Hands-on Reinforcement Learning Workshop using Python

Online - Webinar ON

IEEE Young Professionals Affinity Group Montreal brings you a free hands-on reinforcement learning workshop using Python in Google Colab. This event is co-hosted by IEEE YP Ottawa, YP Toronto, YP Vancouver, IEEE SBs of Polytechnique Montreal, Concordia, ETS, INRS, WIE Ottawa, SIGHT Montreal, and CAS technical chapter of Vancouver section. All students at all levels are welcome to attend, however, registration is mandatory through the secure IEEE web portal. This workshop will cover the basics of using Colab, an introduction to reinforcement learning, and together we will write your first Q-learning code. The workshop will be interactive, and you will have a chance to code with us and ask your questions. We will also have breaks, a discussion forum, polls, and Q&A.   Register  

Free

An overview on IEEE Canada Humanitarian Technology Initiatives

Online - Webinar ON

This talk will introduce the humanitarian track in IEEE and will showcase exemplary IEEE Humanitarian initiatives led by students and young professionals. The aim of this workshop is to encourage IEEE Volunteers to get involved in their local communities and plan events and projects to address those needs.   Register

Free

WIE ILC NETWORKING EVENT – REGION 7

Online - Talk Online - Talk, ON

Join us as we discuss career transitions - from undergrad to graduate school, graduation to academia, and graduation to industry - in an open roundtable forum. Hear each of our panel members share an overview of their research area and why they chose it. We are proud to announce that Dr. Hala As’ad, Applied Signal Processing, Audio & Speech Processing, Machine Learning, Deep Learning will be representing us. Everyone is welcome. Amazon & Tim Horton’s gift cards to be won! Register  

Free

The power of data: Utilizing Machine Learning for Earth, the Sun, and Mars

Online - Talk Online - Talk, ON

Abstract: Data is everywhere and can lead to incredibly valuable insights when harnessed correctly. From automating daily tasks to discovering breakthrough science, to making predictions about the future, utilizing data with machine learning is a fast and ever-evolving field of technology. In this talk, Kelsey will discuss her experiences and challenges using machine learning for space applications, focusing on uses for satellites orbiting Earth, how we can better understand our Sun and its interaction with our atmosphere, and how we can incorporate machine learning into future Mars rover missions. About the speaker: Kelsey Doerksen is a Space Systems Engineer in satellite operations at Planet, a San Francisco-based company that operates the world's largest Earth Observation satellite constellation. In her role, she is responsible for maintaining the health and productivity of 100s of satellites daily and develops software to automate operations and detect anomalous satellite behavior. Kelsey holds a bachelor's degree from Carleton University in Aerospace Engineering: Space Systems Design and a Master's in Electrical and Computer Engineering from the University of Western Ontario. She researches with the Paris Observatory in the fields of Space Weather and Space Debris and has previously interned at the NASA Jet Propulsion Lab with the Machine Learning and Instrument Autonomy Group. Kelsey is beginning her Ph.D. in the Autonomous Intelligent Machines and Systems program at the University of Oxford this Fall, where she will be utilizing machine learning and Earth Observation imagery to research the impacts of climate change on our planet.   Register  

Free

Machine Learning in Biomedical Data

Online - Talk Online - Talk, ON

Abstract In the digital age, the amount of worldwide data that is being generated on a daily basis is rapidly growing, reaching 175 zettabytes by 2025. These massive volumes of data have led to growing interest in using Machine Learning (ML) algorithms to extract valuable insights from databases. ML techniques can be considered as the foundation of a broad spectrum of next-generation technologies, including medical applications. In this presentation, the role of ML in medical applications will be discussed. A newly developed data-driven classification algorithm will be explained, and its performance for the classification of biological datasets will be investigated and compared with the well-known classification models. Bio Behnaz Fakhar Firouzeh is in the final semester of her Ph.D. in Electrical and Computer Engineering at Carleton University. She has been working on signal processing and Compressive Sensing (CS) for over 8 years. She also has 5 years of experience in developing constraint optimization algorithms. Her developed algorithms successfully have been applied in different areas such as signal processing, Machine Learning, and artificial intelligence. Behnaz has (co)authored several articles in different journals and conference proceedings. Register

