August 11, 2016 at 1:00 p.m. Dr. Faycal Saffih, Department of Electrical Engineering, UAE University, will be presenting “Artificially Intelligent Imaging (AI2): System to Circuit to Device Level Implementations of Smart CMOS Imaging, A Generalized Approach for Non-Application Specific Intelligence Design (NAS-ID)”. Speaker: Dr. Faycal Saffih Assistant Professor, Department of Electrical Engineering UAE University Day & Time: Thursday, August 11, 2016 1:00 p.m. – 2:00 p.m. Location: Room ENGLG 05 George Vari Engineering Building Department of Electrical and Computer Engineering Ryerson University Contact: Dimitri Androutsos Abstract: In this talk we will present the development of intelligence (vs intelligent) implementations from top-down and bottom-up approaches and from Electrical engineering design and Biological Biomimicry to Solid-state Physics prediction. Smart CMOS imaging is the application of choice where these multi-disciplinary studies interacts to suggest a novel approach for research to design intelligent devices needed in a verity of advanced technological devices and systems for a variety of applications such as biomedical and renewables systems and devices to name a few. Biography: Dr. Fayçal Saffih (IEEE Member since 2000) received the B.Sc. (with Best Honors) degree in Solid-State Physics from the University of Sétif-1, Sétif, Algeria, in 1996, the M.Sc. degree in Digital Neural networks from Physics Department, University of Malaya, Kuala Lumpur, Malaysia, in 1998, and the Ph.D. degree in Smart CMOS Imaging from Electrical and Computer Engineering Department, University of Waterloo, Waterloo, ON, Canada. Taking a decade journey between academia and industry, Dr. Saffih enriched his experience multidimensionally spanning Microelectronics from devices up-to systems, and industry from R&D department to Entrepreneurship start-up, all of which from West USA (OR) to Singapore’s prestigious A*star Agency for Science, Technology and Research. Recently, Dr. Saffih endeavored into renewable energy research and business starting from Stanford certification in 2013 and currently undertaking an Online program from Renewables Academy (RENAC), Germany Dr. Faycal Saffih is currently an assistant professor at the Electrical Engineering Department of the UAE University and a regular visiting scholar at the University of Waterloo, University of Alberta among others. His research is on intelligence extraction and implementation on devices and systems particularly smart CMOS image sensors.

Thursday October 27, 2016 at 1:00 p.m. Prof. Sadaoki Furui, IEEE Fellow and President of Toyota Technological Institute at Chicago, will be presenting “Perspectives of Automatic Speech Recognition (ASR) Technology”. Speaker: Prof. Sadaoki Furui IEEE Fellow President of Toyota Technological Institute at Chicago Day & Time: Thursday, October 27, 2016 12:00 p.m. – 1:00 p.m. Location: TRS 1129 Ryerson University Toronto Abstract: DNNs (Deep Neural Networks) based on “deep learning” have significantly raised the automatic speech recognition (ASR) performance as of several years ago. This talk gives an overview of major DNN-based techniques successfully used in acoustic and language modeling for ASR. However, what we can do with ASR technology is still very limited, and we still have many challenges that cannot be solved simply by relying on the capability of DNNs. Data sparseness is one of the most difficult problems in constructing ASR systems, since speech is highly variable and it is too costly to construct annotated “big speech data” covering all possible variations. We need to focus on how to collect rich and effective speech databases covering a wide range of variations, active learning for automatically selecting data for annotation, cheap, fast and good-enough transcription, and efficient supervised, semi-supervised, or unsupervised training/adaptation, based on advanced machine learning techniques. We also need to extend current efforts and think deeply about and analyze how human beings are recognizing/understanding speech, and implement various knowledge sources in ASR systems using machine learning techniques to achieve innovations. This talk focuses on my personal perspectives for the future of speech recognition research. Biography: Sadaoki Furui Received the B.S., M.S., and Ph.D. degrees from the University of Tokyo, Japan in 1968, 1970, and 1978, respectively. After joining the Nippon Telegraph and Telephone Corporation (NTT) Labs in 1970, he has worked on speech analysis, speech recognition, speaker recognition, speech synthesis, speech perception, and multimodal human-computer interaction. From 1978 to 1979, he was a visiting researcher at AT&T Bell Laboratories, Murray Hill, New Jersey. He was a Research Fellow and the Director of Furui Research Laboratory at NTT Labs. He became a Professor at Tokyo Institute of Technology in 1997. He was Dean of Graduate School of Information Science and Engineering, and Director of University Library. He was given the title of Professor Emeritus and became Professor at Academy for Global Leadership in 2011. He is now serving as President of Toyota Technological Institute at Chicago (TTI-C). He has authored or coauthored around 1,000 published papers and books. He was elected a Fellow of the IEEE, the Acoustical Society of America (ASA), the Institute of Electronics, Information and Communication Engineers of Japan (IEICE) and the International Speech Communication Association (ISCA). He received the Paper Award and the Achievement Award from the IEEE SP Society, the IEICE, and the Acoustical Society of Japan (ASJ). He received the ISCA Medal for Scientific Achievement, and the IEEE James L. Flanagan Speech and Audio Processing Award. He received the NHK (Japan Broadcasting Corporation) Broadcast Cultural Award and the Okawa Prize. He also received the Achievement Award from the Minister of Science and Technology and the Minister of Education, Japan, and the Purple Ribbon Medal from Japanese Emperor.

