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DTSTART;TZID=America/New_York:20230329T180000
DTEND;TZID=America/New_York:20230329T190000
DTSTAMP:20260415T181921
CREATED:20230319T141943Z
LAST-MODIFIED:20230402T172157Z
UID:10000620-1680112800-1680116400@www.ieeetoronto.ca
SUMMARY:Digital Health and Health Technology Assessment
DESCRIPTION:Digital Health and HTA (Health Technology Assessment) – Organized by the IEEE Computational Intelligence\nDigital Health provides unique opportunities to strengthen health systems and includes a range of technologies and services. These services and technologies have increasingly been used in the health sector because of their impact on patient-centered outcomes and value in healthcare decision-making. Digital health technologies depending on the application\, target groups\, and outcomes\, can be comparators of traditional health technologies or used as an add-on to increase the effectiveness of those health interventions. Such technologies need to be reimbursed by public and private payers sooner or later. This talk is about digital health reimbursement of health and digital health technologies\, the barriers to using digital health\, and some potential solutions in the health systems.\nKeywords: Digital health\, health technology assessment\nSpeaker(s): Dr. Pooyeh Graili\,\nVirtual: https://events.vtools.ieee.org/m/353132
URL:https://www.ieeetoronto.ca/event/digital-health-and-health-technology-assessment-2/
LOCATION:Virtual: https://events.vtools.ieee.org/m/353132
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210712T180000
DTEND;TZID=UTC:20210716T210000
DTSTAMP:20260415T181921
CREATED:20210615T220333Z
LAST-MODIFIED:20210809T205423Z
UID:10000429-1626112800-1626469200@www.ieeetoronto.ca
SUMMARY:Introduction to Python programming - Registration
DESCRIPTION:This is an introduction to Python programming for students without any prior programming knowledge or experience. The proposed 5-day course covers the fundamental aspects of programming\, which include data types\, various operators\, input/output\, conditions\, control flow\, functions\, and algorithms. The learning experience is enhanced by a number of examples and problem sets (data\, strings\, file processing and simple graphics) that will be solved interactively during the lecture with the participation of the students. The course format includes 3 hours of daily lectures (2 hours of lecture and 1 hour of lab). A certificate of completions will be given to the student who successfully complete the course and pass a short exam at the end of the course to evaluate their knowledge. Electronic copies of the course materials will be provided to the students. The students will also be provided with career advice\, and skills development. \nThe course is delivered online and limited space (25 spots) is available. Please register by July 11. After the registration\, applicants will be contacted with the virtual meeting information and course material prior to the start of the course. \nFees:\n– $250 CAD (IEEE or OSPE Members)\n– $350 CAD (Non-members) \nPlease follow IEEE on Social Media:\nTweets by IEEEToronto \nHome \n \nCourse Objective: Attendees will gain a solid understanding of principles of programing using Python; they can progress to more advanced programming topics and explores algorithms that are integral parts of more sophisticated methodologies\, e.g.\, Artificial Intelligence. Attendees will have the knowledge to write various Python programs\, and to design algorithms manipulating files and different types of data including numbers\, and text. \nNote: This course is designed to be offered online\, and it requires the attendees to use their personal computers/laptops. Details to Join in will be forwarded to Registered Attendees \nWho should attend: Students\, second career trainees\, engineers\, scientists\, clinicians\, and in general specialists in variety of non-STEM fields. \nWhat will you receive after completion: A certificate of completion will be given to the students who successfully complete the course and pass a short exam. Electronic copies of the course materials. Attendees will also be provided with career\, and skills development advice. \nSpeaker\nDr. Alireza Sadeghian \nDr. Alireza Sadeghian has been with the Department of Computer Science at Ryerson University since 1999\, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute\, St. Michael’s Hospital\, and serves as the AI research Theme Lead in Healthcare and Analytics at the Institute for Biomedical Engineering\, Science\, and Technology. \nDr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory and has extensive expertise in the areas of AI\, machine learning\, and Deep Learning particularly related to industrial and medical applications. He has supervised 9 postdoctoral fellows\, 8 PhD\, and 24 Master’s students\, as well as 60 research assistants. He has published over 150 journal manuscripts\, refereed conference papers\, and book chapters\, as well as two edited books. He has 2 invention disclosures and 2 patents. \nDr. Sadeghian has been actively involved with a number of international professional and academic boards including IEEE Education Activity Board. Presently\, he is the Chair of IEEE Computational Intelligence Technical Society Chapter\, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access\, Information Sciences\, and Expert Systems Journal. \nEmail: dr.alireza.sadeghian@ieee.org \nAgenda\nDay 1 – June 7\, 2021\, 6:00-9:00 pm: Introduction to computer systems\, hardware architecture\, CPU\, memory\, compilation\, high level vs. low-level programming language\, data representation\, Python and PyCharm interactive IDE installation\, writing/editing/saving/retrieving and running a simple program\, basic data types\, variables\, assignments\, comments\, and expressions. The material learned will be reinforced through examples provided during the lecture. \nDay 2 – June 8\, 2021\, 6:00-9:00 pm: The following topics will be discussed: conditions\, operators (arithmetic\, logic\, and comparison)\, control statements (if and if-else)\, and loops (for and while). The material learned will be reinforced through examples provided during the lecture. \nDay 3 – June 9\, 2021\, 6:00-9:00 pm: Students will be introduced to Strings and text files in Python. They will learn how to work with files\, reading/writing text and numbers from/to a file\, string manipulation\, indexing\, and string slicing. The material learned will be reinforced through examples provided during the lecture. \nDay 4 – June 10\, 2021\, 6:00-9:00 pm: Functions\, arguments\, and return values will be discussed. The material learned will be reinforced through examples provided during the lecture. \nDay 5 – June 11\, 2021\, 6:00-9:00 pm: The topics of lists and dictionaries will be discussed. Students will learn about the basic operators\, creating\, accessing\, slicing\, adding\, removing\, replacing\, and iteration methods for lists and dictionaries. The material learned will be reinforced through examples provided during the lecture.
