Abstract: Digital Manufacturing aims to make the industry more productive, resilient, and globally competitive than ever by improving production efficiency, enhancing product quality, eliminating unplanned maintenance, and reducing energy use. To achieve this, we are working together with the industry to develop advanced digital-twins, AI, platform solutions, and a variety of adoption/commercialization pathways that will shape the future of manufacturing. In this talk we focus on the development of advanced digital twins for representing complex interconnected physical entities in manufacturing, which include sophisticated industrial machines, materials and products, and people involved in production. To achieve dramatic productivity and resilience improvements we devise AI/ML models for digital manufacturing that seamlessly interact with industrial digital twins to get the information they need and become aware of the physical constraints and other data dependencies in the production environment, and use these to optimize production outcomes, predict production issues (such as inefficiencies, product inconsistencies, unplanned maintenance issues) before they occur, and correct them before they affect production outcomes. We discuss the development of a novel digital manufacturing platform for reducing the cost and effort of developing digital manufacturing applications by supporting the open development, easy combination and complete lifecycle management of such digital twins and dependency-aware AI/ML models. We present examples of digital manufacturing solution from over thirty digital manufacturing projects we have conducted together with industry partners in a variety of sectors ranging from food to steel and composites, and tools and vehicles. More importantly, we outline the unique research challenges, opportunities, and directions for digital twin and AI research that can transform the industry and outline the role of a research/industry consortium such as the ARC Digital Manufacturing Hub plays in achieving that.

Bio: Dimitrios Georgakopoulos is the Director of the Australian Research Council’s (ARC’s) Digital Manufacturing Research Hub (which is, an Australian Industrial Transformation Research consortium of seven universities, twenlve companies, and the ARC), and Computer Science Professor at Swinburne University of Technology in Melbourne, Australia. Before that he was Research Director at the ICT Centre of the Commonwealth Scientific and Industrial Research Organization’s (Australia's national research agency’s), where he led Australia’s largest Computer Science research program, and a Professor at RMIT University. Earlier, Dimitrios held senior research and management positions in major industrial laboratories in the USA, including Telcordia Technologies; Microelectronics and Computer Corporation (MCC); GTE Laboratories/Verizon; and Bell Communications Research (Bellcore). Dimitrios has authored 325 articles in IoT, AI, process automation, and data management that have received more than 24,250 citations. According to Elsevier’s science-wide author databases of standardized citation indicators, he is in the 2% of top-cited authors globally (top 1% if self-citations are excluded) in both career and 2024 rankings. His research has attracted $77M of external research grants/contracts. He also led major cross-disciplinary research initiatives in social services, digital agriculture, water management, and defense that received over $100M of research funding from the Australian and US governments. Dimitrios has received 18 awards that include, 2023 National iAward for Government & Public Sector Solution of Year from the Australian Information Industry Association’s, several best paper awards in CORE A*/A flagship conferences, the 2018 Vice-Chancelor’s Research award, and several industry awards that include the 2013 Black Duck Open Source Rookie of the Year Award.

Abstract: While foundation models have revolutionized computing and beyond, the field is still grappling with creating high value applications that meet the reliability and safety requirements of the real world. In this talk, I will discuss the emergence of a new computing stack grounded on foundation models to power a new generation of AI applications called AI agents. My team at Microsoft Research has been at the forefront of the agentic developments with our work on AutoGen, MagenticOne, Phi, Orca and OmniParser models and I will share learnings with respect to the future of agentic AI. The talk will conclude with a discussion of the new opportunities and risks introduced by the emerging agent stack.

Bio: Ece Kamar is the Managing Director of AI Frontiers, leading research and development towards pushing the frontiers of AI capabilities. AI Frontiers is a non-geographical, mission-focused lab inside Microsoft Research that explores innovations in foundation models and platform capabilities to push the frontier of AI capabilities, efficiency and control. Ece’s personal research focuses on developing AI systems that can function reliably in the open world in collaboration with people. She has a decade of experience studying the impact of AI on society and developing AI systems that are reliable, unbiased and trustworthy. She has been instrumental in building the Responsible AI efforts inside Microsoft. She serves as Technical Advisor for Microsoft’s Internal Committee on AI, Engineering and Ethics. Ece is also an Affiliate Faculty with the University of Washington.

Bio: Dr. Huan Liu is a Regents Professor and Ira A. Fulton Professor of Computer Science and Engineering at Arizona State University. He is the recipient of the ACM SIGKDD 2022 Innovation Award for his outstanding contributions to the foundation, principles, and applications of social media mining and feature selection for data Mining. He co-authored the textbook, Social Media Mining: An Introduction, by Cambridge University Press. He is Editor in Chief of ACM TIST, Founding Field Chief Editor of Frontiers in Big Data, and a founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling, and Prediction. He is a Fellow of ACM, AAAI, AAAS, and IEEE.

