Abstract: Thanks to major advances in neuroscience, we are on the brink of a scientific understanding of how the brain achieves consciousness. This talk will describe neuroscientist Bernard Baars' Global Workspace Model (GWM) of the brain, its implications for understanding consciousness, and a novel computer architecture that it inspires.
The major contribution of this talk lies in the precise formal model of Conscious Turing Machine (CTM) aka Conscious AI (CAI) to be presented.
This is joint work of Manuel and Lenore Blum.

Abstract: The nature of engineering and engineers has evolved significantly since the turn of the 21st century. How should universities adjust to this change, and how should we conceive of engineering research and education in this new context? I explore this question with the hope that it will lead to new insights into the role and shape of engineering schools of the future.

Bio: Alexander L. Wolf serves as Dean of the Baskin School of Engineering and is a Distinguished Professor of Computer Science and Engineering at the University of California, Santa Cruz. Prior to this he was a Professor of Computing at Imperial College London, Professor of Computer Science at the University of Colorado at Boulder, and a Member of the Technical Staff at AT&T Bell Laboratories. Prof. Wolf's research interests span the areas of distributed systems, networking, and software engineering. His achievements include seminal work in software architecture, business analytics, and information-centric networks.
   Prof. Wolf served as President of the Association for Computing Machinery (ACM), is a Fellow of the ACM, a Fellow of the IEEE, a Chartered Fellow of the BritishComputer Society, holder of a Royal Society-Wolfson Research Merit Award, two-time recipient of an ACM SIGSOFT Research Impact Award, recipient of both the ACM SIGSOFT Outstanding Research Award and Distinguished Service Award, and recipient of an Alumni Award for Outstanding Achievement in Research from the Department of Computer Science at the University of Massachusetts at Amherst.

Abstract: Driven by advances in medicine and increased lifespans, societies are now aging at an alarming rate. This fact presents a host of new challenges - many seniors live alone and are subject to falls, accidental injuries, chronic disease exacerbations, and depression. The situation places an alarming burden on our health care system and society more generally, a burden that is only expected to grow over time.
  This talk will introduce Emerald, a new technology that uses machine learning for health monitoring in the home. Emerald automates health monitoring through innovations in wireless sensing and machine learning. The Emerald device is a Wi-Fi like box that transmits low power radio signals, and analyzes their reflections using neural networks. It infers the movements, breathing, heart rate, falls, sleep apnea, and sleep stages, of people in the home -- all without requiring them to wear any sensors or wearables. By monitoring a variety of physiological signals continuously and without imposing a burden on users, Emerald can automatically detect degradation in health, enabling early intervention and care. The talk will describe the underlying technology, and present results demonstrating Emerald's promise in a geriatric population.

Bio: Dina Katabi is the Andrew & Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. She is also the director of the MIT’s Center for Wireless Networks and Mobile Computing, a member of the National Academy of Engineering, and a recipient of the MacArthur Genius Award. Professor Katabi received her PhD and MS from MIT in 2003 and 1999, and her Bachelor of Science from Damascus University in 1995. Katabi's research focuses on innovative mobile and wireless technologies with application to digital health. Her research has been recognized with ACM Prize in Computing, the ACM Grace Murray Hopper Award, the SIGCOMM test of Time Award, the Faculty Research Innovation Fellowship, a Sloan Fellowship, the NBX Career Development chair, and the NSF CAREER award. Her students received the ACM Best Doctoral Dissertation Award in Computer Science and Engineering twice. Further, her work was recognized by the IEEE William R. Bennett prize, three ACM SIGCOMM Best Paper awards, an NSDI Best Paper award, and a TR10 award. Several start-ups have been spun out of Katabi's lab.