URL:https://www.ieeetoronto.ca/event/introduction-to-python-programming-registration-2/
LOCATION:Virtual
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210607T180000
DTEND;TZID=America/New_York:20210611T210000
DTSTAMP:20260415T181921
CREATED:20210430T222549Z
LAST-MODIFIED:20210809T205016Z
UID:10000377-1623088800-1623445200@www.ieeetoronto.ca
SUMMARY:Introduction to Python Programming
DESCRIPTION:This is an introduction to Python programming for students without any prior programming knowledge or experience. The proposed 5-day course covers the fundamental aspects of programming\, which include data types\, various operators\, input/output\, conditions\, control flow\, functions\, and algorithms. The learning experience is enhanced by a number of examples and problem sets (data\, strings\, file processing and simple graphics) that will be solved interactively during the lecture with the participation of the students. The course format includes 3 hours of daily lectures. \nCourse Objective: Attendees will gain a solid understanding of principles of programing using Python; they can progress to more advanced programming topics and explores algorithms that are integral parts of more sophisticated methodologies\, e.g.\, Artificial Intelligence. Attendees will have the knowledge to write various Python programs\, and to design algorithms manipulating files and different types of data including numbers\, and text. \nNote: This course is designed to be offered online\, and it requires the attendees to use their personal computers/laptops. Details to Join in will be forwarded to Registered Attendees \nWho should attend: Students\, second career trainees\, engineers\, scientists\, clinicians\, and in general specialists in variety of non-STEM fields. \nWhat will you receive after completion: A certificate of completion will be given to the students who successfully complete the course and pass a short exam. Electronic copies of the course materials. Attendees will also be provided with career\, and skills development advice. \nSpeaker\nDr. Alireza Sadeghian \nDr. Alireza Sadeghian has been with the Department of Computer Science at Ryerson University since 1999\, where he holds the position of the Professor. He is also an Affiliate Scientist at the Li Ka Shing Knowledge Institute\, St. Michael’s Hospital\, and serves as the AI research Theme Lead in Healthcare and Analytics at the Institute for Biomedical Engineering\, Science\, and Technology. \nDr. Sadeghian was the Chair of the Department of Computer Science from 2005 to 2015. He is the founding Director of the Advanced Artificial Intelligence Initiative (AI2) Laboratory and has extensive expertise in the areas of AI\, machine learning\, and Deep Learning particularly related to industrial and medical applications. He has supervised 9 postdoctoral fellows\, 8 PhD\, and 24 Master’s students\, as well as 60 research assistants. He has published over 150 journal manuscripts\, refereed conference papers\, and book chapters\, as well as two edited books. He has 2 invention disclosures and 2 patents. \nDr. Sadeghian has been actively involved with a number of international professional and academic boards including IEEE Education Activity Board. Presently\, he is the Chair of IEEE Computational Intelligence Technical Society Chapter\, Toronto Section. Dr. Sadeghian is also on the Editorial Board of Applied Soft Computing Journal and serves as an Associate Editor of IEEE Access\, Information Sciences\, and Expert Systems Journal. \nEmail: dr.alireza.sadeghian@ieee.org \nAgenda\nDay 1 – June 7\, 2021\, 6:00-9:00 pm: Introduction to computer systems\, hardware architecture\, CPU\, memory\, compilation\, high level vs. low-level programming language\, data representation\, Python and PyCharm interactive IDE installation\, writing/editing/saving/retrieving and running a simple program\, basic data types\, variables\, assignments\, comments\, and expressions. The material learned will be reinforced through examples provided during the lecture. \nDay 2 – June 8\, 2021\, 6:00-9:00 pm: The following topics will be discussed: conditions\, operators (arithmetic\, logic\, and comparison)\, control statements (if and if-else)\, and loops (for and while). The material learned will be reinforced through examples provided during the lecture. \nDay 3 – June 9\, 2021\, 6:00-9:00 pm: Students will be introduced to Strings and text files in Python. They will learn how to work with files\, reading/writing text and numbers from/to a file\, string manipulation\, indexing\, and string slicing. The material learned will be reinforced through examples provided during the lecture. \nDay 4 – June 10\, 2021\, 6:00-9:00 pm: Functions\, arguments\, and return values will be discussed. The material learned will be reinforced through examples provided during the lecture. \nDay 5 – June 11\, 2021\, 6:00-9:00 pm: The topics of lists and dictionaries will be discussed. Students will learn about the basic operators\, creating\, accessing\, slicing\, adding\, removing\, replacing\, and iteration methods for lists and dictionaries. The material learned will be reinforced through examples provided during the lecture.
URL:https://www.ieeetoronto.ca/event/introduction-to-python-programming/
LOCATION:Virtual
CATEGORIES:Signals & Computational Intelligence,Women in Engineering,Young Professionals
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200825T190000
DTEND;TZID=America/Toronto:20200825T200000
DTSTAMP:20260415T181921
CREATED:20210430T023714Z
LAST-MODIFIED:20210430T235522Z
UID:10000329-1598382000-1598385600@www.ieeetoronto.ca
SUMMARY:MAC Protocol Design for IoT Applications
DESCRIPTION:On Tuesday\, August 25\, 2020 at 7:00 p.m.\, IEEE Toronto Computational Intelligence Society will be hosting “MAC Protocol Design for IoT Applications”. \nDay & Time: Tuesday\, August 25\, 2020\n7:00 p.m. ‐ 8:00 p.m. \nOrganizers: IEEE Toronto Computational Intelligence Society \nLocation: Virtual – Zoom \nContact: Lian Zhao \nAbstract: In this presentation\, we introduce media access control (MAC) protocol design for two IoT applications\, i.e.\, vehicle-to-vehicle (V2V) safety message broadcast in connected vehicles and smart factory in industry IoT. For each considered application\, we investigate its unique communication characteristics\, design our MAC protocol accordingly\, and model and analyze the performance of the proposed design. For the V2V safety message broadcast\, we develop a fully distributed MAC that achieves substantially lower delay and collision probability compared to existing distributed MAC designs. For the smart factory application\, we develop a centralized MAC that can support a large number of devices with a single communication channel while satisfying stringent delay and collision requirements. Through the two examples\, we demonstrate the potential of MAC design innovations in supporting emerging IoT applications. \nRegister: Please visit https://events.vtools.ieee.org/m/237813 for more details and to register.