November 2-4, IEEE Signal Processing Society (SPS) is hosting a Winter School on Distributed Signal Processing for Secure Cyber Physical Systems at Concordia. Speakers: This event consists of presentations given by internationally well-known Distinguished Speakers including members of IEEE Signal Processing Society Board of Governors, 6 IEEE Fellows, and a Notable Industry-based Presentation form PwC’s Cybersecurity & Privacy Practice in Canada as follows: Prof. Ali Sayed (UCLA, President-Elect of IEEE SPS); Prof. Georgios Giannakis (IEEE Fellow, University of Minnesota); Prof. Pramod Varshney (IEEE Fellow, Syracuse University); Prof. Deepa Kundur (IEEE Fellow, University of Toronto); Prof. Anna Scaglione (IEEE Fellow, Arizona State University); Prof. Tongwen Chen (IEEE Fellow, University of Alberta); Prof. Mark Coates (McGill University), and; Mr. Sajith Nair, Partner in PwC’s Cybersecurity & Privacy in Canada. About The Event: This is a unique opportunity for Concordia’s students/researchers, working/interested in security and signal processing, to learn more about the state-of-the-art research, get the chance to talk in person with elite and internationally well-known researchers, and to start/build the bases for future research collaborations. Register: ​Please check the School’s Homepage (below) for the call for participation (CPF), Biography of the invited speakers, and Registration details: https://users.encs.concordia.ca/~i-sip/s3pcps2016/

Thursday April 13, 2017 at 10:00 a.m. Dr. Peyman Milanfar, Leader of Computational Imaging team in Google Research, will be presenting an IEEE Signal Processing Society Distinguished Lecture, “Regularization by Denoising (RED)”. Day & Time: Thursday April 13, 2017 10:00 a.m. – 11:00 p.m. Speaker: Dr. Peyman Milanfar Leader of Computational Imaging team in Google Research Visiting Faculty at Electrical Engineering Department, UC Santa Cruz Location: University of Toronto, Bahen Center (Room BA 5281) 40 St. George Street, Toronto, ON M5S 2E4 https://goo.gl/maps/7ick2cparLF2 Contact: Mehrnaz Shokrollahi Organizers: IEEE Signal Processing Chapter Toronto Section Abstract: Image denoising is the most fundamental problem in image enhancement, and it is largely solved: It has reached impressive heights in performance and quality — almost as good as it can ever get. But interestingly, it turns out that we can solve many other problems using the image denoising “engine”. I will describe the Regularization by Denoising (RED) framework: using the denoising engine in defining the regularization of any inverse problem. The idea is to define an explicit image-adaptive regularization functional directly using a high performance denoiser. Surprisingly, the resulting regularizer is guaranteed to be convex, and the overall objective functional is explicit, clear and well-defined. With complete flexibility to choose the iterative optimization procedure for minimizing this functional, RED is capable of incorporating any image denoising algorithm as a regularizer, treat general inverse problems very effectively, and is guaranteed to converge to the globally optimal result. Biography: Peyman leads the Computational Imaging/ Image Processing team in Google Research. Prior to this, he was a Professor of Electrical Engineering at UC Santa Cruz from 1999-2014, where he is now a visiting faculty. He was Associate Dean for Research at the School of Engineering from 2010-12. From 2012-2014 he was on leave at Google-x, where he helped develop the imaging pipeline for Google Glass. Peyman received his undergraduate education in electrical engineering and mathematics from the University of California, Berkeley, and the MS and PhD degrees in electrical engineering from the Massachusetts Institute of Technology. He holds 11 US patents, several of which are commercially licensed. He founded MotionDSP in 2005. He has been keynote speaker at numerous technical conferences including Picture Coding Symposium (PCS), SIAM Imaging Sciences, SPIE, and the International Conference on Multimedia (ICME). Along with his students, he has won several best paper awards from the IEEE Signal Processing Society. He is a Fellow of the IEEE “for contributions to inverse problems and super-resolution in imaging.”

