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DTSTART;TZID=America/New_York:20221201T170000
DTEND;TZID=America/New_York:20221201T183000
DTSTAMP:20260417T074256
CREATED:20221129T183251Z
LAST-MODIFIED:20230402T175028Z
UID:10000590-1669914000-1669919400@www.ieeetoronto.ca
SUMMARY:Advances in Neuroscience at UFES/Brazil
DESCRIPTION:This seminar will cover topics including:\n– Devices for Blind People\, Amputees\, People with Severe Disability\n– Control of Appliances Through sEMG and EOG\, Rehabilitation Through Serious Games\n– Use of Internet of Things (IoT) for Human Activity Recognition (HAR) Based on Convolutional Neural Network (CNN)\n– Robots for Interaction with Children with ASD and Down Syndrome\n– Respiratory Rate Estimation Through Deep Learning Applied to Photoplethysmogram\n– COVID Detection Through Recurrent Neural Networks (RNN) and Deep Learning (DL)\n– Several Applications with Brain-Computer Interfaces (BCIs) Based on Electroencephalography (EEG)\nSpeaker(s): Dr. Teodiano Freire Bastos-Filho\,\nRoom: 105\, Bldg: Eric Palin Hall (EPH)\, 87 Gerrard St E\, Toronto\, Ontario\, Canada\, M5B 2M2
URL:https://www.ieeetoronto.ca/event/advances-in-neuroscience-at-ufes-brazil/
LOCATION:Room: 105\, Bldg: Eric Palin Hall (EPH)\, 87 Gerrard St E\, Toronto\, Ontario\, Canada\, M5B 2M2
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221129T180000
DTEND;TZID=America/New_York:20221129T190000
DTSTAMP:20260417T074256
CREATED:20221102T163220Z
LAST-MODIFIED:20230402T175057Z
UID:10000578-1669744800-1669748400@www.ieeetoronto.ca
SUMMARY:Digital Health - Role of Biomedical Signal Analysis
DESCRIPTION:This talk will focus on the role of digital technology in providing a more patient-centric\nand proactive healthcare system. Following a motivational introduction to wearables\nand their role in providing a connected digital healthcare system\, specific requirements\nfor signal analysis and machine learning would be expanded. Case study examples of\nsome of the innovation projects in the areas of baby heart rate monitoring\, continuous vital signs\nanalysis and mental health applications will be mentioned as the translational\naspects of the research and development done at the Signal Analysis Research Lab\nin Toronto Metropolitan University.\nSpeaker(s): Dr. Sri Krishnan\,\nRoom: ENGLG24\, Bldg: George Vari Engineering and Computing Centre\, ENG\, 245 Church St\, Toronto\, Ontario\, Canada\, M5B 1Z4
URL:https://www.ieeetoronto.ca/event/digital-health-role-of-biomedical-signal-analysis/
LOCATION:Room: ENGLG24\, Bldg: George Vari Engineering and Computing Centre\, ENG\, 245 Church St\, Toronto\, Ontario\, Canada\, M5B 1Z4
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20201120T143000
DTEND;TZID=America/Toronto:20201120T160000
DTSTAMP:20260417T074256
CREATED:20210430T023718Z
LAST-MODIFIED:20210501T001309Z
UID:10000221-1605882600-1605888000@www.ieeetoronto.ca
SUMMARY:Machine Learning and Digital Signal Processing Applications in Online Video Platforms
DESCRIPTION: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”. \nDay & Time: Friday\, November 20\, 2020\n2:30 p.m. – 4:00 p.m. \nSpeaker: Mehrdad Fatourechi\, PhD \nOrganizer: IEEE Signal Processing Chapter Toronto Section \nLocation: This event will be hosted on google meets\nMeeting ID\nmeet.google.com/yej-opbp-uxo\nPhone Numbers\n(US)+1 617-675-4444\nPIN: 974 200 026 6220# \nContact: Mehrnaz Shokrollahi \nAbstract: 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:\n1- Content discovery and SEO optimization\n2- Establishing trust and safety\, and\n3- Protecting the rights of the content owners\nWe will also discuss some of the areas that are currently open for future research. \nRegister: Registration is not required. \nBiography: 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. \nUnder 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\nrecognition 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.
