IEEE Toronto Section


Archive for the ‘Events’ Category

A framework for general purpose digital pathology image analysis, using machine learning methods to identify cancer subsets and immunotherapy biomarkers

Friday, July 7th, 2017

Monday July 17, 2017 at 4:00 p.m. Dr. Trevor McKee, STTARR Innovation Research Centre for Cancer Research, will be presenting “A framework for general purpose digital pathology image analysis, using machine learning methods to identify cancer subsets and immunotherapy biomarkers”.

Day & Time: Monday July 17, 2017
4:00 p.m. – 5:00 p.m.

Speaker: Dr. Trevor McKee
STTARR – Innovation Research Centre for Cancer Research
Toronto, Ontario, Canada

Location: Room ENG101
George Vari Engineering Building (intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizers: Signals & Computational Intelligence Chapter

Abstract: Histological staining, interpreted by a pathologist, has remained the gold standard for cancer diagnosis and staging for over 100 years. There is a growing need for better – and more personalized – cancer treatments, to provide oncologists with the tools they need to best treat their patients. The advent of “molecular medicine”, or targeted therapeutic strategies that rely on knowledge of particular mutations in a cancer in order to tailor treatment, has improved cancer therapy for many patients. This has led to the use of companion diagnostics, in which tumor biopsies are stained for a specific marker or set of markers, using immunohistochemical approaches. The information obtained from the degree of staining or spatial arrangement of stained cells within the tumor helps to identify tumor molecular subclasses that may benefit from such tailored therapeutic approaches.

The increase in the number of slides being stained for specific markers and used in diagnosis, along with the increased need for quantitative assessment of the degree of staining, number of cells, or spatial arrangement of cells within the tumor, has increased the volume and type of work that pathologists encounter in their diagnostic workflow. Our team works on the development of tools for quantitative digital pathology analysis that can benefit pathologists, by building and validating semi-automated algorithms for cellular quantification and intensity scoring of stained slides. We use machine learning methods to learn features that distinguish different morphological regions from pathologist annotations. These are then fed into a tissue segmentation and classification framework to break the tissue down into its components, either on the individual cell level, or the glandular level. Staining intensity is quantified following colour deconvolution of the individual stain components, and reporting metrics are designed, in close collaboration with pathologists and biological scientists, to identify the appropriate outputs for comparing between treatment groups or different cancer types.

The use of multiplexed digital pathology stains allows us to build a generalized analytical framework to perform “tissue cytometry”. This new technology can extract quantitative image-derived features in a reproducible and robust fashion, providing clinicians and biological scientists with tools to measure previously inaccessible phenomena, like measuring the hypoxic gradient directly within tumor sections, or comparing glucose uptake to lactic acid production in the same tumor sample. This approach establish the foundation for a bridge between traditional morphometric assessment of tumor biopsies, and the detailed spatially resolved chemical and molecular content maps of each tumor, providing an invaluable toolkit for the discovery of cancer molecular subtypes, and development of therapeutic interventions.

Biography: Dr. Trevor McKee received his Ph.D. in Biological Engineering from the Massachusetts Institute of Technology in 2005, in the laboratory of Dr. Rakesh Jain of Harvard Medical School. During his graduate work, he pioneered the application of new imaging and analysis technologies to studying drug transport within tumors, and on developing methods to improve drug delivery. He also holds a Bachelors of Science in Chemical Engineering with a Biotechnology minor from the University at Buffalo. He moved to Toronto to continue postdoctoral work at the Ontario Cancer Institute, applying multi-modality imaging and quantitative image analysis methods to study preclinical cancer models. He has a successful track record of high-impact publications with a number of clinical and basic science collaborators, and has also collaborated with pharmaceutical companies on imaging-based preclinical testing of new compounds. He is currently Image Analysis Core Manager of the STTARR Innovation Centre, and manages a team of analysts to develop new algorithms for machine-learning powered image segmentation and quantification across a number of disease sites. His research interests lie in studying the tumor microenvironment, drug and oxygen delivery, and the development of tools for “tissue cytometry” – deriving complex biological and spatial relationships from tissue sections via computational image analysis methods.

