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IEEE Toronto Centennial Workshop: Distributed Machine Learning, The Second Step

Monday, July 15th, 2019

Tuesday July 30th, 2019 at 2:30 p.m. Reza Dibaj, Chair of Magnetics Chapter in the IEEE Toronto Section, will be presenting “IEEE Toronto Centennial Workshop: Distributed Machine Learning, The Second Step”.

Day & Time: Tuesday July 30th, 2019
2:30 p.m. ‐ 3:30 p.m.

Speaker: Reza Dibaj
Chair of Magnetics Chapter, IEEE Toronto Section

Organizers: Magnetics Chapter, IEEE Toronto Centennial College Chapter, WIE IEEE Toronto

Location: Room B3-09
Centennial College, Progress Campus
941 Progress Ave., Toronto, Ontario, M1G 3T8

Contact: Reza Dibaj

Abstract: At the beginning of the workshop, we quickly recap what we did in the previous session and knowing more about the definition of good features in our datasets. To continue our journey that we have started in the previous workshop, we will dive deeper into ML by applying our Decision Tree algorithm on a classical iris dataset. We will thoroughly practice training and testing data, using a step-by-step hands-on. Finally, we will use visualization tools to show what is happening under the hood in a decision tree and how it works as one of the most interpretable algorithms in ML.

IEEE Toronto Centennial Workshop: Distributed Machine Learning, Basic Concepts

Monday, July 15th, 2019

Tuesday July 23rd, 2019 at 2:30 p.m. Reza Dibaj, Chair of Magnetics Chapter in the IEEE Toronto Section, will be presenting “IEEE Toronto Centennial Workshop: Distributed Machine Learning, Basic Concepts”.

Day & Time: Tuesday July 23rd, 2019
2:30 p.m. ‐ 3:30 p.m.

Speaker: Reza Dibaj
Chair of Magnetics Chapter, IEEE Toronto Section

Organizers: Magnetics Chapter, IEEE Toronto Centennial College Chapter, WIE IEEE Toronto

Location: Room B3-09
Centennial College, Progress Campus
941 Progress Ave., Toronto, Ontario, M1G 3T8

Contact: Reza Dibaj

Abstract: Machine Learning is an indispensable part of data science and there is no need to have a thorough programming background to benefit from it. Machine Learning (ML) and statistical techniques have provided a new era that enables us to convert the data to information, and transform the information into actionable knowledge. SciKit and TensorFlow are two states of the art libraries that can be used in Python and this seminar will open the gate to know their bases. The first seminar is about “Hello World!” Machine Learning program, using python language and SciKit learn library.

Data Mining and Machine Learning with Application to Medical Data

Monday, July 1st, 2019

Wednesday July 17th, 2019 at 12:15 p.m. Prof. Steven Wang, Professor in Statistics at York University, will be presenting “Data Mining and Machine Learning with Application to Medical Data”.

Day & Time: Wednesday July 17th, 2019
12:15 p.m. ‐ 1:15 p.m.

Speaker: Prof. Steven Wang
Professor in Statistics
Department of Mathematics and Statistics York University

Organizers: IEEE Toronto Robotics, IEEE Toronto WIE, EMB, UHN

Location: TRI-UC, Basement Lecture theatre
550 University Ave., Toronto, M5G 2A2

GoToMeeting: https://global.gotomeeting.com/join/435099981

Contact: Prof. Azadeh Yadollahi

Abstract: In this talk, we will discuss some applications of data mining and machine learning to medical data. We will discuss a variety of topics: genetic analysis, signal processing method for ECG and EEG, personalized medicine, autoimmune disease and human microbiome analysis. We will also share our experience on data including issues related to data cleaning and missing values.

Biography: Dr. Steven Wang is a professor in Statistics at the Department of Mathematics and Statistics. He received his Ph.D. in Statistics from the University of British Columbia in 2001 and did one year Postdoc on Data Mining at the Pacific Institute of Mathematical Sciences. He joined York University in 2002 and currently a full professor in Statistics. His research included statistical theory, data mining, optimization and machine learning. With his co-inventors, he has applied a Canadian and US patent for deep learning method. In the past 10 years, his research is focused on machine learning and medical data.

Poster: See Poster

MIMO Signalling: Knowing the Classics Can Make a Difference

Monday, June 3rd, 2019

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”.

Day & Time: Thursday June 6th, 2019
10:00 a.m. ‐ 11:00 a.m.

Speaker: Prof. Wing-Kin (Ken) Ma
Chinese University of Hong Kong

Organizers: IEEE Signal Processing Chapter Toronto Section
IEEE Communications Chapter Toronto Section

Location: Room BA-2135, University of Toronto
http://map.utoronto.ca/building/080

Contact: Mehrnaz Shokrollahi, Yashodhan Athavale, Michael Zara,

Abstract: 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.

Biography: 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.

