AI-Ready Workforce Certificate | WSU Everett | May 14-15 [] Program Description The AI-Ready Workforce Certificate is an intensive, two-day, practice-focused training designed to equip students with the AI skills employers increasingly expect – across industries and roles. This program emphasizes hands-on application, ethical and secure AI use, and real workplace workflows participants can immediately apply in internships, entry-level roles, and current jobs. Format: In-person, two-day training Dates: Thursday-Friday, May 14-15 Schedule: 8:30 AM – 5:00 PM WSU Everett, Room 102 Includes: Coffee, snacks, and lunch Certificates: Distributed digitally at program completion What You’ll Learn – Understand AI foundations and how AI is used in modern workplaces – Work effectively with AI tools and agents as part of professional teams – Apply ethical, safe, and secure AI practices – Build practical prompts and AI-enabled workflows for real tasks What You’ll Take Away – A WSU AI-Ready Workforce Certificate (non-credit, co-curricular) – Demonstrate, job-ready AI skills – Confidence using AI responsibility in academic and professional environments. The certificate may be listed on resumes, LinkedIn profiles, Handshake, and professional portfolios as evidence of applied AI competence. Registration Price is determined based on registration type that is selected. – WSU student – $35 – Non-WSU student – $50 – Industry – $75 When paying, just enter email and checkout as a guest. (No need for login to TouchNet.) Contact everett.it@wsu.edu for any questions on this program. (https://secure.touchnet.net/C20607_ustores/web/product_detail.jsp?PRODUCTID=3890&FROMQRCODE=true) Room: Room 102, Bldg: WSU Everett University Center, 915 N Broadway, Everett, Washington, United States

Autonomous mobility is largely approached as a vehicle-centric problem. Persistent challenges in safety, scalability, and public trust suggest a deeper issue: “intelligence” is often considered in isolation rather than as distributed. This presentation argues that truly safe and trustworthy autonomy will emerge only through symbiotic computational systems, where perception, decision-making, and control are distributed across humans, machines, and infrastructure. The presentation starts with an overview of the four decades-long progress in autonomous driving and related advancements in driver assistance technologies. It is followed by a discussion of the central thesis: that many failures in autonomous mobility stem not from algorithms alone, but from how system boundaries are defined— what is sensed, where intelligence resides, and how responsibility is shared. Framing autonomy as a systems- level problem, the talk draws on principles of distributed and embodied cognition to unify perspectives from robotics, artificial intelligence, human–computer interaction, and transportation engineering. Concrete examples from multidisciplinary research by the CVRR and LISA teams at UC San Diego, conducted on real vehicles in real-world driving environments and validated through both quantitative benchmarks and qualitative studies in collaboration with industry partners, illustrate how shared autonomy can tightly couple human state (e.g., intent, attention, readiness) with environmental context to enable safer and more adaptive human–AI interaction. The lecture also discusses how advances in foundation models, self-supervised learning, and active learning can improve generalization and robustness in safety-critical settings. The talk concludes with key open challenges, including multimodal foundation models for traffic ecosystems, human–AI co-adaptation, and continual learning under domain shift, important problems to realize scalable, trustworthy autonomous mobility. Co-sponsored by: Vishnu S. Pendyala, San Jose State University Speaker(s): Professor Mohan Trivedi, Dr. Vishnu S Pendyala Room: MLK Room 225, Dr. Martin Luther King, Jr. Library (SJSU), 150 E San Fernando St San Jose, California 95112, San Jose, California, United States, Virtual: https://events.vtools.ieee.org/m/556950

Zoom Link: https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1 Talk Abstract: Digital twins of the wireless environments offer new capabilities to the communication network design and operation. They could be utilized offline to build site-specific datasets for pre-training and evaluation machine learning models, or online to provide real-time or near real-time priors that aid the various communication system decisions on precoding, channel estimation, spectrum sharing, resource allocation, among many interesting applications. In this talk, I will present key aspects and considerations for modeling, building, calibrating, and utilizing these digital twins to maximize their gains while balancing constraints on cost, latency, and computational overhead. I will also introduce DeepVerse 6G, the world’s first large-scale digital-twin research platform, which provides high-fidelity multi-modal sensing and communication “true” digital twin datasets to accelerate research and development across a wide range of applications. Speaker Biography: Ahmed Alkhateeb received his B.S. and M.S. degrees in Electrical Engineering from Cairo University, Egypt, in 2008 and 2012, and his Ph.D. degree in Electrical and Computer Engineering from The University of Texas at Austin, USA, in 2016. After the Ph.D., he spent some time as a Wireless Communications Researcher at the Connectivity Lab, Facebook, before joining Arizona State University (ASU) in the Spring of 2018, where he is currently an Associate Professor in the School of Electrical, Computer, and Energy Engineering. His research interests are in the broad areas of wireless communications, signal processing, machine learning, and applied math. Dr. Alkhateeb is the recipient of the 2012 MCD Fellowship from The University of Texas at Austin, the 2016 IEEE Signal Processing Society Young Author Best Paper Award for his work on hybrid precoding and channel estimation in millimeter-wave communication systems, and the NSF CAREER Award 2021 to support his research on leveraging machine learning for large-scale MIMO systems. Meeting Link: https://ulaval.zoom.us/j/65778451409?pwd=B1j19PbbWPhyXWjxkTf9PjOfIekUCY.1, Québec City, Quebec, Canada, G1X 4C5