Large-Scale Analytics and Machine Learning for Biomedical Data Types

Wednesday, June 21st, 2017

Wednesday June 28, 2017 at 5:00 p.m. Dr. Shiva Amiri, CEO of BioSymetrics Inc, will be presenting “Large-Scale Analytics and Machine Learning for Biomedical Data Types”.

Day & Time: Wednesday June 28, 2017
5:00 p.m. – 6:00 p.m.

Speaker: Dr. Shiva Amiri
CEO of BioSymetrics Inc
Toronto, Ontario, Canada

Location: Room ENG288
Department of Computer Science
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizers: Signals & Computational Intelligence Chapter, WIE

Abstract: The scale of data being generated in medicine and research can easily overwhelm typical analytic capabilities. This is particularly true with MRI/fMRI scanning, genomics data, streaming/wearables data in addition to other clinical data types, especially if in combination.

Challenges include 1) large file sizes often in heterogeneous formats 2) currently no standard Protocol exists for extraction of standardized characteristics, and 3) traditional methods for group-wise comparison can often result in spurious findings.

The talk will address these challenges by discussing customized processing pipelines built for multiple data types in biomedicine, which enable effective machine learning and other types of analytics on these datasets. This approach leverages the rapid model building capabilities of our real-time machine learning software to iterate through normalization parameters for each data type and disease class. In addition, this platform allows easy integration between the various medical data types (genome sequence, phenotypic, and metabolic data) allowing generation of more comprehensive disease classification models.

The ability to standardize and pre-process multiple types of biomedical data for machine learning, no matter the source and type, and effectively combine it with other data types is a powerful capability and holds promise for the future of diagnostics and precision medicine.

Biography: Shiva Amiri is the CEO of BioSymetrics Inc. where they are developing a unique real-time machine learning technology for the analysis of massive data in biomedicine. BioSymetrics specializes in providing optimized pipelines for complex data types and effective methods in the analytics of integrated data. Prior to BioSymetrics she was the Chief Product Officer at Real Time Data Solutions Inc., she has led the Informatics and Analytics team at the Ontario Brain Institute, where they developed Brain-CODE, a large-scale neuroinformatics platform across the province of Ontario. She was previously the head of the British High Commission’s Science and Innovation team in Canada. Shiva completed her Ph.D. in Computational Biochemistry at the University of Oxford and her undergraduate degree in Computer Science and Human Biology at the University of Toronto. Shiva is involved with several organisations including Let’s Talk Science and Shabeh Jomeh International.

IEEE ComSoc Distinguished Lecture: Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets

Saturday, June 3rd, 2017

Tuesday June 13, 2017 at 3:00 p.m. Prof. Anura Jayasumana, Distinguished Lecturer of the IEEE Communications Society, will be presenting a distinguished lecture “Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets”. Note refreshments begin at 2:00 p.m.

Day & Time: Tuesday June 13, 2017
2:00 p.m. – 3:00 p.m. Refreshments
3:00 p.m. – 4:00 p.m. Lecture

Speaker: Prof. Anura Jayasumana
Dept. of Electrical & Computer Engineering
Colorado State University, Ft. Collins, CO 80523 USA

Location: Room BA 2135
40 St. George Street
Toronto, ON M5S 2E4

Contact: Eman Hammad

Event Link:

Abstract: Driven by higher potency and lower cost/size of devices capable of sensing, actuating, processing and communicating, the Internet of Things and of Everything promises to dramatically increase our ability to embed intelligence in the surroundings. Subnets of simple devices such as RFIDs and tiny sensors/actuators deployed in massive numbers in 2D and complex 3D spaces will be a key aspect of this emerging infrastructure. Most techniques for self-organization, routing and tracking in such networks rely on distances and localization in the physical domain. While geographic coordinates fit well with our intuitions into physical spaces, their use is not feasible in complex environments. Protocols based on geographical coordinates do not scale well to 3D either. We present a novel localization-free coordinate system, the Topology Coordinates (TC). Interestingly, geographic features such as voids and shapes are preserved in the resulting Topology-Preserving Maps (TPMs) of 2-D and 3-D networks. Ability to specify virtual cardinal directions and angles in networks is a radical change from the traditional approaches. A novel self-learning algorithm is presented to provide network awareness to individual nodes, a step toward large-scale evolving sensor networks. Application of TCs to social networking will be illustrated.

