Bio: Dr. Nancy R. Mead is a Fellow at the Software Engineering Institute (SEI) and an adjunct professor at Carnegie Mellon University, focusing on security requirements engineering and software assurance curricula. She previously served as SEI’s director of education (1991–1994). With over 150 publications, her research interests span software security and requirements engineering. Before SEI, Mead was a senior technical staff member at IBM Federal Systems, working on large real-time systems and managing IBM’s software engineering education. She has developed and taught software engineering courses for both academic and professional audiences. Mead serves on editorial boards for the International Journal of Systems and Software Security and Protection and the Requirements Engineering Journal, and holds memberships on the IEEE TCSE Executive Committee and the Open University Advisory Board. A Fellow of IEEE and the IEEE Computer Society, and a Distinguished Educator of the ACM, she has received the IEEE Distinguished Education Award (2015) and was named a Parnas Fellow at Lero in 2019.
Speech Title: AI and Cybersecurity
Abstract: Although cybersecurity has long been a concern of software and hardware systems, the introduction of AI assistance has quickly resulted in changes in the speed and nature of successful cyberattacks. It also changes the ability to protect and defend against attacks. Although AI assistance is in its infancy, thoughtful scientists and engineers need to understand the increased risks and benefits of AI usage. This talk will provide examples of AI usage for both good and harm from a cybersecurity perspective and discuss future possibilities.
Bio: Dr. James Xiaojiang Du is an Endowed-Chair Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. His research interests are security, wireless networks, and systems. He has authored over 600 journal and conference papers in these areas, including the top security conferences such as IEEE Security & Privacy (Oakland), USENIX Security, CCS, and NDSS. He is an IEEE Fellow, a Fellow of the European Alliance for Innovation (EAI), a Fellow of the Asia-Pacific Artificial Intelligence Association, an ACM Distinguished Member, and an ACM Life Member. He serves on the editorial boards of three IEEE/ACM journals. He was a chair and PC member of several premium conferences, such as IEEE Infocom, EAI SecureComm, and IEEE/ACM IWQoS.
Bio: Stephen L. Smith is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada, where he holds a Canada Research Chair in Autonomous Systems. He co-directs the Waterloo Data & Artificial Intelligence Institute and leads the Autonomous Systems Lab. Prior to joining Waterloo, he was a postdoctoral researcher at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds degrees from Queen's University, the University of Toronto, and UC Santa Barbara. Prof. Smith is a licensed Professional Engineer and has advised several startups in transportation and robotics. He has served on editorial boards and organizing committees for major IEEE journals and conferences, including IEEE Transactions on Robotics, IEEE Transactions on Control of Network Systems, ACC, RO-MAN, and MTNS. His honours include the Ontario Early Researcher Award, an NSERC Discovery Accelerator Supplement, and multiple Outstanding Performance Awards from Waterloo. His research focuses on control and optimization for autonomous systems, with emphasis on safe motion planning and human-autonomy interaction.
Speech Title: Planning to Push: Robot Motion Planning in Environments with Movable Obstacles
Abstract: Mobile robots are increasingly expected to operate in complex, unstructured environments where not all obstacles are static. In many scenarios, the most efficient or only path to a goal requires the robot to purposefully interact with and move objects in its way. This talk will discuss the challenges and opportunities of robot motion planning among movable obstacles. We will begin by examining the problem of autonomous ship navigation in ice-covered waters, a real-world scenario where predicting the vessel's interaction with ice is essential for safe and efficient passage. We will discuss our initial approach using a classical planner with simplified interaction physics, and then introduce a more sophisticated method that leverages a deep learning model to predict the motion of obstacles in response to the robot's actions. Finally, we will show how these principles of "planning to push" can be generalized to a broader set of interactive navigation tasks, such as clearing debris or maneuvering objects in cluttered spaces. Through this progression, we will highlight the key considerations for enabling intelligent robotic interaction in dynamic and deformable environments.
Bio: Hamid Marvi is an Associate Professor of Mechanical and Aerospace Engineering at Arizona State University, where he also holds the Fulton Entrepreneurial Professorship and directs the Bio-Inspired Robotics, Technology, and Healthcare (BIRTH) Lab. He is a Senior Global Futures Scientist and an Alliance Fellow of the Mayo Clinic–ASU Alliance for Health Care. His research integrates materials science, robotics, and biology to develop soft and magnetic robots for medical applications. Dr. Marvi’s work has been featured in Science, PNAS, Advanced Materials, and Nature Scientific Reports, as well as in major media outlets like The New York Times and BBC. His honors include Senior Membership in the National Academy of Inventors, the 2024 ABRC New Investigator Award, the Sigma Xi Best Ph.D. Thesis Award, and multiple national awards for innovation in robotics and soft materials.