etc.
Abstract:
"This presentation will provide an introduction to IEEE 1856 (PHM), a comparison of Condition Based Maintenance Plus (CBM+) and PHM, and an explanation of PHM as a methodology for reliability predictions as discussed in the new version of P1413.1 guide."
Panel Speaker: Jeremiah Stoker, Lockheed Martin Company, PHM Engineer.
Abstract:
"This presentation explores the critical relationship between PHM system software dependability and overall system performance. We will discuss how model-driven software solutions can enhance the dependability of PHM system-level software, driving improvements in availability, reliability, maintainability, supportability, and security. Additionally, we will examine the impact of PHM system software dependability on PHM performance factors, including accuracy, confidence, and effectiveness, as defined by IEEE 1856-2017. By understanding the interplay between PHM system software dependability and system performance, we can develop more robust and reliable PHM systems that meet the demands of modern applications."
Panel Speaker: Dr. Stephen Johnson, President, Dependable System Technologies LLC, NASA (Semi-Retired).
Abstract:
"This presentation will provide a view of PHM development with significant influence from NASA and challenges associated with long-term, unmanned systems."
Panel Speaker: Matthew Hu, PhD, MBB. Director, Engineering Management Program, Cullen College of Engineering | Industrial Engineering, University of Houston.
Abstract:
"This presentation will address Robustness Thinking in Design for Reliability in Autonomous Vehicle Development."
Panel Speaker: Wyatt Pena, VP of Operations, Ridgetop Group Inc.
Abstract:
"This presentation will provide an overview for how Ridgetop Group applied the IEEE 1856-2017 PHM standard and Systems Engineering principles to develop scalable health management solutions. It will showcase successful integrations across rail, aerospace, and battery energy storage systems, emphasizing how the standard supports both technical implementation and customer education. The talk will also demonstrate how aligning with IEEE 1856-2017 improves system performance, traceability, and decision making."
"AI Reliability, Explainability, and Trustworthiness - Bridging Innovation and Assurance"
Abstract:
"As artificial intelligence and machine learning (AI/ML) increasingly integrates into mission-critical systems and decision-making processes for the next generation (NextG) wireless network, aerospace, healthcare, and smart infrastructures, the dual challenge of leveraging AI to enhance reliability while ensuring the reliability of AI itself is more pressing than ever. This panel will explore the cutting edge of AI/ML reliability, explainability, and trustworthiness, and spotlight how these dimensions intersect to support reliable, resilient, transparent, and secure systems. Our distinguished panelists bring expertise spanning hardware reliability in hyperscale data centers, AI/ML for reliability engineering, software security and assurance, network reliability, and cybersecurity for large systems. Their combined experience from academia, industry, and government research labs ensures a rich, multi-perspective dialogue."
Expected Takeaways:
"Attendees are expected to leave with a comprehensive understanding of the vital role AI plays in reliability-critical systems and decision-making processes, the need of the explainable reliability, the best practices for designing and deploying reliable AI to enhance system reliability and maintain system resilience and robustness, and emerging trends of reliable AI systems and applications."
Target Audience:
"This panel is intended for researchers, engineers, and policymakers in the field of reliable AI development and its applications in reliability-critical systems."
Moderator: Dr. Ruolin Zhou
Bio:
"Dr. Ruolin Zhou is an associate professor of the department of electrical and computer engineering at the University of Massachusetts – Dartmouth. Her research includes software defined radio (SDR) and AI/ML for wireless communications with a particular focus on spectrum sensing and sharing, physical layer design for an intelligent radio, and wireless security. Her research has been funded by National Science Foundation (NSF), the Office of Naval Research (ONR), the Air Force Research Laboratory (AFRL), Army Research Laboratory (ARL), and industry partners such as Lockheed Martin. She is a recipient of the 2024 IEEE Region 1 Outstanding Teaching in an IEEE Area Award, the Best Team Award of the 2020-2021 AFRL SDR Beyond 5G University Challenge, and the Best Demo Award of the IEEE Global Communications Conference (GLOBECOM) in 2010. Dr. Zhou is currently serving as the 2025 Vice President for Technical Activities within the IEEE Reliability Society (RS), the RS liaison on IEEE Women in Engineering, a steering committee member of the IEEE Future Networks Technical Community (FNTC), a co-chair of the IEEE Future Networks Entrepreneurs Mentorship program (FNEM), and a working group member and a voting member of the IEEE Standard P1900.8, a new standard on machine-learned spectrum awareness under development."