Friday, April 13th at 10:00 a.m., Schubmehl-Prein Professor Kevin W. Bowyer, will be presenting an IEEE Signal Processing Society Distinguished Lecture “Iris Matching and De-Duplication of Voter Registration Lists”. Day & Time: Friday, April 13, 2018 10:00 a.m. ‐ 11:00 a.m. Speaker: Schubmehl-Prein Professor Kevin W. Bowyer Department of Computer Science & Engineering University of Notre Dame, IN, US Location: Room BA-4287, University of Toronto http://map.utoronto.ca/building/080 Contact: Mehrnaz Shokrollahi, Yashodhan Athavale Organizer: IEEE Signal Processing Chapter Toronto Section Abstract: Fingerprint, face and iris are widely used as biometrics to verify a person’s identity. One important application of biometrics is to ensure that each person is enrolled only once on a list of eligible voters. Keeping someone from voting multiple times under different identitiesis referred to as “de-duplicating” the voting register. This talk will present results of a de-duplication trial performed for the country of Somaliland. The talk will cover how iris recognition works, what level of matching accuracy can be expected, what the matching accuracy suggests in terms of expected number of false matches and false non-matches, and some “special case” example images. (You should not need any prior experience with iris recognition tounderstand this talk.) Biography: Kevin Bowyer is the Schubmehl-Prein Family Professor of Computer Science and Engineering at the University of Notre Dame and also serves as Director of International Summer Engineering Programs. Professor Bowyer’s research interests range broadly over computer vision and pattern recognition, including biometrics and data mining. Professor Bowyer received a 2014 Technical Achievement Award from the IEEE Computer Society, with the citation “For pioneering contributions to the science and engineering of biometrics”. Professor Bowyer is a Fellow of the IEEE, “for contributions to algorithms for recognizing objects in images”; a Fellow of the IAPR, “for contributions to computer vision, pattern recognition and biometrics”; and a Golden Core Member of the IEEE Computer Society. Professor Bowyer is serving as General Chair of the 2019 IEEE Winter Conference on Applications of Computer Vision; has served as Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence and as Editor-In-Chief of the IEEE Biometrics Compendium; and is currently serving on the editorial board of IEEE Access. Professor Bowyer’s most recent book is the Handbook of Iris Recognition, edited with Dr. Mark Burge.