URL:https://www.ieeetoronto.ca/event/machine-learning-and-digital-signal-processing-applications-in-online-video-platforms/
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20201007T140000
DTEND;TZID=America/Toronto:20201007T150000
DTSTAMP:20260417T074256
CREATED:20210430T023717Z
LAST-MODIFIED:20210501T000701Z
UID:10000211-1602079200-1602082800@www.ieeetoronto.ca
SUMMARY:GPT-3 for Vision
DESCRIPTION:On Wednesday\, October 7\, 2020 at 2:00 p.m.\, Dr. Ehsan Kamalinejad will present “GPT-3 for Vision”. \nDay & Time: Wednesday\, October 7\, 2020\n2:00 p.m. – 3:00 p.m. \nSpeaker: Ehsan Kamalinejad\, PhD\nCo-Founder & CTO at Visual One\nAssociate Professor at Cal State East Bay University\nFormer Senior Machine Learning Scientist at Apple\nSan Francisco\, USA \nOrganizer: IEEE Toronto Signal Processing Chapter \nLocation: Virtual – Click here for the Google Meets link. \nContact: Mehrnaz Shokrollahi \nAbstract: 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\nobject 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”. \nRegister: Please check back soon for the registration link. \nBiography: 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\nApple 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.
URL:https://www.ieeetoronto.ca/event/gpt-3-for-vision/
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20190606T100000
DTEND;TZID=America/Toronto:20190606T110000
DTSTAMP:20260417T074256
CREATED:20210430T023524Z
LAST-MODIFIED:20210430T231849Z
UID:10000173-1559815200-1559818800@www.ieeetoronto.ca
SUMMARY:MIMO Signalling: Knowing the Classics Can Make a Difference
DESCRIPTION:Thursday June 6th\, 2019 at 10:00 a.m. Prof. Wing-Kin (Ken) Ma\, Chinese University of Hong Kong\, will be presenting an IEEE Signal Processing Society Distinguished Lecture “MIMO Signalling: Knowing the Classics Can Make a Difference”. \nDay & Time: Thursday June 6th\, 2019\n10:00 a.m. ‐ 11:00 a.m. \nSpeaker: Prof. Wing-Kin (Ken) Ma\nChinese University of Hong Kong \nOrganizers: IEEE Signal Processing Chapter Toronto Section\nIEEE Communications Chapter Toronto Section \nLocation: Room BA-2135\, University of Toronto\nhttp://map.utoronto.ca/building/080 \nContact: Mehrnaz Shokrollahi\, Yashodhan Athavale\, Michael Zara\, \nAbstract: In this talk the speaker will share two stories of how his research was benefitted by learning from the basics. The first story concerns physical-layer multicasting\, a topic that has been dominated bybeamforming and optimization techniques. We will see how the classical concept of using channel coding to fight fast fading effects gives spark to rethink multicasting\, and how that leads to a stochastic beamforming approach that goes beyond what beamforming achieves. The second story considers one-bit massive MIMO precoding\, an emerging and challengingtopic. Current research on this topic mostly focuses on optimization\, often in a sophisticated\, if not complicated\, manner. We will see how the traditional idea of Sigma-Delta modulation for DAC of temporal signals can be transferred to the spatial case\, leading to one-bit massive MIMO precoding solutions that are simple and have quantization error well under control. \nBiography: Wing-Kin (Ken) Ma is a Professor with the Department of Electronic Engineering\, The Chinese University of Hong Kong. His research interests lie in signal processing\, optimization and communications. His mostrecent research focuses on two distinct topics\, namely\, structured matrix factorization for data science and remote sensing\, and MIMO transceiver design and optimization. Dr. Ma is active in the Signal Processing Society. He served as editors of several journals\, e.g.\,Senior Area Editor of IEEE Transactions on Signal Processing\, Lead Guest Editor of a special issue in IEEE Signal Processing Magazine\, to name a few. He is currently a member of the Signal Processing for Communications and Networking (SPCOM) Technical Committee. He received Research Excellence Award 2013– 2014 by CUHK\, the 2015 IEEE Signal Processing Magazine Best Paper Award\, the 2016 IEEE Signal Processing Letters Best Paper Award\, and the 2018 IEEE Signal Processing Best Paper Award. He is an IEEE Fellow and is currently an IEEE SPS Distinguished Lecturer.