Panelists:
Dr. Preeti Chauhan:
"Dr. Preeti Chauhan is a Technical Program Manager (TPM) at Google, leading strategic and transformational initiatives in AI/ML hardware within the Data Center Quality and Reliability group. She leverages her expertise to drive improvements in data center quality, reliability, and deployment speed at Google's massive scale. Her extensive experience encompasses quality and reliability leadership for cutting-edge technologies like Intel's Foveros 3D packaging and server microprocessors. Actively engaged within the engineering community, she serves as a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and co-edits the data column in the prestigious Computer magazine. Dr. Chauhan is currently serving as the 2025 Vice President for Meetings and Conferences within the IEEE Reliability Society and as a liaison to the IRPS Board of Directors."
Dr. Zhaojun Steven Li:
"Dr. Zhaojun Steven Li is with the Department of Industrial Engineering and Engineering Management at Western New England University in Springfield, MA, USA. Dr. Li’s research interests include AI/ML, data analytics, applied statistics, operations research, and reliability engineering. He received his Ph.D. in Industrial Engineering from the University of Washington. He is an ASQ Certified Reliability Engineer (CRE) and Caterpillar Six Sigma Black Belt (SSBB). He has been serving on editorial boards for IEEE Transactions on Reliability and IEEE Access Reliability Society Section. He is a senior member of IISE and IEEE. He has served as a board member of IISE Quality Control and Reliability Engineering (QCRE) Division and IEEE Reliability Society. Dr. Li was the President of the IEEE Reliability Society 2022-2024."
Dr. Jason Rupe:
"Dr. Jason Rupe received his Ph.D. in modeling large scale systems and networks for performance and reliability. He has held titles including senior technical staff and director at USWEST, Qwest, Polar Star Consulting, and Tenica. He was the last Managing Editor for the IEEE Transactions on Reliability, Denver Section Chair, and co-chair of IEEE Blockchain initiative. He is currently the President of the IEEE Reliability Society. At CableLabs, he is the Distinguished Technologist working on Proactive Network Maintenance, Network and Service Reliability, DOCSIS® Tools and Readiness, Optical Operations and Maintenance, and reliability advancement for the industry. He was the RS Engineer of the year for 2021, and CableLabs inventor of the year for 2020."
Rick Kuhn:
"Rick Kuhn is a computer scientist in the Computer Security Division at National Institute of Standards and Technology (NIST), and is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He co-developed the role based access control (RBAC) model that is the dominant form of access control today. His current research focuses on combinatorial methods for assured autonomy (csrc.nist.gov/acts) and hardware security/functional verification. He has authored three books and more than 200 conference or journal publications on cybersecurity, software failure, and software verification and testing. He received an MS in computer science from the University of Maryland College Park and an MBA from William & Mary. Before joining NIST, he worked as a software developer with NCR Corporation and the Johns Hopkins University Applied Physics Laboratory."
Dr. Angelos Stavrou:
"Dr. Angelos Stavrou is a professor of electrical and computer engineering and serves as entrepreneurship lead for the Virginia Tech Innovation Campus. He is the founder of Kryptowire Inc., now Quokka Inc, a venture capital-funded mobile security startup. Stavrou's research interests include large systems security and survivability, intrusion detection systems, and nextG Cyber Security. Dr. Stavrou has served as a NIST guest researcher and is senior IEEE, ACM and USENIX member. He is an active member of NIST's Mobile Security Team and has written more than 140 peer-reviewed conference and journal articles. He has served as principal investigator on research awards from NSF, DARPA, IARPA, DHS, AFOSR, ARO, and ONR. Dr. Stavrou is an associate editor of IEEE Transactions on Computers, IEEE Security and Privacy, and IEEE Internet Computing magazines and a previous co-chair of the IEEE Blockchain initiative. Dr. Stavrou was previously a member of the computer science faculty at George Mason University. He received the GMU Department of Computer Science Outstanding Research Award in 2010, 2016, and 2018 and was awarded with the 2012 George Mason Emerging Researcher, Scholar, Creator Award, a university-wide award. In 2013, he received the IEEE Reliability Society Engineer of the Year award. Under DHS funding, Kryptowire designed and implemented novel MDM and analysis software that can collect mobile application and network telemetry from mobile devices for which his team was awarded the DHS Cyber Security Division's "Significant Government Impact Award" in 2017 and "Bang for the Buck Award" in 2019."