URL:https://www.ieeetoronto.ca/event/mac-protocol-design-for-iot-applications/
LOCATION:Virtual – Zoom
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200824T183000
DTEND;TZID=America/Toronto:20200824T203000
DTSTAMP:20260415T181921
CREATED:20210430T023713Z
LAST-MODIFIED:20210430T235432Z
UID:10000328-1598293800-1598301000@www.ieeetoronto.ca
SUMMARY:2D Game Development in Unity with C# - Session 5
DESCRIPTION:On Monday\, August 24\, 2020 at 6:30 p.m.\, IEEE Ryerson Computational Intelligence Chapter will be hosting “2D Game Development in Unity with C# – Session 5”. \nDay & Time: Monday\, August 24\, 2020\n6:30 p.m. ‐ 8:30 p.m. \nSpeaker: Steven Medeot \nOrganizers: IEEE Ryerson Computational Intelligence Chapter\, IEEE Toronto WIE \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nAbstract: Our interactive workshop welcomes new and experienced programmers who are interested in 2D game development.  This event hosted by IEEE Ryerson Computational Intelligence Chapter is sponsored by IEEE WIE and will provide the building blocks and best practices in developing a 2D level game including\, creating a player\, creating enemies\, game loops\, animations\, and more!  All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a friendly competition with small prizes at the end of the workshop series. \nSession 5 focuses on polishing your 2D game. \nRegister: https://forms.gle/VvZW3oeZ81UCtgnX7 \nBiography: Steven Medeot is a 3rd-year Computer Science Student at Ryerson University. He has a background in Game Development\, who completed the Game Programming curriculum at George Brown College with a few years of experience working in this industry and enjoys developing his own games on the side. He strongly believes that creating a game that people can find joy in is a wonderful experience and wants to share some of the basic knowledge he has learned throughout the years.
URL:https://www.ieeetoronto.ca/event/2d-game-development-in-unity-with-c-session-5/
LOCATION:Online via Zoom
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200817T183000
DTEND;TZID=America/Toronto:20200817T203000
DTSTAMP:20260415T181921
CREATED:20210430T023542Z
LAST-MODIFIED:20210430T235109Z
UID:10000325-1597689000-1597696200@www.ieeetoronto.ca
SUMMARY:2D Game Development in Unity with C# - Session 4
DESCRIPTION:On Monday\, August 17\, 2020 at 6:30 p.m.\, IEEE Ryerson Computational Intelligence Chapter will be hosting “2D Game Development in Unity with C# – Session 4”. \nDay & Time: Monday\, August 17\, 2020\n6:30 p.m. ‐ 8:30 p.m. \nSpeaker: Steven Medeot \nOrganizers: IEEE Ryerson Computational Intelligence Chapter\, IEEE Toronto WIE \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nAbstract: Our interactive workshop welcomes new and experienced programmers who are interested in 2D game development.  This event hosted by IEEE Ryerson Computational Intelligence Chapter is sponsored by IEEE WIE and will provide the building blocks and best practices in developing a 2D level game including\, creating a player\, creating enemies\, game loops\, animations\, and more!  All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a friendly competition with small prizes at the end of the workshop series. \nSession 4 explores animations and the associated features in animating your player/enemies. \nRegister: https://forms.gle/VvZW3oeZ81UCtgnX7 \nBiography: Steven Medeot is a 3rd-year Computer Science Student at Ryerson University. He has a background in Game Development\, who completed the Game Programming curriculum at George Brown College with a few years of experience working in this industry and enjoys developing his own games on the side. He strongly believes that creating a game that people can find joy in is a wonderful experience and wants to share some of the basic knowledge he has learned throughout the years.
URL:https://www.ieeetoronto.ca/event/2d-game-development-in-unity-with-c-session-4/
LOCATION:Online via Zoom
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200810T183000
DTEND;TZID=America/Toronto:20200810T203000
DTSTAMP:20260415T181921
CREATED:20210430T023541Z
LAST-MODIFIED:20210430T234952Z
UID:10000322-1597084200-1597091400@www.ieeetoronto.ca
SUMMARY:2D Game Development in Unity with C# - Session 3
DESCRIPTION:On Monday\, August 10\, 2020 at 6:30 p.m.\, IEEE Ryerson Computational Intelligence Chapter will be hosting “2D Game Development in Unity with C# – Session 3”. \nDay & Time: Monday\, August 10\, 2020\n6:30 p.m. ‐ 8:30 p.m. \nSpeaker: Steven Medeot \nOrganizers: IEEE Ryerson Computational Intelligence Chapter\, IEEE Toronto WIE \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nAbstract: Our interactive workshop welcomes new and experienced programmers who are interested in 2D game development.  This event hosted by IEEE Ryerson Computational Intelligence Chapter is sponsored by IEEE WIE and will provide the building blocks and best practices in developing a 2D level game including\, creating a player\, creating enemies\, game loops\, animations\, and more!  All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a friendly competition with small prizes at the end of the workshop series. \nSession 3 teaches the concept of game loops and scenes. \nRegister: https://forms.gle/VvZW3oeZ81UCtgnX7 \nBiography: Steven Medeot is a 3rd-year Computer Science Student at Ryerson University. He has a background in Game Development\, who completed the Game Programming curriculum at George Brown College with a few years of experience working in this industry and enjoys developing his own games on the side. He strongly believes that creating a game that people can find joy in is a wonderful experience and wants to share some of the basic knowledge he has learned throughout the years.
