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Archive for the ‘Signals & Computational Intelligence’ Category

IEEE Ryerson Python Workshop 5

Monday, March 19th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are pleased to announce the start of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python as well as Machine learning to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 19, 2018

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 4

Monday, March 12th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the fourth workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 12, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

IEEE Ryerson Python Workshop 3

Friday, March 2nd, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the third workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, March 5, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

Register at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-3-tickets-43189931247

IEEE Ryerson Python Workshop 2

Saturday, February 10th, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, WIE IEEE Toronto, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the second workshop of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, February 12, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

Register at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-2-tickets-42931234478

Introduction to Python Workshop

Thursday, February 1st, 2018

IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE WIE, IEEE Computational Intelligence Chapter, and Robotics/ Automation Chapter are Please to announce the start of their series of python workshops. A series of 6 workshops will give the participants the ability to use the basics of python as well as Machine learning to help them in their study or workplace. At the end of these workshops there will be a certificate given to participants who attended these workshops.

Day & Time: Monday, February 5, 2018
6:00 p.m. ‐ 8:00 p.m.

Location: Ryerson University (Victoria Building, Room VIC 301)

Contact: ieee.ryersonu@gmail.com

Organizer: IEEE Ryerson Student Branch, IEEE Ryerson Computer Chapter, IEEE Computational Intelligence Chapter, WIE IEEE Toronto, Instrumentation-Measurement/Robotics-Automation

RVSP: https://www.eventbrite.com/e/ieee-ryerson-intro-to-python-workshop-tickets-42588313793

Why Deep Learning Works So Well?

Friday, November 17th, 2017

Monday, November 27th at 10:30 a.m., Prof. C.-C. Jay Kuo, Fellow of IEEE and Dean’s Professor in Electrical Engineering-Systems, University of Southern California, will be presenting “Why Deep Learning Works So Well?”.

Day & Time: Monday, November 27, 2017
10:30 a.m. ‐ 11:30 a.m.

Speaker: Prof. C.-C. Jay Kuo, Fellow of IEEE, AAAS, SPIE
Dean’s Professor in Electrical Engineering-Systems, University of Southern California

Location: Room ENG 358
George Vari Engineering Building (Intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Xiao-Ping Zhang, Alireza Sadeghian, Alex Dela Cruz

Organizer: Electrical and Computer Engineering and CASPAL Ryerson
Signals & Computational Intelligence Chapter

Abstract: Deep learning networks, including convolution and recurrent neural networks (CNN and RNN), provide a powerful tool for image, video and speech processing and understanding nowadays. However, their superior performance has not been well understood. In this talk, I will unveil the myth of the superior performance of CNNs. To begin with, I will describe network architectural evolution in three generations: first, the McClulloch and Pitts (M-P) neuron model and simple networks (1940-1980); second, the artificial neural network (ANN) (1980-2000); and, third, the modern CNN (2000-Present). The differences between these three generations will be clearly explained. Next, theoretical foundations of CNNs have been studied from the approximation, the optimization and the signal representation viewpoints, and I will present main results from the signal processing viewpoints. I will use an intuitive way to explain the complicated operations of the CNN systems.

Biography: Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Dean’s Professor in Electrical Engineering-Systems. His research interests are in the areas of digital media processing, compression, communication and networking technologies. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 140 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. Dr. Kuo is a co-author of about 250 journal papers, 900 conference papers and 14 books.

Data-Driven Care: Enabling Science and Technologies

Friday, November 10th, 2017

Tuesday, November 21st at 5:00 p.m., Dr. Philip Asare, Assistant Professor of Electrical and Computer Engineering at Bucknell University, will be presenting “Data-Driven Care: Enabling Science and Technologies”.

Day & Time: Tuesday November 21st, 2017
5:00 p.m. – 6:00 p.m.

