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SUMMARY:AI against COVID-19 Competition: Closing Ceremony
DESCRIPTION:IEEE SIGHT (Special Interest Group on Humanitarian Technology) of Montreal Section\, Vision and Image Processing Research Group of the University of Waterloo and DarwinAI Corp. invite you to the closing ceremony of the virtual competition on AI for COVID-19 diagnosis with chest X-ray images. In the First Phase\, the challenge consisted of designing robust machine learning algorithms to predict if the subjects of study are either COVID-19 positive or COVID-19 negative. Join us to celebrate the amazing work done by all the teams and know who will be participating in the Second Phase. Then\, you are also invited to a networking session with everybody! \nAll the information will be sent to the registrants.
URL:https://www.ieeetoronto.ca/event/ai-against-covid-19-competition-closing-ceremony/
LOCATION:Virtual
CATEGORIES:SIGHT
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20210531T000000
DTEND;TZID=America/Toronto:20210531T235500
DTSTAMP:20260416T103824
CREATED:20210520T162352Z
LAST-MODIFIED:20210809T204824Z
UID:10000415-1622419200-1622505300@www.ieeetoronto.ca
SUMMARY:AI against COVID-19: Screening X-ray Images for COVID-19 Infections
DESCRIPTION:Join the virtual competition on AI for COVID diagnosis\, thanks to Microsoft Canada\, the exclusive technology and cloud platform sponsor! \n\n\n\nThe 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? \nIEEE SIGHT (Special Interest Group on Humanitarian Technology) of 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. \nIn the First Phase of the competition\, 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. \nThis competition is organized in collaboration with the National Research Council Canada and is 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)\, University of Toronto and Vancouver Simon Fraser University\, WIE (Women In Engineering) Ottawa. It is largely sponsored by Microsoft\, and partially by the IEEE Canada Humanitarian Initiatives Committee and the IEEE Montreal Section. \nHow to participate \nNote: This competition only accepts participants living in Canada\, due to restrictions on funds transfer. \nNO PURCHASE NECESSARY TO ENTER OR WIN. \nThe competition is hosted on the Eval.ai online platform. To participate\, you or your team will need to perform the following steps: \n\nRegister individually at the link provided below in the current webpage (vTools).\nRegister yourself or your team at the link on Eval.ai: https://eval.ai/web/challenges/challenge-page/925/participate. Follow the instructions here: https://evalai.readthedocs.io/en/latest/participate.html#.\nDownload the dataset from https://www.kaggle.com/andyczhao/covidx-cxr2.\nDesign an AI algorithm that gets CXR images as inputs and predicts the labels of the images in the output (COVID or non-COVID).\nTrain your AI algorithm using the training dataset.\nSubmit your AI algorithm through Eval.ai for evaluation against the test dataset for the competition.\n\nPrizes \nFor the First Phase\, the first five best solutions will be awarded monetary prizes and Azure credits: \n\nFirst place: 1\,000 CAD + 500 CAD in Azure.\nSecond place: 800 CAD + 300 CAD in Azure.\nThird place: 600 CAD + 300 CAD in Azure.\nFourth place: 400 CAD + 300 CAD in Azure.\nFifth place: 300 CAD + 300 CAD in Azure.\n\nThe top 10 teams on the leaderboard will also have the following opportunities: \n\nParticipate in the 2nd phase to refine their solution and receive funding for a project.\nWrite a scientific paper with the Vision and Image Processing Research Group\, from the University of Waterloo\, to explain their approach.\n\nFor the Second Phase\, the best three projects can receive funds up to the following amounts: \n\nProject 1: 5\,000 CAD.\nProject 2: 5\,000 CAD.\nProject 3: 4\,000 CAD.\n\nTerm of funding: Up to 4 months following the announcement of the selected teams. The deadline is December 31st\, 2021. \n\nFor more information\, visit IEEE SIGHT Montreal website. \n 
URL:https://www.ieeetoronto.ca/event/ai-against-covid-19-screening-x-ray-images-for-covid-19-infections/
LOCATION:Virtual
CATEGORIES:SIGHT,Young Professionals
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BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20210323T090000
DTEND;TZID=America/Toronto:20210323T130000
DTSTAMP:20260416T103824
CREATED:20210430T023727Z
LAST-MODIFIED:20210809T204252Z
UID:10000368-1616490000-1616504400@www.ieeetoronto.ca
SUMMARY:Hands-on Reinforcement Learning Workshop using Python
DESCRIPTION: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. \nVirtual platform info has been delivered to registrants in rounds of emails. For immediate assistance\, please write us at yp.ieee.mtl@gmail.com \nSpeakers: \nSadia Khaf \nSadia Khaf received the B.E. degree in electrical engineering from the National University of Sciences and Technology\, School of Electrical Engineering and Computer Science (NUST-SEECS)\, Islamabad\, Pakistan\, in 2015. She received the M.Sc. degree in electrical and electronics engineering from Bilkent University\, Ankara\, Turkey\, in 2018. From 2015 to 2018\, she was a Research Assistant with IONOLAB\, Turkey. From 2018 to 2020\, she worked with the Faculty of Electrical Engineering\, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI)\, Pakistan\, as a Lecturer. She conducted her research there on Mobile Edge Computing and Deep Learning with the TeleCoN research group. Currently\, she is with École de Technologie supérieure\, Montreal\, Canada\, as a Ph.D. student. Her research interests include reinforcement learning\, radio resource management\, cognitive radio networks\, and industrial internet-of-things (IIoT). She was the recipient of the highest level of merit scholarships at NUST\, Bilkent\, and ÉTS. She also secured the P.E.O. International Peace Scholarship. She is the co-founder of SAYA school\, Pakistan\, and IEEE Women in Engineering (WIE) branch at ÉTS. She serves as the Vice-Chair of the IEEE ÉTS and Industrial Relations Manager of the IEEE Montreal Young Professionals Affinity Group. \nFaye Satari \nFaye Satari was born in Quchan\, a small town with minimal educational infrastructure and facilities. When she finished primary school\, she was accepted in the provincial entrance exam of the Exceptional Talents High School. After excelling in high school and hard working around the clock\, she participated in a very competitive tuition-free nationwide university entrance exam (i.e. Konkour) among about one and half million participants; She was accepted in Computer Software Engineering of Urmia University. During her undergraduate education\, she actively participated in many teamwork projects and attended some technical seminars as well as joining associations at her university. Furthermore\, she got the title of top student in technical faculty of the university in one semester and received her B. Sc. degree in Computer Software Engineering from Urmia University of Technology\, Urmia\, Iran\, in 2008. She is currently pursuing an M.Sc.A. Computer Engineering in the Department of Computer and Software Engineering\, Polytechnique Montréal\, University of Montreal\, Montreal\, Canada and she is a member of IEEE Young Professionals. Her current research interests include the Internet of Things (IoT)\, Smart Cities\, and telecommunications systems.
URL:https://www.ieeetoronto.ca/event/hands-on-reinforcement-learning-workshop-using-python/
LOCATION:Montreal\, Quebec Canada
CATEGORIES:SIGHT,Women in Engineering,Young Professionals
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