URL:https://www.ieeetoronto.ca/event/2d-game-development-in-unity-with-c-session-3/
LOCATION:Online via Zoom
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200803T183000
DTEND;TZID=America/Toronto:20200803T203000
DTSTAMP:20260415T181921
CREATED:20210430T023539Z
LAST-MODIFIED:20210430T234920Z
UID:10000314-1596479400-1596486600@www.ieeetoronto.ca
SUMMARY:2D Game Development in Unity with C# - Session 2
DESCRIPTION:On Monday\, August 3\, 2020 at 6:30 p.m.\, IEEE Ryerson Computational Intelligence Chapter will be hosting “2D Game Development in Unity with C# – Session 2”. \nDay & Time: Monday\, August 3\, 2020\n6:30 p.m. ‐ 8:30 p.m. \nSpeaker: Steven Medeot \nOrganizers: IEEE Ryerson Computational Intelligence Chapter\, IEEE Toronto WIE \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nAbstract: Our interactive workshop welcomes new and experienced programmers who are interested in 2D game development.  This event hosted by IEEE Ryerson Computational Intelligence Chapter is sponsored by IEEE WIE and will provide the building blocks and best practices in developing a 2D level game including\, creating a player\, creating enemies\, game loops\, animations\, and more!  All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a friendly competition with small prizes at the end of the workshop series. \nSession 2 of the 2D Game Development workshop series explores interfaces and interactability. \nRegister: https://forms.gle/VvZW3oeZ81UCtgnX7 \nBiography: Steven Medeot is a 3rd-year Computer Science Student at Ryerson University. He has a background in Game Development\, who completed the Game Programming curriculum at George Brown College with a few years of experience working in this industry and enjoys developing his own games on the side. He strongly believes that creating a game that people can find joy in is a wonderful experience and wants to share some of the basic knowledge he has learned throughout the years.
URL:https://www.ieeetoronto.ca/event/2d-game-development-in-unity-with-c-session-2/
LOCATION:Online via Zoom
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200729T180000
DTEND;TZID=America/Toronto:20200729T200000
DTSTAMP:20260415T181921
CREATED:20210430T023538Z
LAST-MODIFIED:20210430T234634Z
UID:10000310-1596045600-1596052800@www.ieeetoronto.ca
SUMMARY:Introduction to NLP for Classification Task – Session 4
DESCRIPTION:On Wednesday\, July 29\, 2020 at 6:00 p.m.\, IEEE Toronto WIE\, Computational Intelligence Society\, and IM/RA will be hosting “Introduction to Natural Language Processing (NLP) for Classification Task – Session 4”. \nDay & Time: Wednesday\, July 29\, 2020\n6:00 p.m. ‐ 8:00 p.m. \nOrganizers: IEEE Toronto WIE\, Computational Intelligence Society\, IM/RA Society \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi\, Younes Sadat Nejad \nAbstract: Introduction to Natural Language Processing (NLP) for Classification Task is a series of workshops hosted by IEEE Toronto Section\, WIE\, Computational Intelligence Society\, Instrumentation Measurement/Robotics Automation Chapter and Ryerson Advanced AI lab. \nOur main goal is to get started on NLP classification tasks for competition and explore duplicate question detection and sentiment analysis tasks. \nIn this session\, we will be focusing on RNN and LSTM. \nRegister: Please visit https://events.vtools.ieee.org/m/236479 or https://events.vtools.ieee.org/m/236480 for more details and to register.
URL:https://www.ieeetoronto.ca/event/introduction-to-nlp-for-classification-task-session-4/
LOCATION:Online via Zoom Toronto\, Ontario Canada
CATEGORIES:Communications,Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200727T183000
DTEND;TZID=America/Toronto:20200727T203000
DTSTAMP:20260415T181921
CREATED:20210430T023537Z
LAST-MODIFIED:20210430T234533Z
UID:10000309-1595874600-1595881800@www.ieeetoronto.ca
SUMMARY:2D Game Development in Unity with C# - Session 1
DESCRIPTION:On Monday\, July 27\, 2020 at 6:30 p.m.\, IEEE Ryerson Computational Intelligence Chapter will be hosting “2D Game Development in Unity with C# – Session 1”. \nDay & Time: Monday\, July 27\, 2020\n6:30 p.m. ‐ 8:30 p.m. \nSpeaker: Steven Medeot \nOrganizers: IEEE Ryerson Computational Intelligence Chapter\, IEEE Toronto WIE \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nAbstract: Our interactive workshop welcomes new and experienced programmers who are interested in 2D game development. This event hosted by IEEE Ryerson Computational Intelligence Chapter is sponsored by IEEE WIE and will provide the building blocks and best practices in developing a 2D level game including\, creating a player\, creating enemies\, game loops\, animations\, and more! All who attend all five sessions will get a certificate from IEEE WIE and can submit their 2D game into a friendly competition with small prizes at the end of the workshop series. \nIn our first session\, we will review basic programming concepts\, object-oriented programming\, and introduce best practices working with C# in the Unity environment. \nRegister: https://forms.gle/VvZW3oeZ81UCtgnX7 \nBiography: Steven Medeot is a 3rd-year Computer Science Student at Ryerson University. He has a background in Game Development\, who completed the Game Programming curriculum at George Brown College with a few years of experience working in this industry and enjoys developing his own games on the side. He strongly believes that creating a game that people can find joy in is a wonderful experience and wants to share some of the basic knowledge he has learned throughout the years.