Biography: Anura Jayasumana is a Professor of Electrical and Computer Engineering at Colorado State University, where he also holds a joint appointment in Computer Science. He is the Associate Director of Information Sciences & Technology Center at Colorado State. He is a Distinguished Lecturer of the IEEE Communications Society. His research interests span high-speed networking to wireless sensor networking, and anomaly detection to DDoS defense. He has served extensively as a consultant to industry ranging from startups to Fortune 100 companies. He received the B.Sc. degree from the University of Moratuwa, Sri Lanka and M.S. and Ph.D. degrees in Electrical Engineering from the Michigan State University. Prof. Jayasumana has supervised 20+ Ph.D. and 50+ M.S. students, holds two patents, and is the co-author over 250 papers. He is the recipient of the Outstanding Faculty Award from the Mountain States Council of the American Electronics Association.

Robust Beamforming Design: A New Approach

Saturday, June 3rd, 2017

Wednesday June 7, 2017 at 2:00 p.m. Mostafa Medra, PhD. Candidate, will be presenting “Robust Beamforming Design: A New Approach”.

Day & Time: Wednesday June 7, 2017
2:00 p.m. – 3:00 p.m.

Speaker: Mostafa Medra, PhD. Candidate
Dept. of Electrical & Computer Engineering
McMaster University

Location: Room BA 2145
40 St. George Street
Toronto, ON M5S 2E4

Contact: Eman Hammad

Event Link:

Abstract: Due to the increasing demand for higher data rates, spatial multiplexing received a lot of attention. The ability of a base station to do beamforming so that it can serve multiple users at the same time slot and frequency can provide significantly higher rates. When the channel state information is assumed to be perfectly known at the transmitter, designs as the zeroforcing, regularized zero-forcing and maximum ratio transmission can be applied. Those conventional methods are typically of low complexity. In reality the channel state information is estimated and estimation errors are inevitable. Many beamforming designs tried to incorporate the channel uncertainty model into the design problem. While those robust designs normally work better than the conventional designs, their computational complexity is usually much higher. Today we will provide a new approach to dealing with robust beamforming design that is of low- complexity and performs significantly better than both conventional and current robust methods.

Biography: Mostafa Medra (S’06-M’16) received the B.Sc. and M.Sc. degrees, both in Electrical Engineering, from Alexandria University, Alexandria, Egypt in 2009 and 2013, respectively. Since the fall of 2013, he has been working towards his Ph.D. degree at McMaster University, Hamilton, Ontario, Canada. He held a research position with the Spirtonic research team in 2012-2013, working on digital signal processing for non-destructive testing using ultrasonic waves. His current research interests include MIMO communications, optimization, wireless communications and signal processing.

InAs Quantum Dot Micro-disk Lasers Grown on Exact (001) Si Emitting at Communication Wavelengths

Monday, May 29th, 2017

Wednesday May 31, 2017 at 2:10 p.m. Kei May Lau, Fang Professor of Engineering and Chair Professor at the Hong Kong University of Science and Technology will be presenting “InAs Quantum Dot Micro-disk Lasers Grown on Exact (001) Si Emitting at Communication Wavelengths”.

Day & Time: Wednesday May 31, 2017
2:10 p.m. – 3:00 p.m.