Speaker: Dr. Philip Asare
Assistant Professor of Electrical and Computer Engineering
Swanson Fellow in Sciences and Engineering
Multicultural Student Services Faculty Fellow (Fall 2015)
Bucknell University

Location: Room ENG-LG 12
George Vari Engineering Building (Intersection of Church & Gould)
Ryerson University
245 Church St, Toronto, M5B 1Z4

Contact: Alireza Sadeghian, Alex Dela Cruz

Organizer: Signals & Computational Intelligence Chapter

Abstract: Recent advances in medical technologies provide an opportunity to collect and use a variety of data to assist in the delivery of care to patients in and out of the clinic. In the clinic, tools can be developed that provide insights into patient state that were not previously possible. In some cases various actions can be automated to assist clinicians in delivering care. Outside the clinic, patients can be empowered to manage their own care as they go about their daily lives without being confined to the hospital. Quite a number of impressive technologies have been demonstrated in the research space with a few emerging as commercial projects on the market; however, there are a number of challenges to overcome in order to realize the full potential of these technological advances. This talk will describe past and on-going work in this area by the speaker and others to ensure that the data are trustworthy, the tools that depend on the data are robust and safe, and the technologies are more likely to be adopted by the healthcare ecosystem. These would hopefully lead to the greatest possible impact for patients and their care providers.

Biography: Philip Asare is an Assistant Professor of Electrical and Computer Engineering and Swanson Fellow in the Sciences and Engineering at Bucknell University, in Lewisburg, Pennsylvania, in the USA. He is currently a Visiting Scholar/Professor in Electrical and Computer Engineering at Ryerson University during his leave from Bucknell for the 2017-18 academic year. His research interests are in the general are of cyber-physical systems with medicine being one of his primary application areas. He was a Scholar-in-Residence at the U.S. Food and Drug Administration for the 2012-13 academic year working with researchers in the Office of Science and Engineering Laboratories on regulatory approaches for emerging mobile connected medical devices. His work in this area has received a best student paper and best paper award at the Interncation Conference on Body Area Networks (BodyNets). He most recently co-organize the Prototype to Patient Treatment workshop as part of the 2016 Annual Wireless Health Conference through the National Science Foundation Nanosystems Engineering Research Center (NERC) for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST). Asare is a member of the IEEE and its Computer Society and Engineering in Medicine and Biology Society (EMBS). He is also a member of the ACM and its Special Interest Group on Embedded Systems (SIGBED).

On System-Level Analysis & Design of Cellular Networks: The Magic of Stochastic Geometry

Thursday, August 31st, 2017

Friday September 8, 2017 at 10:00 a.m. Professor Marco Di Renzo from Paris-Saclay University/CNRS, will be presenting “On System-Level Analysis & Design of Cellular Networks: The Magic of Stochastic Geometry”.

Day & Time: Friday September 8, 2017
10:00 a.m. – 11:00 a.m.

Speaker: Professor Marco Di Renzo
Paris-Saclay University/CNRS, France

Location: Room ENG288
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: This talk is aimed to provide a comprehensive crash course on the critical and essential importance of spatial models for an accurate system-level analysis and optimization of emerging 5G ultra-dense and heterogeneous cellular networks. Due to the increased heterogeneity and deployment density, new flexible and scalable approaches for modeling, simulating, analyzing and optimizing cellular networks are needed. Recently, a new approach has been proposed: it is based on the theory of point processes and it leverages tools from stochastic geometry for tractable system-level modeling, performance evaluation and optimization. The potential of stochastic geometry for modeling and analyzing cellular networks will be investigated for application to several emerging case studies, including massive MIMO, mmWave communication, and wireless power transfer. In addition, the accuracy of this emerging abstraction for modeling cellular networks will be experimentally validated by using base station locations and building footprints from two publicly available databases in the United Kingdom (OFCOM and Ordnance Survey). This topic is highly relevant to graduate students and researchers from academia and industry, who are highly interested in understanding the potential of a variety of candidate communication technologies for 5G networks.

Biography: Marco Di Renzo received the “Laurea” and Ph.D. degrees in Electrical and Information Engineering from the University of L’Aquila, Italy, in 2003 and 2007, respectively. In October 2013, he received the Doctor of Science degree from the University Paris-Sud, France. Since 2010, he has been a “Chargé de Recherche Titulaire” CNRS (CNRS Associate Professor) in the Laboratory of Signals and Systems of Paris-Saclay University – CNRS, CentraleSupélec, Univ Paris Sud, France. He is an Adjunct Professor at the University of Technology Sydney, Australia, a Visiting Professor at the University of L’Aquila, Italy, and a co-founder of the university spin-off company WEST Aquila s.r.l., Italy. He serves as the Associate Editor-in-Chief of IEEE COMMUNICATIONS LETTERS, and as an Editor of IEEE TRANSACTIONS ON COMMUNICATIONS and IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society and IEEE Communications Society. He is a recipient of several awards, and a frequent tutorial and invited speaker at IEEE conferences.

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.