URL:https://www.ieeetoronto.ca/event/2d-game-development-in-unity-with-c-session-1/
LOCATION:Online via Zoom
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200723T130000
DTEND;TZID=America/Toronto:20200723T153000
DTSTAMP:20260415T181921
CREATED:20210430T023536Z
LAST-MODIFIED:20210430T234342Z
UID:10000307-1595509200-1595518200@www.ieeetoronto.ca
SUMMARY:Advanced Topics on Scalable Deployment of Machine Learning and Drone-Based Search and Rescue
DESCRIPTION:On Thursday\, July 23\, 2020 at 1:00 p.m.\, Dalia Hanna and Mujahid Sultan will be presenting “Advanced Topics on Scalable Deployment of Machine Learning and Drone-Based Search and Rescue”. \nDay & Time: Thursday\, July 23\, 2020\n1:00 p.m. – 4:00 p.m. \nSpeakers: Dalia Hanna\, Mujahid Sultan \n\n\nOrganizers: IEEE Toronto WIE\, IEEE IM/RA\, Ryerson CS Graduate Student Council\, IEEE Ryerson Computational Intelligence Chapter\, Ryerson CSCU \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi \nTitle: Factors affecting the Automation of the Search and Rescue Operations: An Algorithm on Finding Missing Lost Persons Living with Dementia \nAbstract: Unmanned Aerial Vehicles (UAV) are now used in many applications. The focus in this presentation is on their use in public safety\, specifically in search and rescue (SAR) operations involving lost persons living with dementia (LPLWD). When it comes to saving lives\, there are many human factors associated with UAV operations that impact the performance of expert human SAR teams that could be improved through forms of automation. These include familiarity with the search location\, tasks associated with piloting and search/flight management during SAR operations.  A LPLWD may not be interested in assisting in their own rescue as they may not know they are lost. As such\, it has been observed that they tend to keep walking until they are faced with an obstacle that bars their further progress. The approach presented in this research work focuses on developing a people finding algorithm to identify higher probability locations where an LPLWD might be found\, through informed\, behavior-based analysis of the search location; then\, developing an algorithm to fly a UAV to the vicinity of these higher probability locations.  The algorithm was tested and validated through field testing. The results from both the data collection process and the field tests indicated that there are efficiencies in using the drone\, which enhances the probability of finding the lost person alive.  An informed cleaning process involving both manual and ‘R’-automated approaches to scrub and augment the data–adding any missing values in the dataset\, helped in understanding the behaviour of the lost person and in determining what significant variables enhanced their survivability. Linear regression was utilized to acquire the correlation among the numeric values in the database. The analysis indicated that there was no significant correlation among the independent variables; however\, the data indicated that the wanderer tended to be found closer to where they left or were last seen. Logistic regression was used to investigate the survivability using three classification models. Finally\, a framework is presented considering all the factors form the field tests and data analysis. \n\nTitle: How to build and deploy machine learning models in the scalable cloud  \nAbstract: Machine learning model development is a skill taught at schools and is a good skill to have but where most of the student’s lake is how to serve these models to the clients. How to scale. Make sure that the server does not die if it gets a million hits in a second. How to build security around it. \nAgenda: Interested students who want to build along with me\, can bring their laptop with MobaXterm installed and we can do the following together. \n\nlogin to a cloud environment (I will provide the cloud login credentials during the presentation)\ncreate a virtual environment for development\nbuild a semantic search engineby pulling libraries from the net\npick a visualization and presentation method from D3JS\ndevelop an application using MVC pattern like the flask\nwrap the application in a docker container\ninstall scalable web engine like NGINX\nhost it to the cloud (azure)\nprovide secure access with a username and password to anyone on the internet\n\nThis presentation will expose the tools required to build scalable machine learning applications in the cloud. \n\nRegistration: Please visit https://forms.gle/7ZoimYgVjjpC9mag8 to register. \nBiographies: \nDalia Hanna\nTopic: Factors affecting the Automation of the Search and Rescue Operations: An Algorithm on Finding Missing Lost Persons Living with Dementia \nDalia Hanna is a PhD Candidate in the Department of Computer Science\, Ryerson University. She is a member of Ryerson’s Network-Centric Applied Research Lab\, a multidisciplinary Computational Public Safety-focused research lab. She has a B.Sc. in Electronics and Communication Engineering and M.Sc. in Instructional Design and Technology with a specialization in Online Learning. Dalia is also a certified project management professional (PMP ® ) and a certified facilitator. Her research interest in utilizing technology tools for public safety\, search and rescue\, and emergency management operations. . Dalia authored several research papers and presented in national and international conferences. \nMujahid Sultan\nTopic: Factors affecting the Automation of the Search and Rescue Operations: An Algorithm on Finding Missing Lost Persons Living with Dementia \nMujahid Sultan is a senior computer scientist and enterprise architect with vast experience in machine learning\, pattern recognition\, deep learning\, NLP\, text synthesis\, transcription\, time-series forecasting and cloud-native developments (Python\, microservices\, APIs\, Docker\, Kubernetes). His current research focus: a) working to develop a robust clustering method with mathematical proofs b) improving learning from imbalanced data on graph-based deep learning backends (TensorFlow\, Torch and CNTK)\, and c) building Machine Learning based dynamic SDN controllers. \nHe has authored in high impact journals in fields of Machine Learning\, Artificial Intelligence\, Data Visualization\, Genetics and Drug Discovery for Cancer\, Requirements Engineering and Enterprise Architecture. His publications can be found at https://orcid.org/0000-0001-6721-4044 \nAreas of Expertise include: Regression\, Clustering\, Classification\, Deep Learning\, Convolutional and Recurrent Neural Networks (LSTMs)\, Natural Language Processing (NLP)\, Self-Organizing Maps (SOM)\, Topic Modeling and Parallel Processing. Expert in info visualization using matlab\, matplotlib\, D3js and plotly. \nSkills: Full-stack development: (Angular+Flask+Docker); Python: (Scikit-Learn\, Keras\, TensorFlow\, NLTK\, Spacy\, NumPy\, Matplotlib\, SpaCy to name a few); MATLAB: (toolboxes: statistics\, microeconomics\, parallel processing\, bioinformatics to name a few). \nPlatform experience: Docker Containers and Kubernetes on AWS\, Azure/Azure Stack and Google Cloud Platform. PaaS/IaaS: (AWS: (Elastic Beanstalk\, Lambda\, Poly\, Sage-Maker)\, Azure ML\, and Heroku).
URL:https://www.ieeetoronto.ca/event/advanced-topics-on-scalable-deployment-of-machine-learning-and-drone-based-search-and-rescue/
LOCATION:Online via Zoom Toronto\, Ontario Canada
CATEGORIES:Computer,Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200715T180000
DTEND;TZID=America/Toronto:20200715T200000
DTSTAMP:20260415T181921
CREATED:20210430T023536Z
LAST-MODIFIED:20210430T234230Z
UID:10000306-1594836000-1594843200@www.ieeetoronto.ca
SUMMARY:Introduction to NLP for Classification Task - Session 2
DESCRIPTION:Recorded Material:\nVideo: https://drive.google.com/file/d/1gBUK_NtU3kSNblsGaYouLHyfDHlxr1tt/view\nPowerPoint: 2.IntroductiontoNLP\,Kagle \nOn Wednesday\, July 15\, 2020 at 6:00 p.m.\, IEEE Toronto WIE and Computational Intelligence Society will be hosting “Introduction to Natural Language Processing (NLP) for Classification Task – Session 2”. \nDay & Time: Wednesday\, July 15\, 2020\n6:00 p.m. ‐ 8:00 p.m. \nOrganizers: IEEE Toronto WIE\, Computational Intelligence Society \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi\, Younes Sadat Nejad \nAbstract: Introduction to Natural Language Processing (NLP) for Classification Task is a series of workshops hosted by IEEE Toronto Section\, WIE\, Computational Intelligence Society\, Instrumentation Measurement/Robotics Automation Chapter and Ryerson Advanced AI lab. \nOur main goal is to get started on NLP classification tasks for competition and explore duplicate question detection and sentiment analysis tasks. \nIn the second session\, we will introduce the concept of deep learning\, and then specifically focus on Natural Language Process. We will also introduce Kaggle Account as an environment for python coding. \nRegister: Please visit https://events.vtools.ieee.org/m/235444 or https://events.vtools.ieee.org/m/235447 for more details and to register.