URL:https://www.ieeetoronto.ca/event/mimo-signalling-knowing-the-classics-can-make-a-difference/
LOCATION:Room BA-2135\, University of Toronto
CATEGORIES:Communications,Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20190418T160000
DTEND;TZID=America/Toronto:20190418T170000
DTSTAMP:20260417T074256
CREATED:20210430T023522Z
LAST-MODIFIED:20210430T231408Z
UID:10000284-1555603200-1555606800@www.ieeetoronto.ca
SUMMARY:Improving Speech Understanding in the Real-World for Hearing Devices: Solutions\, Challenges and Opportunities
DESCRIPTION: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”. \nDay & Time: Thursday\, April 18th\, 2019\n4:00 p.m. – 5:00 p.m. \nSpeaker: Dr. Tao Zhang\nDirector of Signal Processing Research Department\nStarkey Hearing Technologies \nOrganizers: IEEE Signal Processing Chapter Toronto Section \nLocation: Room BA 1230\, University of Toronto \nContact: Mehrnaz Shokrollahi\nYashodhan Athavale\nMichael Zara \nAbstract: 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. \nBiography: 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. \nHe 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. \nHis 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.
URL:https://www.ieeetoronto.ca/event/improving-speech-understanding-in-the-real-world-for-hearing-devices-solutions-challenges-and-opportunities/
LOCATION:Room BA 1230\, University of Toronto
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20180413T100000
DTEND;TZID=America/Toronto:20180413T110000
DTSTAMP:20260417T074256
CREATED:20210430T014021Z
LAST-MODIFIED:20210430T222901Z
UID:10000107-1523613600-1523617200@www.ieeetoronto.ca
SUMMARY:Iris Matching and De-Duplication of Voter Registration Lists
DESCRIPTION: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”. \nDay & Time: Friday\, April 13\, 2018\n10:00 a.m. ‐ 11:00 a.m. \nSpeaker: Schubmehl-Prein Professor Kevin W. Bowyer\nDepartment of Computer Science & Engineering University of Notre Dame\, IN\, US \nLocation: Room BA-4287\, University of Toronto\nhttp://map.utoronto.ca/building/080 \nContact: Mehrnaz Shokrollahi\, Yashodhan Athavale \nOrganizer: IEEE Signal Processing Chapter Toronto Section \nAbstract: 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.) \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/iris-matching-and-de-duplication-of-voter-registration-lists/
LOCATION:Room BA-4287\, Bahen Centre for Information Technology\, University of Toronto\, M5S 2E4
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170524T090000
DTEND;TZID=America/Toronto:20170524T170000
DTSTAMP:20260417T074256
CREATED:20210430T012916Z
LAST-MODIFIED:20210430T211508Z
UID:10000056-1495616400-1495645200@www.ieeetoronto.ca
SUMMARY:Biomedical Signal and Image Analysis Workshop
DESCRIPTION:Wednesday May 24\, 2017 at 9:15 a.m. IEEE Signal Processing Chapter\, Toronto Section\, IEEE Engineering in Medicine and Biology Society\, Toronto Chapter\, and Signal Analysis Research (SAR) Lab\, Ryerson University will be presenting a series of sessions “Biomedical Signal and Image Analysis Workshop”. \nDay & Time: Wednesday May 24\, 2017\nMorning Session: 9:15 a.m. – 12:30 p.m\nAfternoon Session: 1:15 p.m. – 4:30 p.m. \nSpeakers: \nDr. Rangaraj M. Rangayyan\, ranga@ucalgary.ca\nDepartment of Electrical & Computer Engineering\nUniversity of Calgary\, AB\, Canada \nDr. Sridhar Krishnan\, krishnan@ryerson.ca\nDepartment of Electrical & Computer Engineering\nRyerson University\, ON\, Canada \nDr. April Khademi\, akhademi@ryerson.ca\nDepartment of Electrical & Computer Engineering\nRyerson University\, ON\, Canada \nDr. Karthy Umapathy\, karthi@ee.ryerson.ca\nDepartment of Electrical & Computer Engineering\nRyerson University\, ON\, Canada \nDr. Naimul Khan\, n77khan@ryerson.ca\nDepartment of Electrical & Computer Engineering\nRyerson University\, ON\, Canada \nDr. Teodiano Bastos\, teodiano@gmail.com\nDepartamento de Engenharia Elétrica\nUniversidade Federal do Espírito Santo\, Vitoria\, Brasil \nLocation: ENG 102\, George Vari Engineering and Computing Centre\n245 Church Street\nToronto\, Ontario M5B 2K3\nRyerson University\nhttps://goo.gl/maps/2qLpvJKgkYw \nContact: Mehrnaz Shokrollahi\nYashodhan Athavale \nOrganizers: Signal Analysis Research (SAR) Lab\, Ryerson University\nIEEE Signal Processing Chapter\, Toronto Section\nIEEE Engineering in Medicine and Biology Society\, Toronto Chapter \nMorning Session: \n9:15am Welcome remarks\n9:30am Talk M1: Color Image Processing with Biomedical Applications – Dr. Raj Rangayyan\, U of Calgary \n10:45am – 11:00am break \n11:00am Talk M2: Medical Image Analysis Techniques for Radiology and Pathology Images – Dr. April Khademi\, Ryerson Univ.