Speaker: Kei May Lau
Fang Professor of Engineering and Chair Professor
Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology

Location: Room BA 1220
40 St. George Street
Toronto, ON M5S 2E4

Contact: Junho Jeong

Organizers: IEEE Toronto Photonics Society

Abstract: To support an energy-efficient optical interconnect technology enabled by silicon photonics, development of low-energy-consumption active devices and the corresponding integration technology is needed. Most communication wavelength lasers with excellent device performance have been grown on III-V substrates and bonded to silicon. For integration, there are considerable advantages in a technology that allow growth and fabrication of such lasers on III-V/ Si compliant substrates. Quantum dot (QD) active layers grown on lattice-matched substrates have already shown their capability for lasers with low-threshold densities and temperature-independent operation. In addition, the reduced sensitivity of QD to defects and their unique capability of filtering dislocations make them an ideal candidate as the gain medium of hetero-integrated III-V on Si optical sources. In this talk, I will discuss the growth of multi-stack QDs on compliant substrates by MOCVD. Fabrication and laser characteristics of whispering-gallery-mode (WGM) micro-disk lasers using the grown epitaxial structures will also be discussed. Initial demonstration was achieved using simple a colloidal lithography process in combination with dry and wet-etching. The micro-disk lasers were one to four microns in diameter, with single mode lasing at either 1.3 or 1.55 μm, depending on the barrier/cladding system. With smooth sidewalls and sufficient undercut by wet etching of the pedestal, the air-cladded MDs exhibit ultra-low thresholds of a few mW by optical pumping. Preliminary results of electrically-pumped micro-lasers will also be presented. These energy-efficient microlasers are excellent candidates for on-chip integration with silicon photonics.

Biography: Professor Kei May Lau is Fang Professor of Engineering at the Hong Kong University of Science and Technology (HKUST). She received the B.S. and M.S. degrees in physics from the University of Minnesota, Minneapolis, and the Ph.D. degree in Electrical Engineering from Rice University, Houston, Texas. She was on the ECE faculty at the University of Massachusetts/Amherst and initiated MOCVD, compound semiconductor materials and devices programs. Since the fall of 2000, she has been with the ECE Department at HKUST. She established the Photonics Technology Center for R&D effort in III-V materials, optoelectronic, high power, and high-speed devices. Professor Lau is a Fellow of the IEEE, and a recipient of the US National Science Foundation (NSF) Faculty Awards for Women (FAW) Scientists and Engineers (1991) and Croucher Senior Research Fellowship (2008). She is an Editor of the IEEE EDL and Associate Editor of Applied Physics Letters.

Women in Robotics: Building Smart Robots with AI

Saturday, May 20th, 2017

Wednesday May 31, 2017 at 6:00 p.m. hear about the work of Dr. Sanja Fidler, Assistant Professor in Machine Learning and Computer Vision, University of Toronto and Dr. Inmar Givoni, Director of Machine Learning at Kindred Systems Inc., as part of “Women in Robotics: Building Smart Robots with AI”.

Day & Time: Wednesday May 31, 2017
6:00 p.m. – 9:00 p.m.

Speakers: Dr. Sanja Fidler, Assistant Professor, Department of Computer Science, University of Toronto
Dr. Inmar Givoni, Director, Machine Learning, Kindred Systems Inc.

Location: To be Announced

Organizers: IEEE Toronto Engineering in Medicine and Biology Society (EBMS), IEEE Women in Engineering, Society of Women Engineers Toronto


6:00 pm – Networking
6:30 pm – Welcome
6:40 pm – Speakers
7:30 pm – Panel Discussion – Women in Robotics
8:00 pm – Networking
9:00 pm – Close

Get Your Bot On!, its partners Society of Women Engineers Toronto, IEEE Toronto Engineering in Medicine and Biology Society (EBMS) and IEEE Women in Engineering are pleased to bring you the ‘Women in Robotics Speaker Series’. This series celebrates the work of women in the field of robotics and provides a forum for them to share their work and career with the community. We invite all community members to come and learn, participate in the discussion, and celebrate the contribution of women to this field.

Dr. Sanja Fidler, Assistant Professor, Department of Computer Science, University of Toronto

Dr. Sanja Fidler is an Assistant Professor at the Department of Computer Science, University of Toronto. She is the recipient of the Amazon Academic Research Award (2017) and the NVIDIA Pioneer of AI Award (2016). Previously she was a Research Assistant Professor at TTI-Chicago a philanthropically endowed academic institute located in the campus of the University of Chicago. She completed her PhD in computer science at University of Ljubljana in 2010, and was a postdoctoral fellow at University of Toronto during 2011-2012.