URL:https://www.ieeetoronto.ca/event/introduction-to-nlp-for-classification-task-session-2/
LOCATION:Online via Zoom
CATEGORIES:Communications,Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20200708T180000
DTEND;TZID=America/Toronto:20200708T193000
DTSTAMP:20260415T181921
CREATED:20210430T023534Z
LAST-MODIFIED:20210430T234017Z
UID:10000304-1594231200-1594236600@www.ieeetoronto.ca
SUMMARY:Introduction to NLP for Classification Task – Session 1
DESCRIPTION:Recorded Material:\nVideo: https://drive.google.com/file/d/1gBUK_NtU3kSNblsGaYouLHyfDHlxr1tt/view?usp=sharing\nPowerPoint: 1-Intro to Python\, Data Science Libraries\, and Pytorch \nOn Wednesday\, July 8\, 2020 at 6:00 p.m.\, IEEE Toronto WIE and Computational Intelligence Society will be hosting “Introduction to Natural Language Processing (NLP) for Classification Task – Session 1”. \nDay & Time: Wednesday\, July 8\, 2020\n6:00 p.m. ‐ 7:30 p.m. \nOrganizers: IEEE Toronto WIE\, Computational Intelligence Society \nLocation: Virtual – Zoom \nContact: Ayda Naserialiabadi\, Younes Sadat Nejad \nAbstract: Introduction to Natural Language Processing (NLP) for Classification Task is a series of workshops hosted by IEEE Toronto Section\, WIE\, Computational Intelligence Society\, Instrumentation Measurement/Robotics Automation Chapter and Ryerson Advanced AI lab. \nOur main goal is to get started on NLP classification tasks for competition and explore duplicate question detection and sentiment analysis tasks. In session 1\, we will be covering the introduction to Python\, Data Science Libraries and Pytorch. \nRegister: Please visit https://events.vtools.ieee.org/m/233944 or https://events.vtools.ieee.org/m/233942 for more details and to register.
URL:https://www.ieeetoronto.ca/event/introduction-to-nlp-for-classification-task-session-1/
CATEGORIES:Communications,Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20180312T180000
DTEND;TZID=America/Toronto:20180312T200000
DTSTAMP:20260415T181921
CREATED:20210430T014017Z
LAST-MODIFIED:20210430T221025Z
UID:10000194-1520877600-1520884800@www.ieeetoronto.ca
SUMMARY:IEEE Ryerson Python Workshop 4
DESCRIPTION: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. \nDay & Time: Monday\, March 12\, 2018\n6:00 p.m. ‐ 8:00 p.m. \nLocation: Ryerson University (Victoria Building\, Room VIC 301) \nContact: ieee.ryersonu@gmail.com \nOrganizer: IEEE Ryerson Student Branch\, IEEE Ryerson Computer Chapter\, IEEE Computational Intelligence Chapter\, WIE IEEE Toronto\, Instrumentation-Measurement/Robotics-Automation
URL:https://www.ieeetoronto.ca/event/ieee-ryerson-python-workshop-4/
LOCATION:Ryerson University (Victoria Building\, Room VIC 301)
CATEGORIES:Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20180305T180000
DTEND;TZID=America/Toronto:20180305T200000
DTSTAMP:20260415T181921
CREATED:20210430T014016Z
LAST-MODIFIED:20210430T220723Z
UID:10000191-1520272800-1520280000@www.ieeetoronto.ca
SUMMARY:IEEE Ryerson Python Workshop 3
DESCRIPTION: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. \nDay & Time: Monday\, March 5\, 2018\n6:00 p.m. ‐ 8:00 p.m. \nLocation: Ryerson University (Victoria Building\, Room VIC 301) \nContact: ieee.ryersonu@gmail.com \nOrganizer: IEEE Ryerson Student Branch\, IEEE Ryerson Computer Chapter\, IEEE Computational Intelligence Chapter\, WIE IEEE Toronto\, Instrumentation-Measurement/Robotics-Automation \nRegister at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-3-tickets-43189931247
URL:https://www.ieeetoronto.ca/event/ieee-ryerson-python-workshop-3/
LOCATION:Ryerson University (Victoria Building\, Room VIC 301)
CATEGORIES:Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20180212T180000
DTEND;TZID=America/Toronto:20180212T200000
DTSTAMP:20260415T181921
CREATED:20210430T014014Z
LAST-MODIFIED:20210430T220335Z
UID:10000181-1518458400-1518465600@www.ieeetoronto.ca
SUMMARY:IEEE Ryerson Python Workshop 2
DESCRIPTION: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. \nDay & Time: Monday\, February 12\, 2018\n6:00 p.m. ‐ 8:00 p.m. \nLocation: Ryerson University (Victoria Building\, Room VIC 301) \nContact: ieee.ryersonu@gmail.com \nOrganizer: IEEE Ryerson Student Branch\, IEEE Ryerson Computer Chapter\, IEEE Computational Intelligence Chapter\, WIE IEEE Toronto\, Instrumentation-Measurement/Robotics-Automation \nRegister at: https://www.eventbrite.com/e/ieee-ryerson-python-workshop-2-tickets-42931234478
URL:https://www.ieeetoronto.ca/event/ieee-ryerson-python-workshop-2/
LOCATION:Ryerson University (Victoria Building\, Room VIC 301)
CATEGORIES:Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20180205T180000
DTEND;TZID=America/Toronto:20180205T200000
DTSTAMP:20260415T181921
CREATED:20210430T012929Z
LAST-MODIFIED:20210430T220150Z
UID:10000175-1517853600-1517860800@www.ieeetoronto.ca
SUMMARY:Introduction to Python Workshop
DESCRIPTION: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. \nDay & Time: Monday\, February 5\, 2018\n6:00 p.m. ‐ 8:00 p.m. \nLocation: Ryerson University (Victoria Building\, Room VIC 301) \nContact: ieee.ryersonu@gmail.com \nOrganizer: IEEE Ryerson Student Branch\, IEEE Ryerson Computer Chapter\, IEEE Computational Intelligence Chapter\, WIE IEEE Toronto\, Instrumentation-Measurement/Robotics-Automation \nRVSP: https://www.eventbrite.com/e/ieee-ryerson-intro-to-python-workshop-tickets-42588313793
URL:https://www.ieeetoronto.ca/event/introduction-to-python-workshop/
LOCATION:Ryerson University (Victoria Building\, Room VIC 301)
CATEGORIES:Instrumentation & Measurement,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20171127T103000
DTEND;TZID=America/Toronto:20171127T113000
DTSTAMP:20260415T181921
CREATED:20210430T012927Z
LAST-MODIFIED:20210430T215717Z
UID:10000165-1511778600-1511782200@www.ieeetoronto.ca
SUMMARY:Why Deep Learning Works So Well?