\n11:45am Talk M3: Biomedical Signal Processing for Cardiac Arrhythmias – Dr. Karthi Umapathy\, Ryerson Univ. \nAfternoon Session: \n1:15pm Talk A1: Wearables\, IoT and Analytics for Connected Healthcare – Dr. Sri Krishnan\, Ryerson Univ.\n2:00pm Talk A2: Assistive Technologies and BCI for Rehab Applications – Dr. Teodiano Bastos\, UFES\, Brazil \n2:45pm – 3:00pm break \n3:00pm Talk A3: Interactive Machine Learning for Biomedical Signal and Image Analysis – Dr. Naimul Khan\, Ryerson Univ.\n3:45pm – 4:30pm Open think-tank discussions on challenges and opportunities facing this field in the era of big data\, AI\, and translational research – moderated by S. Krishnan \nBiographies: \nRangaraj M. Rangayyan is a Professor Emeritus of the Department of Electrical and Computer engineering (ECE) at the University of Calgary. Dr. Rangayyan received his Ph.D. in Electrical Engineering from the Indian Institute of Science in 1980. He has over 35 years as a professor at the University of Calgary and at the University of Manitoba. His research interests include digital signal and image processing\, biomedical signal and image analysis\, and computer-aided diagnosis. Dr. Rangayyan is the author of two well cited textbooks: “Biomedical Signal Analysis” (IEEE/ Wiley\, 2002\, 2015) and “Biomedical Image Analysis” (CRC\, 2005). He has published over 430 papers in journals and conferences\, and coauthored several books. He has supervised and co-supervised 17 Doctoral theses\, 27 Master theses\, and more than 50 researchers at various levels. He has been recognized with the 2013 IEEE Canada Outstanding Engineer Medal\, the IEEE Third Millennium Medal (2000)\, and elected as Fellow\, IEEE (2001); Fellow\, Engineering Institute of Canada (2002); Fellow\, American Institute for Medical and Biological Engineering (2003); Fellow\, SPIE (2003); Fellow\, Society for Imaging Informatics in Medicine (2007); Fellow\, Canadian Medical and Biological Engineering Society (2007); Fellow\, Canadian Academy of Engineering (2009); and Fellow\, Royal Society of Canada. He has lectured in more than 20 countries and has held the Visiting Professorships with more than 15 universities world-wide. He has been invited as a Distinguished Lecturer by IEEE EMBS in Toronto and as an invited lecture at the IEEE International Summer School in France. \nSridhar (Sri) Krishnan is a Professor in the Department of Electrical and Computer (ECE) Engineering and the Associate Dean of Research\, Development and External Partnerships for the Faculty of Engineering and Architectural Science (FEAS) at Ryerson University. He is also a Canada Research Chair in Biomedical Signal Analysis. Dr. Krishnan received his Ph. D. in ECE from the University of Calgary in 1999. Dr. Krishnan’s research interests include adaptive signal representations and analysis and their applications in biomedicine\, multimedia (audio)\, and biometrics. He has published over 280 papers in refereed journals and conferences\, filed 8 invention disclosures\, and has been granted one US patent. He has received over 20 awards and certificates of appreciation for his contributions in research and innovation. Dr. Krishnan has been invited to present in more than 30 international conferences and workshops. He has supervised and trained 10 Post-doc fellows\, 9 Doctoral theses\, 29 Master theses\, 9 Master projects\, 39 Research Assistants (RA)\, and 17 Visiting RAs. Dr. Krishnan is a Fellow of the Canadian Academy of Engineering. Dr. Krishnan is also the Co-Director of the Institute for Biomedical Engineering\, Science and Technology (iBEST) and an Affiliate Scientist at the Keenan Research Centre in St. Michael’s Hospital\, Toronto. \nKarthi Umapathy is an Associate Professor in the Department of Electrical and Computer Engineering (ECE) at Ryerson University. Dr. Umapathy received his Ph. D. in ECE from the University of Western Ontario in 2006. During his graduate studies he held the prestigious NSERC CGS and PGS awards. He was an inaugural Ryerson postdoctoral fellow and was also the recipient of the Heart & Stroke Richard Lewar Centre of Excellence research fellowship award. Dr. Umapathy’s research interests include biomedical signal and image analysis\, time-frequency