In 2010 she visited UC Berkeley. She has served as a Program Chair of the 3DV conference, and as an Area Chair of CVPR, EMNLP, ICCV, ICLR, and NIPS. Together with Rich Zemel and Raquel Urtasun, she received the NVIDIA Pioneer of AI award.

Her main research interests are object detection, 3D scene understanding, and the intersection of language and vision.

You can find Dr. Fidler on the web at

Dr. Inmar Givoni, Director, Machine Learning, Kindred Systems Inc.

Dr. Inmar Givoni is the Director of Machine Learning at Kindred, where her team develops algorithms for machine intelligence, at the intersection of robotics and AI. Prior to that, she was the VP of Big Data at Kobo, where she led her team in applying machine learning and big data techniques to drive e-commerce, customer satisfaction, CRM, and personalization in the e-pubs and e-readers business. She first joined Kobo in 2013 as a senior research scientist working on content analysis, website optimization, and reading modelling among other things. Prior to that, Inmar was a member of technical staff at Altera (now Intel) where she worked on optimization algorithms for cutting-edge programmable logic devices.

Inmar received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. During her graduate studies, she worked at Microsoft Research, applying machine learning approaches for e-commerce optimization for Bing, and for pose-estimation in the Kinect gaming system. She holds a BSc in computer science and computational biology from the Hebrew University in Jerusalem. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at big data, analytics, and machine learning events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths.

You can find Dr. Givoni on the web at

Biomedical Signal and Image Analysis Workshop

Thursday, May 18th, 2017

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”.

Day & Time: Wednesday May 24, 2017
Morning Session: 9:15 a.m. – 12:30 p.m
Afternoon Session: 1:15 p.m. – 4:30 p.m.


Dr. Rangaraj M. Rangayyan,
Department of Electrical & Computer Engineering
University of Calgary, AB, Canada

Dr. Sridhar Krishnan,
Department of Electrical & Computer Engineering
Ryerson University, ON, Canada

Dr. April Khademi,
Department of Electrical & Computer Engineering
Ryerson University, ON, Canada

Dr. Karthy Umapathy,
Department of Electrical & Computer Engineering
Ryerson University, ON, Canada

Dr. Naimul Khan,
Department of Electrical & Computer Engineering
Ryerson University, ON, Canada

Dr. Teodiano Bastos,
Departamento de Engenharia Elétrica
Universidade Federal do Espírito Santo, Vitoria, Brasil

Location: ENG 102, George Vari Engineering and Computing Centre
245 Church Street
Toronto, Ontario M5B 2K3
Ryerson University

Contact: Mehrnaz Shokrollahi
Yashodhan Athavale

Organizers: Signal Analysis Research (SAR) Lab, Ryerson University
IEEE Signal Processing Chapter, Toronto Section
IEEE Engineering in Medicine and Biology Society, Toronto Chapter

Morning Session:

9:15am Welcome remarks
9:30am Talk M1: Color Image Processing with Biomedical Applications – Dr. Raj Rangayyan, U of Calgary

10:45am – 11:00am break

11:00am Talk M2: Medical Image Analysis Techniques for Radiology and Pathology Images – Dr. April Khademi, Ryerson Univ.
11:45am Talk M3: Biomedical Signal Processing for Cardiac Arrhythmias – Dr. Karthi Umapathy, Ryerson Univ.

Afternoon Session:

1:15pm Talk A1: Wearables, IoT and Analytics for Connected Healthcare – Dr. Sri Krishnan, Ryerson Univ.
2:00pm Talk A2: Assistive Technologies and BCI for Rehab Applications – Dr. Teodiano Bastos, UFES, Brazil

2:45pm – 3:00pm break

3:00pm Talk A3: Interactive Machine Learning for Biomedical Signal and Image Analysis – Dr. Naimul Khan, Ryerson Univ.
3:45pm – 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


Rangaraj 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.

Sridhar (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.

Karthi 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.

April 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.

Naimul 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.

Teodiano 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

Factory Tour of Northern Transformer In Vaughan

Wednesday, May 17th, 2017

Friday June 30, 2017 at 2:00 p.m. IEEE Toronto is proud to present a facility tour of Northern Transformer in Vaughan.