Thursday April 18th, 2019 at 4:00 p.m. Dr. Tao Zhang, Director of Signal Processing Research Department, will be presenting “Improving Speech Understanding in the Real-World for Hearing Devices: Solutions, Challenges and Opportunities”. Day & Time: Thursday, April 18th, 2019 4:00 p.m. – 5:00 p.m. Speaker: Dr. Tao Zhang Director of Signal Processing Research Department Starkey Hearing Technologies Organizers: IEEE Signal Processing Chapter Toronto Section Location: Room BA 1230, University of Toronto Contact: Mehrnaz Shokrollahi Yashodhan Athavale Michael Zara Abstract: The cocktail party problem has remained to be one of the most challenging problems for hearing aids even after decades of extensive research. In this talk, we will review our research on the cutting-edge single-microphone speech enhancement with emphasis on deep learning-based approaches. We will introduce and discuss our research on the multi-microphone speech enhancement with an emphasis on robust and real-time algorithms. We will present our latest research on the multimodal speech enhancement by considering brain signals (i.e. EEG) and microphone signals in a single joint-optimization framework. Finally, we will discuss the challenges and opportunities in deploying these algorithms in practice. We will present our perspectives on future research directions especially in the areas of individualizations and customizations using big data and machine learning. Biography: Tao Zhang received his B.S. degree in physics from Nanjing University, Nanjing, China in 1986, M.S. degree in electrical engineering from Peking University, Beijing, China in 1989, and Ph.D. degree in speech and hearing science from the Ohio-State University, Columbus, OH, USA in 1995. He joined the Advanced Research Department at Starkey Laboratories, Inc. as a Sr. Research Scientist in 2001, managed the DSP department from 2004 to 2008 and the Signal Processing Research Department from 2008 to 2014. Since 2014, he has been Director of the Signal Processing Research department at Starkey Hearing Technologies, a global leader in providing innovative hearing technologies. He has received many prestigious awards including Inventor of the Year Award, the Mount Rainier Best Research Team Award, the Most Valuable Idea Award, the Outstanding Technical Leadership Award and the Engineering Service Award at Starkey. He is a senior member of IEEE and the Signal Processing Society and the Engineering in Medicine and Biology Society. He serves on the IEEE AASP Technical Committee and the industrial relationship committee and the IEEE ComSoc North America Region Board, He is an IEEE SPS Distinguished Industry Speaker, the IEEE SPS Industry Convoy for the Unites States (Region 1-6) and the Chair of IEEE Twin-cities Signal Processing and CommunicationChapter. His current research interests include audio, acoustic, speech signal processing and machine learning; multimodal signal processing and machine learning for hearing enhancement, health and wellness monitoring; psychoacoustics, room and ear canal acoustics; ultra-low power real-time embedded system design and device-phone-cloud ecosystem design. He has authored and coauthored 120+ presentations and publications, received 20+ approved patents and had additional 30+ patents pending.

On Wednesday, October 7, 2020 at 2:00 p.m., Dr. Ehsan Kamalinejad will present “GPT-3 for Vision”. Day & Time: Wednesday, October 7, 2020 2:00 p.m. – 3:00 p.m. Speaker: Ehsan Kamalinejad, PhD Co-Founder & CTO at Visual One Associate Professor at Cal State East Bay University Former Senior Machine Learning Scientist at Apple San Francisco, USA Organizer: IEEE Toronto Signal Processing Chapter Location: Virtual – Click here for the Google Meets link. Contact: Mehrnaz Shokrollahi Abstract: Deep learning in computer vision (CV) has proved to be very effective in solving many problems in real world. However, while the raw number of researches done in standard CV problems (such as ImageNet object classification/detection) has exploded, the measurable progress in these fields has slowed down. Additionally, there are many real-world problems in vision that are simply not compatible with the current approaches. This demands a new wave of problem statements in CV (and a new set of benchmarks). This talk focuses on one important set of such problem statements. We propose that many real-world problems in vision are “event recognition” problems. We introduce a concrete definition for the event recognition problem. We will see that this definition of event detection prohibits large sample sets. Hence, these events need to be recognize based on very few samples. We start by reviewing the current literature and we propose some promising directions for approaching this problem. At the end we show some demos from our recent effort on wrestling with this very challenging problem. Our solution can be best described by the “vision counterpart of GPT-3 few shot learner”. Register: Please check back soon for the registration link. Biography: Ehsan Kamalinejad (EK) is a senior machine learning engineer. He is currently working on Visual One which is a YCombinator backed startup he co-founded. Before that he was working for several years at Apple and Amazon as a staff machine learning engineer. Ehsan holds a faculty position as an associate professor at Cal State East Bay University. He got his PhD from University of Toronto. He has more than 7 years of experience delivering machine learning products in computer vision and natural language processing. His current project, Visual One, is about bringing next level intelligence to surveillance cameras.