analysis\, digital signal processing\, cardiac electrophysiology\, and magnetic resonance imaging. One of his recent projects involves studying the electrical activity on the surface of the human heart during ventricular fibrillation to reduce sudden cardiac death in North America. Dr. Umapathy brings with him a vast knowledge in Magnetic Resonance Imaging (MRI) from his works in Philips Medical Systems India. As the Area Manager and Country Specialist for Philips\, he led many successful MRI projects in India and Japan. \nApril Khademi recently jointed Ryerson University as an Assistant Professor in in the Department of Electrical and Computer (ECE). Dr. Khademi received her Ph.D. in Biomedical Engineering from the University of Toronto. Dr. Khademi’s research interests include medical image analysis techniques for radiology and pathology images\, generalized grayscale and colour image processing methodologies\, biomedical signal processing\, machine learning\, personalized medicine\, computer-aided diagnosis\, Big Data analytics\, Magnetic Resonance Imaging\, and digital pathology. Dr. Khademi was an Assistant Professor in Biomedical Engineering at University of Guelph. She was the Senior Scientist and Innovation Specialist at PathCore Inc. Dr. Khademi also brings with her the industry and healthcare experience from her works at GE Healthcare\, Toronto Rehabilitation Institute\, and Sunnybrook Health Sciences Centre. Dr. Khademi is the recipient of more than 10 awards including Governor General’s Gold Medal for her Masters thesis and the prestigious NSERC-CGSD3. She has over 40 publications\, and has been invited to speaker in more than 25 conferences\, seminars and workshops. \nNaimul Khan recently jointed Ryerson University as an Assistant Professor in the Department of Electrical and Computer Engineering (ECE). Dr. Khan received his Ph. D. in ECE from Ryerson University in 2014. Dr. Khan’s research interests include designing interactive methods for visual computing that can bridge the gap between end-users and systems. He has contributed to the fields of machine learning\, computer vision\, and medical imaging. Dr. Khan was previously a research engineer at Sunnybrook Research institute\, and an R&D Manager at AWE Company Ltd. At AWE\, he led the Fort York Time Tablet project in partnership with the City of Toronto to create an augmented reality exhibit of the history of the Fort. The project has garnered significant media and public attention. Dr. Khan was the recipient of several awards including the OCE TalentEdge Postdoctoral Fellowship\, the Ontario Graduate Scholarship\, and Queen Elizabeth II Graduate Scholarship in Science & Technology. \nTeodiano Bastos is a Full Professor in the Department of Electrical Engineering at Universidade Federal do Espírito Santo and a Level 1 Researcher at CNPq. Dr. Bastos received his Ph. D. in Electrical and Electronic Engineering from the Universidad Complutense de Madrid\, Spain\, in 1994. Dr. Bastos’ research interests are in Electronic Measurement and Control Systems\, including sensors\, control\, mobile robots\, industrial robotics\, rehabilitation robotics\, assistive technology\, and biological signal processing. Dr. Bastos has over 500 publications in journals\, conferences\, and books
URL:https://www.ieeetoronto.ca/event/biomedical-signal-and-image-analysis-workshop/
LOCATION:ENG 102\, George Vari Engineering and Computing Centre\, 245 Church Street\, Toronto
CATEGORIES:Engineering in Medicine and Biology,Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170413T100000
DTEND;TZID=America/Toronto:20170413T110000
DTSTAMP:20260417T074256
CREATED:20210430T012914Z
LAST-MODIFIED:20210430T210852Z
UID:10000123-1492077600-1492081200@www.ieeetoronto.ca
SUMMARY:Regularization by Denoising (RED)
DESCRIPTION: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)”. \nDay & Time: Thursday April 13\, 2017\n10:00 a.m. – 11:00 p.m. \nSpeaker: Dr. Peyman Milanfar\nLeader of Computational Imaging team in Google Research\nVisiting Faculty at Electrical Engineering Department\, UC Santa Cruz \nLocation: University of Toronto\, Bahen Center (Room BA 5281)\n40 St. George Street\, Toronto\, ON M5S 2E4\nhttps://goo.gl/maps/7ick2cparLF2 \nContact: Mehrnaz Shokrollahi \nOrganizers: IEEE Signal Processing Chapter Toronto Section \nAbstract: 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. \nBiography: 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.”