The IEEE Toronto Industry Relations Committee and Power & Energy Chapter would like to thank Northern Transformer for hosting this very successful tour and their amazing hospitality.

Day & Time: Friday June 30, 2017
2:00 p.m. – 4:00 p.m.

Location: Northern Transformer
245 McNaughton Rd. E.
Maple, Ontario
Canada L6A 4P5


Contact: Hugo Sanchez

Organizers: IEEE Toronto Industry Relations Committee, Power & Energy Chapter
Co-sponsored by Hugo Sanchez

Abstract: Northern Transformer, founded in Concord, Ontario in 1981, is a North American manufacturer of liquid filled transformers of the highest quality and reliability serving the North American market. Northern Transformer’s primary focus is the design and manufacture of liquid filled Power Transformers, Grounding Transformers and Specialty Transformers ranging from 500kVA to 115MVA with a maximum primary voltage of 160kV (650 BIL).

Attendees are encouraged to bring their own safety shoes and glasses to provide themselves with an additional layer of safety. However, the safety shoes and glasses are not mandatory to attend this tour.

Pictures from Event:

Designing a Gamification Course for an Higher Education Audience

Friday, May 12th, 2017

Friday May 26, 2017 at 1:30 p.m. Dr. Sergio A. A. Freitas, Associate Professor in the Gama Engineering College (FGA) and Director of the Distance Education Center at the University of Brasilia (UnB), Brazil, will be presenting “Designing a Gamification Course for an Higher Education Audience”.

Day & Time: Friday May 26, 2017
1:30 p.m. – 3:30 p.m

Speaker: Dr. Sergio A. A. Freitas
Associate Professor in the Gama Engineering College (FGA)
Director of the Distance Education Center
University of Brasilia (UnB), Brazil

Location: Ryerson University
George Vari Centre for Computing and Engineering
Room: ENG 288
245 Church Street
Toronto, Ontario M5B 2K3

Contact: Dr. Maryam Davoudpour

Organizers: IEEE Toronto (WIE, Measurement/Instrumentation-Robotics, Magnetics chapters), Computer Science Department of Ryerson University

Abstract: The gamification of activities in classrooms has become of great interest in higher education. Today’s students have a lot of experience in virtual environments and games, and researchers who have tested/used gamification in their classrooms have reported an increase in student engagement and retention.

This course presents a four step process to create a gamified course: Identifying the students’ profile (step 1) and the gamification object (step 2), creating the gamification project (step 3), and finally, implementing the gamification project (step 4).

At the end of the workshop it is expected that the participant will be able to design a basic gamified course.

Biography: Dr. Sergio A. A. Freitas is currently an Associate Professor in the Gama Engineering College (FGA) and Director of the Distance Education Center at the University of Brasilia (UnB), Brazil. He is also the coordinator of research in the FGA Software Factory Laboratory. His current research projects focus on interdisciplinary studies and applications of learning methodologies on engineering undergraduate courses, and software engineering methodologies. Prof. Freitas areas of expertise include gamification, PBL, virtual learning environments in education and training, and software engineering methodologies. Dr. Freitas has coauthored journal publications, conference articles and book chapters in the aforementioned topics, and has coordinated and participated on many projects from various funding agencies CNPq, FAP-ES, FAP-DF, Cebraspe, and Brazilian Federal Ministries.

7th Annual E3 Symposium

Thursday, May 4th, 2017

Friday May 12, 2017 the School of Engineering Technology and Applied Science and the Centennial Energy Institute invite you to our 2017 E3 Symposium: The Future is Smart: The Transformation of Canadian Manufacturing. This event will bring together advanced manufacturing innovators from across a number of sectors in the economy. The event will feature industry titans sharing best practices.

Day & Time: Friday May 12, 2017
8:00 am to 8:45 am – Registration and Breakfast.
9:00 am to 4:00 pm – Speakers & Sessions.

Location: Centennial College: Progress Campus
Library Building Auditorium
941 Progress Avenue, Toronto, Ontario

Event Page:

Contact: Maryam Davoudpour