Free

AI against COVID-19: Screening X-ray Images for COVID-19 Infections

Online event

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has generated an unprecedented global health crisis, with more than 2.7 million deaths worldwide. Do you want to contribute to the fight against this pandemic? IEEE SIGHT (Special Interest Group on Humanitarian Technology) of the Montreal Section, Vision and Image Processing Research Group of the University of Waterloo, and DarwinAI Corp. invite data scientists, students and professionals working on Artificial Intelligence (AI) to participate in a virtual competition to help medical researchers diagnose COVID-19 with chest X-ray (CXR) images. The ultimate goal is to contribute to the development of highly accurate yet practical AI solutions for detecting COVID-19 cases and, hopefully, accelerating the treatment of those who need it the most. Moreover, this AI for Good initiative will also allow us to take action on at least one of the United Nations Sustainable Development Goals (SDGs), Good Health and Well-Being. The competition is composed of 2 phases: In the First Phase, the challenge consists of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. The dataset for this competition is the dataset curated by COVID-Net, a global open-source initiative launched by DarwinAI Corp., Canada, and Vision and Image Processing Research Group, University of Waterloo, Canada, for accelerating advancements in machine learning to aid healthcare workers around the world in the fight against the COVID-19 pandemic. More about the COVID-Net initiative and available open-source resources are available here. In the Second Phase, the 10 top teams of the first phase will have the opportunity to refine their solution and submit a proposal for a follow-up project to positively impact society or the academic community. This competition is organized in collaboration with the National Research Council Canada and co-hosted by the IEEE Young Professionals Affinity Groups of Montreal, Ottawa, Toronto and Vancouver Sections, Vancouver Circuit and Systems (CAS) Technical Chapter, the Student Branches of INRS (Institut National de la Recherche Scientifique), the University of Toronto and Vancouver Simon Fraser University,  and WIE (Women In Engineering) Ottawa. It is also largely sponsored by Microsoft, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. For more details and registration click here!  

Free

Introduction to the Professional Development Series

Online - Webinar ON

This is the main event that will introduce the Professional Development Series organized by IEEE Ottawa Young Professionals Affinity Group. Speaker: Mohamed K. Emara is a PhD Student in the Department of Electronics at Carleton University, and a Committee Member and Area Chair for IEEE Canada Young Professionals (Region 7). He received the B.Eng. degree in aerospace engineering and the M.A.Sc. degree in electrical and computer engineering from Carleton University in 2016 and 2018, respectively. From 2019-2020 he was the Chair of IEEE Ottawa Young Professionals. He held various other volunteer positions with IEEE Canada and IEEE Ottawa Section. Co-Hosting: This series of talks is co-hosted by IEEE Montreal Young Professionals Affinity Group (YPAG), IEEE Saint-Maurice YPAG, IEEE Canada Eastern Provinces YPAG, IEEE Carleton University and IEEE uOttawa Student Branches, and IEEE Ottawa Women in Engineering. Register

Free

Leveraging External Data Sources to Enhance Secure System Design

Online - Webinar ON

ABSTRACT: Today's software systems are riddled with security vulnerabilities that invite attack. We envisage a secure software design process at the architectural level, in which the security requirements are adequate, thus enabling appropriate security controls to be implemented to mitigate known threats and vulnerabilities. How can we ensure that the security requirements are adequate? In this talk, we tackle this question by focusing on how external online data sources for vulnerabilities, attack patterns, threat intelligence, and other security information can be leveraged, using Natural Language Processing (NLP), to assist designers in validating the adequacy of the security requirements. This validation is done by determining which requirements map to known threats (identified from the external data), which requirements may be extraneous, and which threats may need a closer look to identify new requirements. We will discuss the availability and nature of the external data sources and describe how we employ NLP to process the data to support the design of secure systems. BIOGRAPHY: Dr. Jason Jaskolka is an Assistant Professor in the Department of Systems and Computer Engineering and the Director of the Cyber Security Evaluation and Assurance (CyberSEA) Research Lab at Carleton University, Ottawa, ON, Canada. He received his Ph.D. in Software Engineering in 2015 from McMaster University, Hamilton, ON, Canada. His research interests include cyber security evaluation and assurance, threat modeling, security-by-design, and formal methods and data-driven approaches for software and security engineering. He is interested in applying his research to critical infrastructures, industrial control systems, cyber-physical and distributed systems, and the Internet of Things (IoT). Register

Free

ANCWT On Line Cohort for the year 2021

Online - Webinar ON

We are excited to announce the first-ever ANCWT On-Line  Cohort for the year 2021. We are inviting all the new immigrant women and graduating female candidates to apply. This special cohort is going to be hosted on Zoom and uOttawa Brightsapce will be the Leaning management system. Two Steps for getting into the Cohort: Fill the Pre-screening form by clicking the register button below. Register We will contact you for registration via email if you are shortlisted. Applications are welcomed until May 2nd midnight.   Mark the date as June 7, 2021, to June 25th, 2021.  

Free