Events

Tuesday, November 21st at 5:00 p.m., Dr. Philip Asare, Assistant Professor of Electrical and Computer Engineering at Bucknell University, will be presenting “Data-Driven Care: Enabling Science and Technologies”. Day & Time: Tuesday November 21st, 2017 5:00 p.m. – 6:00 p.m. Speaker: Dr. Philip Asare Assistant Professor of Electrical and Computer Engineering Swanson Fellow in Sciences and Engineering Multicultural Student Services Faculty Fellow (Fall 2015) Bucknell University Location: Room ENG-LG 12 George Vari Engineering Building (Intersection of Church & Gould) Ryerson University 245 Church St, Toronto, M5B 1Z4 Contact: Alireza Sadeghian, Alex Dela Cruz Organizer: Signals & Computational Intelligence Chapter Abstract: Recent advances in medical technologies provide an opportunity to collect and use a variety of data to assist in the delivery of care to patients in and out of the clinic. In the clinic, tools can be developed that provide insights into patient state that were not previously possible. In some cases various actions can be automated to assist clinicians in delivering care. Outside the clinic, patients can be empowered to manage their own care as they go about their daily lives without being confined to the hospital. Quite a number of impressive technologies have been demonstrated in the research space with a few emerging as commercial projects on the market; however, there are a number of challenges to overcome in order to realize the full potential of these technological advances. This talk will describe past and on-going work in this area by the speaker and others to ensure that the data are trustworthy, the tools that depend on the data are robust and safe, and the technologies are more likely to be adopted by the healthcare ecosystem. These would hopefully lead to the greatest possible impact for patients and their care providers. Biography: Philip Asare is an Assistant Professor of Electrical and Computer Engineering and Swanson Fellow in the Sciences and Engineering at Bucknell University, in Lewisburg, Pennsylvania, in the USA. He is currently a Visiting Scholar/Professor in Electrical and Computer Engineering at Ryerson University during his leave from Bucknell for the 2017-18 academic year. His research interests are in the general are of cyber-physical systems with medicine being one of his primary application areas. He was a Scholar-in-Residence at the U.S. Food and Drug Administration for the 2012-13 academic year working with researchers in the Office of Science and Engineering Laboratories on regulatory approaches for emerging mobile connected medical devices. His work in this area has received a best student paper and best paper award at the Interncation Conference on Body Area Networks (BodyNets). He most recently co-organize the Prototype to Patient Treatment workshop as part of the 2016 Annual Wireless Health Conference through the National Science Foundation Nanosystems Engineering Research Center (NERC) for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST). Asare is a member of the IEEE and its Computer Society and Engineering in Medicine and Biology Society (EMBS). He is also a member of the ACM and its Special Interest Group on Embedded Systems (SIGBED).

Monday, November 27th at 10:30 a.m., Prof. C.-C. Jay Kuo, Fellow of IEEE and Dean’s Professor in Electrical Engineering-Systems, University of Southern California, will be presenting “Why Deep Learning Works So Well?”. Day & Time: Monday, November 27, 2017 10:30 a.m. ‐ 11:30 a.m. Speaker: Prof. C.-C. Jay Kuo, Fellow of IEEE, AAAS, SPIE Dean’s Professor in Electrical Engineering-Systems, University of Southern California Location: Room ENG 358 George Vari Engineering Building (Intersection of Church & Gould) Ryerson University 245 Church St, Toronto, M5B 1Z4 Contact: Xiao-Ping Zhang, Alireza Sadeghian, Alex Dela Cruz Organizer: Electrical and Computer Engineering and CASPAL Ryerson Signals & Computational Intelligence Chapter Abstract: Deep learning networks, including convolution and recurrent neural networks (CNN and RNN), provide a powerful tool for image, video and speech processing and understanding nowadays. However, their superior performance has not been well understood. In this talk, I will unveil the myth of the superior performance of CNNs. To begin with, I will describe network architectural evolution in three generations: first, the McClulloch and Pitts (M-P) neuron model and simple networks (1940-1980); second, the artificial neural network (ANN) (1980-2000); and, third, the modern CNN (2000-Present). The differences between these three generations will be clearly explained. Next, theoretical foundations of CNNs have been studied from the approximation, the optimization and the signal representation viewpoints, and I will present main results from the signal processing viewpoints. I will use an intuitive way to explain the complicated operations of the CNN systems. Biography: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 140 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. Dr. Kuo is a co-author of about 250 journal papers, 900 conference papers and 14 books.