DESCRIPTION: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?”. \nDay & Time: Monday\, November 27\, 2017\n10:30 a.m. ‐ 11:30 a.m. \nSpeaker: Prof. C.-C. Jay Kuo\, Fellow of IEEE\, AAAS\, SPIE\nDean’s Professor in Electrical Engineering-Systems\, University of Southern California \nLocation: Room ENG 358\nGeorge Vari Engineering Building (Intersection of Church & Gould)\nRyerson University\n245 Church St\, Toronto\, M5B 1Z4 \nContact: Xiao-Ping Zhang\, Alireza Sadeghian\, Alex Dela Cruz \nOrganizer: Electrical and Computer Engineering and CASPAL Ryerson\nSignals & Computational Intelligence Chapter \nAbstract: 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/why-deep-learning-works-so-well/
LOCATION:Room ENG 358\, 245 Church St\, Toronto\, M5B 1Z4
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20171121T170000
DTEND;TZID=America/Toronto:20171121T180000
DTSTAMP:20260415T181921
CREATED:20210430T012926Z
LAST-MODIFIED:20210430T215658Z
UID:10000164-1511283600-1511287200@www.ieeetoronto.ca
SUMMARY:Data-Driven Care: Enabling Science and Technologies
DESCRIPTION: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”. \nDay & Time: Tuesday November 21st\, 2017\n5:00 p.m. – 6:00 p.m. \nSpeaker: Dr. Philip Asare\nAssistant Professor of Electrical and Computer Engineering\nSwanson Fellow in Sciences and Engineering\nMulticultural Student Services Faculty Fellow (Fall 2015)\nBucknell University \nLocation: Room ENG-LG 12\nGeorge Vari Engineering Building (Intersection of Church & Gould)\nRyerson University\n245 Church St\, Toronto\, M5B 1Z4 \nContact: Alireza Sadeghian\, Alex Dela Cruz \nOrganizer: Signals & Computational Intelligence Chapter \nAbstract: 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. \nBiography: 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).
URL:https://www.ieeetoronto.ca/event/data-driven-care-enabling-science-and-technologies/
LOCATION:Room ENG-LG 12\, George Vari Engineering Building\, Ryerson University
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170908T100000
DTEND;TZID=America/Toronto:20170908T110000
DTSTAMP:20260415T181921
CREATED:20210430T012919Z
LAST-MODIFIED:20210430T212408Z
UID:10000135-1504864800-1504868400@www.ieeetoronto.ca
SUMMARY:On System-Level Analysis & Design of Cellular Networks: The Magic of Stochastic Geometry
DESCRIPTION: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”. \nDay & Time: Friday September 8\, 2017\n10:00 a.m. – 11:00 a.m. \nSpeaker: Professor Marco Di Renzo\nParis-Saclay University/CNRS\, France \nLocation: Room ENG288\nGeorge Vari Engineering Building (Intersection of Church & Gould)\nRyerson University\n245 Church St\, Toronto\, M5B 1Z4 \nContact: Alireza Sadeghian\, Alex Dela Cruz \nOrganizers: Signals & Computational Intelligence Chapter \nAbstract: 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/on-system-level-analysis-design-of-cellular-networks-the-magic-of-stochastic-geometry/
LOCATION:Room ENG288\, George Vari Engineering Building\, 245 Church St\, Toronto\, M5B 1Z4
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170717T160000
DTEND;TZID=America/Toronto:20170717T170000
DTSTAMP:20260415T181921
CREATED:20210430T012918Z
LAST-MODIFIED:20210430T212205Z
UID:10000131-1500307200-1500310800@www.ieeetoronto.ca
SUMMARY:A framework for general purpose digital pathology image analysis\, using machine learning methods to identify cancer subsets and immunotherapy biomarkers
DESCRIPTION: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”. \nDay & Time: Monday July 17\, 2017\n4:00 p.m. – 5:00 p.m. \nSpeaker: Dr. Trevor McKee\nSTTARR – Innovation Research Centre for Cancer Research\nToronto\, Ontario\, Canada \nLocation: Room ENG101\nGeorge Vari Engineering Building (intersection of Church & Gould)\nRyerson University\n245 Church St\, Toronto\, M5B 1Z4 \nContact: Alireza Sadeghian\, Alex Dela Cruz \nOrganizers: Signals & Computational Intelligence Chapter \nAbstract: 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. \nThe 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. \nThe 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/a-framework-for-general-purpose-digital-pathology-image-analysis-using-machine-learning-methods-to-identify-cancer-subsets-and-immunotherapy-biomarkers/
LOCATION:Room ENG101\, 245 Church St\, Toronto\, M5B 1Z4
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170628T170000
DTEND;TZID=America/Toronto:20170628T180000
DTSTAMP:20260415T181921
CREATED:20210430T012917Z
LAST-MODIFIED:20210430T212051Z
UID:10000129-1498669200-1498672800@www.ieeetoronto.ca
SUMMARY:Large-Scale Analytics and Machine Learning for Biomedical Data Types
DESCRIPTION: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”. \nDay & Time: Wednesday June 28\, 2017\n5:00 p.m. – 6:00 p.m. \nSpeaker: Dr. Shiva Amiri\nCEO of BioSymetrics Inc\nToronto\, Ontario\, Canada \nLocation: Room ENG288\nDepartment of Computer Science\nRyerson University\n245 Church St\, Toronto\, M5B 1Z4 \nContact: Alireza Sadeghian\, Alex Dela Cruz \nOrganizers: Signals & Computational Intelligence Chapter\, WIE \nAbstract: 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. \nChallenges 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. \nThe 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. \nThe 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. \nBiography: 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.