On Friday, November 20, 2020 at 2:30 p.m., Mehrdad Fatourechi will present “Machine Learning and Digital Signal Processing Applications in Online Video Platforms”. Day & Time: Friday, November 20, 2020 2:30 p.m. – 4:00 p.m. Speaker: Mehrdad Fatourechi, PhD Organizer: IEEE Signal Processing Chapter Toronto Section Location: This event will be hosted on google meets Meeting ID meet.google.com/yej-opbp-uxo Phone Numbers (US)+1 617-675-4444 PIN: 974 200 026 6220# Contact: Mehrnaz Shokrollahi Abstract: In the past 15 years, we have seen exponential growth in online video platforms such as YouTube, Instagram, Netflix, TikTok, amongst others. In this talk, we will look at some of the challenges these platforms have been facing and how machine learning and digital signal processing are playing important roles in addressing these challenges. We will focus on discussing 3 areas: 1- Content discovery and SEO optimization 2- Establishing trust and safety, and 3- Protecting the rights of the content owners We will also discuss some of the areas that are currently open for future research. Register: Registration is not required. Biography: Mehrdad is the VP of Engineering of BroadbandTV, a media-tech company that is advancing the world through the creation, distribution, management, and monetization of content. Mehrdad is currently responsible for managing the research and development (R&D) and IT departments. When he joined BBTV in March 2010, he was initially responsible for managing the research team, and then his role later expanded to lead the entire engineering department. Under his leadership, BBTV’s tech team has become one of the leading and most innovative teams in digital video space, building several internal and external products (including VISO Catalyst, VISO Collab, VISO Prism, VISO NOVI, and VISO Mine) as well as filing several patents. Mehrdad has an in-depth knowledge of digital signal processing, machine learning, and pattern recognition algorithms. He holds a PhD in Electrical Engineering from the University of British Columbia (UBC), where he was nominated for NSERC’s Doctoral Prize Award. He is an author on more than 30 journal and conference papers with a focus on pattern recognition, machine learning and intelligent algorithms. He previously held positions in the tech/education industry including roles as a research associate and sessional lecturer at UBC, as well as consulting with several companies (INETCO, BC Mining Research, and STC enterprises). He was the co-chair of the IEEE Signal Processing Chapter in Vancouver for two years.

This talk will focus on the role of digital technology in providing a more patient-centric and proactive healthcare system. Following a motivational introduction to wearables and their role in providing a connected digital healthcare system, specific requirements for signal analysis and machine learning would be expanded. Case study examples of some of the innovation projects in the areas of baby heart rate monitoring, continuous vital signs analysis and mental health applications will be mentioned as the translational aspects of the research and development done at the Signal Analysis Research Lab in Toronto Metropolitan University. Speaker(s): Dr. Sri Krishnan, Room: ENGLG24, Bldg: George Vari Engineering and Computing Centre, ENG, 245 Church St, Toronto, Ontario, Canada, M5B 1Z4

This seminar will cover topics including: - Devices for Blind People, Amputees, People with Severe Disability - Control of Appliances Through sEMG and EOG, Rehabilitation Through Serious Games - Use of Internet of Things (IoT) for Human Activity Recognition (HAR) Based on Convolutional Neural Network (CNN) - Robots for Interaction with Children with ASD and Down Syndrome - Respiratory Rate Estimation Through Deep Learning Applied to Photoplethysmogram - COVID Detection Through Recurrent Neural Networks (RNN) and Deep Learning (DL) - Several Applications with Brain-Computer Interfaces (BCIs) Based on Electroencephalography (EEG) Speaker(s): Dr. Teodiano Freire Bastos-Filho, Room: 105, Bldg: Eric Palin Hall (EPH), 87 Gerrard St E, Toronto, Ontario, Canada, M5B 2M2