URL:https://www.ieeetoronto.ca/event/regularization-by-denoising-red/
LOCATION:University of Toronto\, Bahen Center (Room BA 5281)
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20161102
DTEND;VALUE=DATE:20161105
DTSTAMP:20260417T074256
CREATED:20210430T002607Z
LAST-MODIFIED:20210430T010138Z
UID:10000081-1478044800-1478303999@www.ieeetoronto.ca
SUMMARY:IEEE Signal Processing Society (SPS) Winter School on Distributed Signal Processing for Secure Cyber Physical Systems
DESCRIPTION:November 2-4\, IEEE Signal Processing Society (SPS) is hosting a Winter School on Distributed Signal Processing for Secure Cyber Physical Systems at Concordia. \nSpeakers: 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:\nProf. Ali Sayed (UCLA\, President-Elect of IEEE SPS);\nProf. Georgios Giannakis (IEEE Fellow\, University of Minnesota);\nProf. Pramod Varshney (IEEE Fellow\, Syracuse University);\nProf. Deepa Kundur (IEEE Fellow\, University of Toronto);\nProf. Anna Scaglione (IEEE Fellow\, Arizona State University);\nProf. Tongwen Chen (IEEE Fellow\, University of Alberta);\nProf. Mark Coates (McGill University)\, and;\nMr. Sajith Nair\, Partner in PwC’s Cybersecurity & Privacy in Canada. \nAbout 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. \nRegister: ​Please check the School’s Homepage (below) for the call for participation (CPF)\, Biography of the invited speakers\, and Registration details:\nhttps://users.encs.concordia.ca/~i-sip/s3pcps2016/
URL:https://www.ieeetoronto.ca/event/ieee-signal-processing-society-sps-winter-school-on-distributed-signal-processing-for-secure-cyber-physical-systems/
LOCATION:Concordia
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20161027T130000
DTEND;TZID=America/Toronto:20161027T140000
DTSTAMP:20260417T074256
CREATED:20210430T002606Z
LAST-MODIFIED:20210430T010335Z
UID:10000078-1477573200-1477576800@www.ieeetoronto.ca
SUMMARY:Perspectives of Automatic Speech Recognition (ASR) Technology
DESCRIPTION: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”. \nSpeaker: Prof. Sadaoki Furui\nIEEE Fellow\nPresident of Toyota Technological Institute at Chicago \nDay & Time: Thursday\, October 27\, 2016\n12:00 p.m. – 1:00 p.m. \nLocation: TRS 1129\nRyerson University\nToronto \nAbstract: 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/perspectives-of-automatic-speech-recognition-asr-technology/
LOCATION:TRS 1129\, Ryerson University\, Toronto
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20160811T130000
DTEND;TZID=America/Toronto:20160811T140000
DTSTAMP:20260417T074256
CREATED:20210430T002604Z
LAST-MODIFIED:20210430T002831Z
UID:10000001-1470920400-1470924000@www.ieeetoronto.ca
SUMMARY: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)
DESCRIPTION: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)”. \nSpeaker: Dr. Faycal Saffih\nAssistant Professor\, Department of Electrical Engineering\nUAE University \nDay & Time: Thursday\, August 11\, 2016\n1:00 p.m. – 2:00 p.m. \nLocation: Room ENGLG 05\nGeorge Vari Engineering Building\nDepartment of Electrical and Computer Engineering\nRyerson University \nContact: Dimitri Androutsos \nAbstract: 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/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/
LOCATION:Room ENGLG 05 George Vari Engineering Building
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20160307T150000
DTEND;TZID=America/Toronto:20160307T160000
DTSTAMP:20260417T074256
CREATED:20210429T230400Z
LAST-MODIFIED:20210430T000646Z
UID:10000029-1457362800-1457366400@www.ieeetoronto.ca
SUMMARY:Convexity\, Sparsity\, Nullity and all that… in Data Analysis
DESCRIPTION:Monday March 7th\, 2016 at 3:00 p.m. Prof. Hamid Krim\, Department of Electrical & Computer Engineering of North Carolina State University\, will be presenting a distinguished lecture\, “Convexity\, Sparsity\, Nullity and all that… in Data Analysis”. \nSpeaker: Prof. Hamid Krim\nDepartment of Electrical & Computer Engineering\nNorth Carolina State University\nRaleigh\, NC\, US \nDay & Time: Monday\, March 7th\, 2016\n3:00 p.m. – 4:00 p.m. \nLocation: Room VIC300\, Ryerson University\n285 Victoria St\, Toronto\nMap: https://goo.gl/maps/EAvPDLGSqrt \nContact: Mehrnaz Shokrollahi \nAbstract: High dimensional data exhibit distinct properties compared to its low dimensional counterpart; this causes a common performance decrease and a formidable computational cost increase of traditional approaches. Novel methodologies are therefore needed to characterize data in high dimensional spaces. Considering the parsimonious degrees of freedom of high dimensional data compared to its dimensionality\, we study the union-of-subspaces (UoS) model\, as a generalization of the