The WOW Dinner Toronto 2019

Monday, May 20th, 2019

The WOW Dinner is the world’s leading #womenintech dinner promoting diversity & inclusion in tech and related industries. The inspirational global series of networking events coming to Toronto on May 21, 2019 at the Gladstone Hotel, and coincides with the Collision Conference held just blocks away. The event welcomes people of all genders and orientation. Expect an evening of great conversation with a vibrant group and an eclectic mix of innovators, amazing food and inspiring speakers!

Day & Time: Tuesday May 21st, 2019
5:30 p.m. ‐ 8:00 p.m.

Organizers: Newhaus Communications, Green Capulet, IEEE Toronto WIE

Price: All IEEE members can get tickets for $50.

Register: https://www.eventbrite.ca/e/the-wow-dinner-toronto-for-women-in-tech-tickets-59334500087

Location: The Gladstone Hotel

ComSoc Industry Visit: Siemens RUGGEDCOM

Saturday, May 11th, 2019

IEEE Toronto ComSoc Chapter in partnership with IEEE Toronto Industrial Relations are inviting all interested to a unique opportunity to visit Siemens RUGGEDCOM Facility at 300 Applewood Crescent, Concord, ON L4K 4E5.

RUGGEDCOM is a Canadian based company that is a subsidiary of Siemens. RUGGEDCOM networking products are designed to meet, even surpass internationally recognized industry standards for fast, reliable, standardized communications in numerous mission-critical industrial applications around the world.

During the visit we will get to take a factory tour, meet with an R&D engineer and tour the R&D lab. RUGGEDCOM will host us for a lunch afterwards.

Day & Time: Thursday May 23rd, 2019
10:00 a.m. ‐ 1:00 p.m.

Organizers: IEEE Toronto ComSoc

Transportation: The location is not far from Vaughan Metropolitan Centre (TTC). registered individuals are welcome to coordinate their own transportation to the location.

Meeting point: We plan to meet inside the building entrance at 10am.

Register: https://events.vtools.ieee.org/m/198904

Location: Siemens Ruggedcom, 300 Applewood Crescent
Concord, Ontario, Canada L4K 4E5

Contact: Toronto_Chapter@comsoc.org

Assessment of Egocentric Spatial Orientation using Virtual Reality for Diagnosis and Monitoring Alzheimer’s Disease

Saturday, May 11th, 2019

Friday May 24th, 2019 at 12:15 p.m. Prof. Zahra Moussavi, Director of the Biomedical Engineering Program at University of Manitoba and Canada Research Chair, will be presenting “Assessment of Egocentric Spatial Orientation using Virtual Reality for Diagnosis and Monitoring Alzheimer’s Disease”.

Day & Time: Friday May 24th, 2019
12:15 p.m. ‐ 1:15 p.m.

Speaker: Zahra Moussavi
Director of the Biomedical Engineering Program
Professor & Canada Research Chair
Department of Electrical & Computer Engineering
University of Manitoba

Organizers: IEEE Toronto WIE, IEEE Toronto, IEEE Toronto Engineering in Medicine and Biology Chapter, UHN

Location: TRI-UC, Basement Lecture Theatre
550 University Ave., Toronto, M5G 2A2

GoToMeeting: https://global.gotomeeting.com/join/543203653

Contact: Dr. Maryam Davoudpour

Abstract: Memory and cognitive declines are associated with normal brain aging but are also precursors to dementia, in particular the so called the pandemic of the century, Alzheimer’s disease. While currently there is no cure or “vaccine” against dementia, based on brain’s plasticity, there are hopes to delay the onset or to slow the progression of disease.

Alzheimer’s disease is multi-facet condition; thus, the key to its management is in multi- disciplinary approaches. The clinical diagnosis of neurodegenerative disorders, in general, is based on an extensive evaluation of cognition and behavioral performance along with functional status, which provides a variable grade of accuracy especially at early stages of the disease. In this talk, I will review diagnostic objective methods that can assist Alzheimer’s diagnosis. In particular, I will elaborate on the application and research outcomes of virtual reality egocentric spatial assessment for and its potentials for a differential diagnosis of Alzheimer’s versus other types of dementia.

Biography: Zahra Moussavi is a professor, a Canada Research Chair, and the founder and director of Biomedical Engineering Graduate Program at University of Manitoba. Her current research focuses are on medical devices instrumentation and signal analysis for sleep apnea management and Alzheimer’s diagnosis and treatment using virtual reality, rTMS and EVestG technologies. She is the recipient of several awards including the “Canada’s Most Powerful Women (Top 100)” and “Manitoba Distinguished Women” in 2014 and IEEE EMBS Distinguished Lecturer, 2014 and 2019. She has published more than 259 peer-reviewed papers in journals and conferences, and has given 94 invited talks/seminars including 2 Tedx Talks and 9 keynote speaker seminars at national and international conferences. Aside from academic work, on her spare time, she writes science articles for public; also has developed and offered memory fitness programs for aging population.

Poster Link: Click here

Cyber Attacks on Internet of Things Sensor Systems for Inference

Friday, April 12th, 2019

Tuesday May 7th, 2019 at 4:00 p.m. Professor Rick S. Blum, will be presenting “Cyber Attacks on Internet of Things Sensor Systems for Inference”.