Workshops
"Workshop 1: "Integrative Innovations in Personalized Medical Technologies"
Tutorial speaker: Dr. Bui - "AIoT-Enabled Portable and Wearable Health
Monitoring Systems"
Bio:
"Dr. Bui is an Assistant Professor of Electrical Engineering, specializing in the development of cutting-edge and practical Smart Health Platforms. His research focuses on advancing sensing technologies for medical devices, including the development of innovative techniques to enhance mobile and wearable health monitoring systems and the design of embedded intelligent systems that leverage the latest advancements in Artificial Intelligence and Machine Learning for impactful medical applications. His contributions have been recognized with numerous prestigious honors, including Best Paper Awards at ACM MobiCom 2019, Best Paper nominations at ACM SenSys 2018, selection for ACM SIGMOBILE Research Highlights in 2020, and inclusion in the Communications of the ACM Research Highlights in 2021. As a recipient of several competitive research grants sponsored by the U.S. National Science Foundation (NSF) and the U.S. Department of Defense (DoD), Dr. Bui continues to lead the development of advanced sensing technologies and computational models for critical healthcare applications, with this proposed research representing a natural extension of his ongoing work."
Abstract:
" This presentation introduces the mission and vision of portable and wearable technologies in the healthcare, particularly in the context of recent advancements in artificial intelligence (AI). It highlights the rapid development of wearable technologies—such as microcontrollers and microprocessors—and showcases innovative projects including in-ear blood pressure monitoring, non-invasive glucose measurement, and wearable devices designed for critical conditions like face-touch detection. With the integration of Artificial Intelligence of Things (AIoT), these technologies are now addressing more complex challenges, such as sleep monitoring and enhancement through EEG headbands. Furthermore, portable and wearable devices are enabling novel applications in medical treatment and rehabilitation, exemplified by our 3D volumetric display project. The presentation concludes by reaffirming the transformative potential of AIoT in shaping the future of healthcare, emphasizing a vision centered on accessibility, personalization, and proactive health management."
Tutorial speaker: Dr. Carpenter - "Investigating the Impact of Porous
Coating Extent and Bone Mineral Density on
Mechanical Stresses for Patients with Bone-anchored
Limbs"
Bio:
"Dr. Dana Carpenter is an Associate Professor in the Department of Mechanical Engineering at the University of Colorado Denver. He earned his BS in Mechanical Engineering from the Georgia Institute of Technology in 1999, and he then went on to earn his MS and PhD in Mechanical Engineering from Stanford University in 2002 and 2006, respectively. Dr. Carpenter’s research combines mechanical testing, computational modeling, and image-based analysis to investigate the biomechanics and mechanobiology of bones, joints, and orthopedic implants. He has performed mechanical testing, image-based biomechanical analysis, volumetric bone mineral density analysis, biomechanical strength analysis, and finite element analysis of skeletal structures and orthopedic implanted devices. His current research focuses on the biomechanics of people with bone-anchored limbs, combining motion capture data and image-based finite element modeling to investigate the mechanical environment in bones and implants during walking."
Abstract:
"This presentation shows how motion capture data and medical image-based computational modeling can be combined to analyze the stress and strain in bone-implant systems during walking. This new modeling pipeline is used to investigate the effects of bone quality (in terms of bone mineral density) and the extent of osseointegration (bone ingrowth that attaches implants to skeletal structures) affect the stress distribution in the residual bones of people with bone-anchored prosthetic limbs. The data produced by this new modeling technique can help to identify promising candidates for implantation of bone-anchored limbs and to help identify the causes of clinical problems like implant loosening and post-implantation fracture."
"Workshop 2: "Introduction to PHM Theory and Practice"
Tutorial speaker: Dr. Stephen Johnson
Bio:
" Dr. Stephen Johnson is the President of Dependable System Technologies, LLC, and the general editor for System Health Management: with Aerospace Applications (2011). His PHM experience includes being the control system fault protection engineer on the Magellan deep space probe to Venus in the 1980s, the head of Martin Marietta Astronautics Vehicle Health Management research in the early 1990s, a co-founder and head of engineering for a small PHM business in the 1990s, a faculty member of the Space Studies Department at the University of North Dakota from 1997 to 2005, the Analysis Lead for Mission and Fault Management on NASA Marshall Space Flight Center’s Space Launch System program and its precursors from 2005 to 2023, and numerous small PHM and systems engineering R&D contracts with government and industry from 2005 to the present. He has authored and co-authored numerous articles and books on system health management and fault management theory and practice, and other topics including systems engineering, space history and economics, and the philosophy of technology.
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Abstract:
" This session is split between a 1 hr introduction to the concepts that will be discussed in the 5 hr workshop. The workshop is intended to be a one-day short course (shortened to 5 hrs) that introduces the core concepts and practices of Prognostics and Health Management / System Health Management.
The tutorial and workshop provide an introductory overview of the following:
1. history of and motivation for PHM;
2. core concepts and terminology;
3. fault management functions;
4. goals and requirements;
5. architecture and design issues and strategies;
6. technical performance metrics;
7. analysis methods.
The tutorial will start on Tuesday morning (June 10), followed by the workshop, and end the day with the PHM Standards Panel.
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