Abstract: Artificial Intelligence and the Internet of Things are contributing to the collection and use of our data across ever more diverse scenarios. In the process they open the door to new privacy and security threats. Yet these technologies can also contribute to the development of solutions that help address these threats. Many of these threats have to do with the limitations we, humans, have when it comes to recognizing threats and protecting ourselves against them. This presentation will provide an overview of work in usable security and privacy over the past 15 years drawing on research conducted with my collaborators at Carnegie Mellon University. This will include our work on the development of privacy and security assistants, our work on privacy and security nudging, our work on semi-automated compliance analysis, and our recent work on user-oriented privacy threat modeling. I will further discuss our work on a privacy infrastructure for the internet of things and its deployment in smart cities. I will also share my experience integrating some of this research into our education programs at CMU, including our privacy engineering program, and discuss how our work has influenced developments in both industry and government over the years.

Bio: Norman Sadeh (https://normsadeh.org) is a Professor in the School of Computer Science at Carnegie Mellon University (CMU). He co-founded and co-directs CMU’s Privacy Engineering Program, and also co-founded and for ten years co-directed CMU’s PhD Program in Societal Computing. Norman served as lead principal investigator on two of the largest US research projects in privacy, the Usable Privacy Policy Project (https://usableprivacy.org) and the Personalized Privacy Assistant Project (https://privacyassistant.org). He was also founding CEO and, until its acquisition by Proofpoint, chairman and chief scientist of Wombat Security Technologies, a company that defined the multi-billion dollar user-oriented cybersecurity market. Technologies Norman developed with colleagues at CMU and Wombat are used to protect tens of millions of users around the world against cybersecurity attacks such as phishing. Dr. Sadeh's privacy research has been credited with influencing the development of privacy-enhancing solutions at companies such as Apple, Google and Facebook, and results of his research have informed activities at regulatory agencies, including the Federal Trade Commission and the California Office of the Attorney General. In the late nineties Norman also served as Chief Scientist of the EUR 550 million European Union's e-Commerce initiative, which included all pan-European research in cybersecurity and privacy as well as contributions to several major European public policy initiatives.

Abstract: Artificial Intelligence (AI) techniques ae being applied to numerous applications from Healthcare to Cyber Security to Finance. For example, Machine Learning (ML) algorithms are being applied to solve security problems such as malware analysis and insider threat detection. However, there are many challenges in applying ML algorithms for various applications. For example, (i) the ML algorithms may violate the privacy of individuals. This is because we can gather massive amounts of data and apply ML algorithms on the data to extract highly sensitive information. (ii) ML algorithms may not be fair to various segments of the population. (iii) ML algorithms themselves may be attacked possibly resulting in catastrophic errors including in cyber physical systems such as transportation systems. In this presentation, we discuss our research we are conducting as part of the USDOT National University Technology Center TraCR (Transportation Cybersecurity and Resiliency) led by Clemson University. In particular, we describe (i) the application of federated machine learning techniques for detecting attacks in transportation systems; (ii) publishing synthetic transportation data sets that preserves privacy, (iii) fairness algorithms for transportation systems, and (iv) examining how GenAI systems are being integrated with transportation systems to provide security. Finally we discuss resiliency issues with respect to transportation systems where such systems and applications must continue to operate in the midst of attacks and failures.

Bio: Dr. Bhavani Thuraisingham is the Founders Chair Professor of Computer Science and the Founding Executive Director of the Cyber Security Research and Education Institute at the University of Texas at Dallas (UTD). She is an elected Fellow of the ACM, IEEE, the AAAS, and the NAI. Her research interests are integrating cyber security and artificial intelligence/data science including as they relate to the cloud, social media, and Transportation Systems. She has received several technical, education and leadership awards including the IEEE CS 1997 Edward J. McCluskey Technical Achievement Award, the IEEE CS 2023 Taylor L. Booth Education Award, ACM SIGSAC 2010 Outstanding Contributions Award, the IEEE Comsoc Communications and Information Security 2019 Technical Recognition Award, the IEEE CS Services Computing 2017 Research Innovation Award, the ACM CODASPY 2017 Lasting Research Award, and the ACM SACMAT 10 Year Test of Time Awards for 2018 and 2019 (for papers published in 2008 and 2009). Her 44+ year career includes industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and US Academia. Her work has resulted in 140+ journal articles, 300+ conference papers, 200+ keynote and featured addresses, seven US patents, sixteen books, and over 120 panel presentations including at Fortune Media, Lloyds of London Insurance, Dell Technologies World, United Nations, and the White House Office of Science and Technology Policy. She has also written opinion columns for popular venues such as the New York Times, Inc. Magazine, Womensday.com and the Legal 500, She received her PhD from the University of Wales, Swansea, UK, and the prestigious earned higher doctorate (D. Eng) from the University of Bristol, UK. She also has a Certificate in Public Policy Analysis from the London School of Economics and Political Science. She has been featured in the book by the ACM in 2024 titled: “Rendering History: The Women of ACM-W” as one of the 30+ “Women that Changed the Face of World Wide Computing Forever.”