Day & Time: Tuesday, May 7th, 2019
4:00 p.m. – 5:00 p.m.

Speaker: Professor Rick S. Blum
Robert W. Wieseman Endowed Professor of Electrical Engineering
Electrical and Computer Engineering Dept., Lehigh University

Organizers: IEEE Signal Processing Chapter Toronto Section

Location: Room SLC-508, Ryerson University

Contact: Mehrnaz Shokrollahi
Yashodhan Athavale
Michael Zara

Abstract: The Internet of Things (IoT) improves pervasive sensing and control capabilities via the aid of modern digital communication, signal processing and massive deployment of sensors. The employment of low cost and spatially distributed IoT sensor nodes with limited hardware and battery power, along with the low required latency to avoid unstable control loops, presents severe security challenges. Attackers can modify the data entering or communicated from the IoT sensors which can have serious impact on any algorithm using this data for inference. In this talk we describe how to provide tight bounds (with sufficient data) on the performance of the best algorithms trying to estimate a parameter from the attacked data and communications under any assumed statistical model describing how the sensor data depends on the parameter before attack. The results hold regardless of the estimation algorithm adopted which could employ deep learning, machine learning, statistical signal processing or any other approach. Example algorithms that achieve performance close to these bounds are illustrated. Attacks that make the attacked data useless for reducing these bounds are also described. These attacks provide a guaranteed attack performance in terms of the bounds regardless of the algorithms the estimation system employs. References are supplied which provide various extensions to all the specific results presented and a brief discussion of applications to IEEE 1588 for clock synchronization is provided.

Biography: Rick S. Blum received a B.S.E.E from Penn State in 1984 and an M.S./Ph.D in EE from the University of Pennsylvania in 1987/1991. From 1984 to 1991 he was with GE Aerospace. Since 1991, he has been at Lehigh University. His research interests include signal processing for cyber security, smart grid, communications, sensor networking, radar and sensor processing. He was an AE for IEEE Trans. on Signal Processing and for IEEE Communications Letters. He has edited special issues for IEEE Trans. on Signal Processing, IEEE Journal of Selected Topics in Signal Processing and IEEE Journal on Selected Areas in Communications. He was a member of the SAM Technical Committee (TC) of the IEEE Signal Processing Society. He was a member of the Signal Processing for Communications TC of the IEEE Signal Processing Society and is a member of the Communications Theory TC of the IEEE Communication Society. He was on the awards Committee of the IEEE Communication Society. Dr. Blum is a Fellow of the IEEE, an IEEE Signal Processing Society Distinguished Lecturer (twice), an IEEE Third Millennium Medal winner, a member of Eta Kappa Nu and Sigma Xi, and holds several patents. He was awarded an ONR Young Investigator Award and an NSFResearch Initiation Award.

Improving Speech Understanding in the Real-World for Hearing Devices: Solutions, Challenges and Opportunities

Friday, April 12th, 2019

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.

Diversity and Inclusion in Computing

Saturday, March 30th, 2019

Monday April 1st, 2019 at 7:30 p.m. Aislin O’Hara of O’Hara & Associates Consulting, will be presenting “Diversity and Inclusion in Computing”.

Day & Time: Monday April 1st, 2019
7:30 p.m. ‐ 9:00 p.m.

Speaker: Aislin O’Hara of O’Hara & Associates Consulting

Organizers: IEEE Toronto Systems Chapter

Location: Room ENG 103, Ryerson University
George Vari Engineering and Computing Centre
245 Church Street,
Toronto, Ontario
Canada M5B 2K3

Contact: Mehrdad Tirandazian

Register: https://events.vtools.ieee.org/m/196718

Abstract: Josh Bersin, world class industry analyst & founder of Bersin by Deloitte cited Diversity & Inclusion as one of the hottest topics for 2019 technology companies. Through this presentation, we will uncover what diversity & inclusion really means, why it matters and what strategies can be used to foster inclusion. We will explore some case studies showing best practices and listen to a testimonial from a person living with a disability. Students will learn the ways diversity and
inclusion is protected under the Ontario Human Rights Code, participate in a rich discussion on common misconceptions and leave with a deepened understanding on how the technology sector can leverage diversity to become a more successful industry as a whole.

Biography: Aislin is a Certified Professional Consultant on Aging with 12 years of experience executing accessible & inclusive customer experience solutions within the public sector. Presently, Aislin is the Principal Consultant and founder of O’Hara & Associates Consulting, which helps businesses prepare for the demographic shift in our population by providing age-friendly strategies and advice on designing accessible & inclusive solutions. Most recently, Aislin was the Project Lead – Customer Experience for TTC Wheel-Trans, the 3rd largest specialized transit agency in North America, whose customer base is vastly comprised of seniors and persons with disabilities. Aislin was responsible for designing and implementing various accessible, diverse & inclusive customer facing initiatives for the Wheel-Trans Transformation Program. Aislin’s work on accessible customer service design was recently published in the Journal of the Transportation Research Board and was recently presented at the January 2019 Washington conference.