linear subspace model. The UoS model preserves the simplicity of the linear subspace model\, and enjoys the additional stability to address nonlinear data. We show a sufficient condition to use l1 minimization to reveal the underlying UoS structure\, and further propose a bi-sparsity model (R0Sure) as an effective algorithm\, to recover the given data characterized by the UoS model from errors/corruptions. As an interesting twist on the related problem of Dictionary Learning Problem\, we discuss the sparse null space problem (SNS). Based on linear equality constraint\, it first appeared in 1986 and has since inspired results\, such as sparse basis pursuit\, we investigate its relation to the analysis dictionary learning problem\, and show that the SNS problem plays a central role\, and may naturally be exploited to solve dictionary learning problems. Substantiating examples are provided\, and the application and performance of these approaches are demonstrated on a wide range of problems\, such as face clustering and video segmentation. \nBiography: Hamid Krim received his BSc.\, MSc. and PhD. in Electrical Engineering. He was a member of Technical staff at AT&T Bell Labs\, where he has conducted R&D in the areas of telephony and digital communication systems/subsystems. Following an NSF post-doctoral fellowship at Foreign Centers of Excellence\, LSS/University of Orsay\, Paris\, France. He later joined the Laboratory for Information and Decision Systems\, MIT\, Cambridge\, MA as a Research Scientist\, where he was performing and supervising research. He is presently Professor of Electrical Engineering in the ECE department\, North Carolina State University\, Raleigh\, leading the Vision\, Information and Statistical Signal Theories and Applications group. His research interests are in statistical signal and image analysis\, and mathematical modelling\, with a keen emphasis on applied problems in classification and recognition using geometric and topological tools. He is currently serving on the IEEE editorial board of SP\, and the TCs of SPTM and Big Data Initiative\, as well as an AE of the new IEEE Transactions on SP on Information Processing on Networks\, and of the IEEE SP Magazine. He is also one of the 2015-2016 Distinguished Lecturers of the IEEE SP Society.
URL:https://www.ieeetoronto.ca/event/convexity-sparsity-nullity-and-all-that-in-data-analysis/
LOCATION:Room VIC300\, Ryerson University\, 285 Victoria St\, Toronto
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20151123T140000
DTEND;TZID=America/Toronto:20151123T150000
DTSTAMP:20260417T074256
CREATED:20210429T230357Z
LAST-MODIFIED:20210429T234113Z
UID:10000043-1448287200-1448290800@www.ieeetoronto.ca
SUMMARY:Intelligent Medical Devices for Affordable Healthcare
DESCRIPTION:Monday November 23\, 2015 at 2:00 p.m. Professor Dinesh Kumar\, RMIT University of Melbourne\, Australia will be presenting “Intelligent Medical Devices for Affordable Healthcare”. \nSpeaker: Professor Dinesh Kumar\nRMIT University\nMelbourne\, Australia \nDay & Time: Monday\, November 23\, 2015\n2:00 p.m. – 3:00 p.m. \nLocation: Room ENG 106\nGeorge Vari Engineering and Computing Centre\nRyerson University\n245 Church Street\nToronto \nOrganizer: IEEE Toronto Signal Processing Chapter \nContact: Sri Kirshnan\, Email:krishnan@ryerson.ca \nAbstract: Technology is giving us longer and healthier lives. However\, this comes at the cost\, both\, in terms of the research\, infrastructure\, and the cost of running the devices. Often\, this makes many of these technologies only suitable for the wealthy societies.\nProf Kumar will share his vision for devices and technologies for affordable healthcare. He will count the real cost of the devices\, and suggest methods for making these more affordable without compromising the efficacy in improving the health outcomes. While automatic devices are often considered the demand of the wealthy\, Kumar will show that these intelligent devices are the necessity for remote communities. \nBiography: Dr. Dinesh Kumar is a Professor of Electrical and Computer Engineering at RMIT University in Melbourne\, Australia. Dr. Kumar did his B.E (Hons) and PhD in Biomedical Engineering from Indian Institute of Technology (IIT)\, Chennai and Delhi and has been researching in the field of developing affordable medical devices for 20 years. Dr. Kumar has been working towards developing intelligent devices and techniques that facilitate the user for early detection of disease\, perform risk assessment of disease and provide assistive technologies for people who are frail or disabled. He has published over 350 refereed publications and his work has been cited over 5000 times.
URL:https://www.ieeetoronto.ca/event/intelligent-medical-devices-for-affordable-healthcare/
LOCATION:Room ENG106\, Ryerson University
CATEGORIES:Signal Processing
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20150917T120000
DTEND;TZID=America/New_York:20150917T130000
DTSTAMP:20260417T074256
CREATED:20210430T025801Z
LAST-MODIFIED:20210430T025801Z
UID:10000376-1442491200-1442494800@www.ieeetoronto.ca
SUMMARY:Exploring Power Network Signatures for Information Forensics
DESCRIPTION:September 17\, 2015 at 12:00 p.m. Dr. Min Wu\, Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland\, College Park will be presenting “Exploring Power Network Signatures for Information Forensics”. \nSpeaker: Dr. Min Wu\nIEEE Fellow\nProfessor of Electrical and Computer Engineering\nUniversity of Maryland\, College Park \nDay & Time: Thursday\, September 17\, 2015\n12:00 p.m. – 1:00 p.m. \nLocation: Room ENG105\, Ryerson University\n245 Church Street\, Toronto\, Ontario M5B 2K3\nClick here to see the Map – Look for ENG \nOrganizer: IEEE Signal Processing Society\nElectrical and Computer Engineering Graduate Program\nCASPAL Ryerson \nContact: Prof. Xiao-Ping Zhang\nCASPAL (Communications and Signal Processing Applications Lab.)\nDepartment of Electrical and Computer Engineering\,\nRyerson University \nAbstract: Osama bin Laden’s video propaganda prompted numerous information forensic questions: given a video under question\, when and where was it shot? Was the sound track captured together at the same time/location as the visual\, or superimposed later? Similar questions about the time\, location\, and integrity of multimedia and other sensor recordings are important to provide evidence and trust in crime solving\, journalism\, infrastructure monitoring\, smart grid management\, and other informational operations. \nAn emerging line of research toward addressing these questions exploits novel signatures induced by the power network. An example is the small random-like fluctuations of the electricity frequency known as the Electric Network Frequency (ENF)\, owing to the dynamic control process to match the electricity supplies with the demands in the grid. These environmental signatures reflect the attributes and conditions of the power grid and become naturally “embedded” into various types of sensing signals. They carry time and location information and may facilitate integrity verification of the primary sensing data. This talk will provide an overview of recent information forensics research on ENF carried out by our Media and Security Team (MAST) at University of Maryland\, and discuss some on-going and open research issues in and beyond security applications. \nBiography:Min Wu is a Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland\, College Park. She received her Ph.D. degree in electrical engineering from Princeton University in 2001. At UMD\, she leads the Media and Security Team (MAST)\, with main research interests on information security and forensics and multimedia signal processing. Her research and education have been recognized by a NSF CAREER award\, a TR100 Young Innovator Award from the MIT Technology Review Magazine\, an ONR Young Investigator Award\, a Computer World “40 Under 40” IT Innovator Award\, a University of Maryland Invention of the Year Award\, an IEEE Mac Van Valkenburg Early Career Early Career Teaching Award\, and several paper awards from IEEE SPS\, ACM\, and EURASIP. She was elected IEEE Fellow for contributions to multimedia security and forensics. Dr. Wu chaired the IEEE Technical Committee on Information Forensics and Security (2012-2013)\, and has served as Vice President – Finance of the IEEE Signal Processing Society (2010-2012) and Founding Chief Editor of the IEEE SigPort initiative (2013-2014). Currently\, she is serving as Editor-in-Chief (2015-2017) of the IEEE Signal Processing Magazine and an IEEE Distinguished Lecturer.
URL:https://www.ieeetoronto.ca/event/exploring-power-network-signatures-for-information-forensics/
LOCATION:245 Church St\, Toronto\, ON M5B 1Z4\, Canada
CATEGORIES:Signal Processing
END:VEVENT
END:VCALENDAR