URL:https://www.ieeetoronto.ca/event/large-scale-analytics-and-machine-learning-for-biomedical-data-types/
LOCATION:Room ENG288\, 245 Church St\, Toronto\, M5B 1Z4
CATEGORIES:Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20170428T090000
DTEND;TZID=America/Toronto:20170428T160000
DTSTAMP:20260415T181921
CREATED:20210430T012914Z
LAST-MODIFIED:20210430T211027Z
UID:10000126-1493370000-1493395200@www.ieeetoronto.ca
SUMMARY:Engineering the Internet of Things – Digital Twin Seminar
DESCRIPTION:Friday April 28\, 2017 at 9:00 a.m. IEEE Toronto and SimuTech Group will be hosting the seminar “Engineering the Internet of Things – Digital Twin”. \nDay & Time: Friday April 28\, 2017\n9:00 a.m. – 4:00 p.m. \nLocation: Ryerson University\nGeorge Vari Centre for Computing and Engineering\nRoom: ENG 288\n245 Church Street\nToronto\, Ontario M5B 2K3 \nCost: Free including lunch \nRegister: http://go.simutechgroup.com/ieee-iot-digital-twin-toronto \nContact: SimuTech Group – Mohsen Tayefeh\nIEEE Toronto – Dr. Maryam Davoudpour \nOrganizers: IEEE Toronto (WIE\, Signals & Computational Intelligence\, Measurement/Instrumentation-Robotics\, Magnetics chapters)\, Computer Science Department of Ryerson University\, SimuTech Group (ANSYS Elite Channel partner) \nAbstract: High-tech–industry product development teams routinely use coupled multiphysics software to analyze the trade-offs among speed\, bandwidth\, signal integrity\, power integrity\, thermal performance and EMI/EMC. \nThe Internet of Things is a network of smart products\, or “things”\, that use embedded sensors\, software\, and electronics to communicate with each other over a network. The communication data can be analyzed by cloud based software to derive actionable information\, leading to predictive and prescriptive outcomes. \nIn this seminar\, the following topics will be discussed: \n– Engineering the Internet of Things\n– 5 Engineering Challenges for Smart Product Development\n– Case Study: Search and Rescue Drone-Satellite System\n– Signal Integrity/EMI/EMC\, Human body\, Federal Regulations\n– User experience – Wearable devices (Multiphysics Simulation)\n– Digital Twin – GE and ANSYS collaboration\n– Case Study: prescriptive maintenance case study\n– Lunch\n– RF Antenna placement\n– Step by step workshop – Antenna analysis\n– PCB design – Power Integrity\n– Thermal management (CFD)\n– Networking\, Door prize/draw (Drone)
URL:https://www.ieeetoronto.ca/event/engineering-the-internet-of-things-digital-twin-seminar/
LOCATION:George Vari Centre for Computing and Engineering Room: ENG 288\, 245 Church Street\, Toronto
CATEGORIES:Instrumentation & Measurement,Magnetics,Signals & Computational Intelligence,Women in Engineering
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20161129T143000
DTEND;TZID=America/Toronto:20161129T153000
DTSTAMP:20260415T181921
CREATED:20210430T002609Z
LAST-MODIFIED:20210430T005331Z
UID:10000093-1480429800-1480433400@www.ieeetoronto.ca
SUMMARY:Developing Wearable Technologies for improved management of sleep-related breathing disorders
DESCRIPTION:Tuesday November 29th\, 2016 at 2:30 p.m. Dr. Azadeh Yadollahi\, Scientist at SleepdB Laboratory and Assistant Professor at University of Toronto\, will be presenting “Developing Wearable Technologies for improved management of sleep-related breathing disorders”. \nSpeaker: Dr. Azadeh Yadollahi\nScientist\, SleepdB Laboratory\, Toronto Rehabilitation Institute\nAssistant Professor\, Biomaterial & Biomedical Engineering\, University of Toronto\nAdjunct Faculty\, Department of Biomedical Engineering\, University of Manitoba \nDay & Time: Tuesday\, November 29th\, 2016\n2:30 p.m. – 3:30 p.m. \nLocation: Room ENG-460\n245 Church Street\, Toronto\, ON\nRyerson University \nOrganizer: IEEE Signal Processing Chapter Toronto Section \nContact: Mehrnaz Shokrollahi \nAbstract: Over four million Canadians live with a chronic respiratory disease such as asthma\, chronic obstructive pulmonary disease (COPD) or obstructive sleep apnea (OSA)—all of which are associated with high morbidity. In Canada\, 6.5% of total health care costs are related to these disorders\, amounting to $5.7B in direct and $6.72B in indirect costs per year. Moreover\, the overlap between asthma\, COPD\, and OSA is common\, is clinically important\, worsens quality of life\, and is associated with greater morbidity and mortality more than the sum of the contributing disorders. A feature common to chronic respiratory diseases is that their symptoms\, eg. shortness of breath\, worsen during sleep. Most emergency visits and deaths related to asthma and COPD occur during the night. However\, our understanding of the mechanisms of respiratory disorders exacerbation at night is limited; which consequently challenges our ability to manage these disorders. One of the main barriers to determine the underlying pathophysiology of sleep-related respiratory disorders is that the available technologies to perform studies are expensive\, invasive\, and confound normal breathing and sleep patterns. Therefore\, the results may not be applicable to a wide range of people or over a long period of time to evaluate treatments and interventions. Therefore\, the mechanistic link between sleep and respiratory disease\, particularly the role of night-time fluid redistribution\, is not well understood. To address this gap\, my team is developing novel technologies to monitor respiratory related physiological signals during sleep\, as well and technologies to non-invasively assess tissue composition\, and its role on the pathophysiology of sleep related breathing disorders. \nBiography: Dr. Azadeh Yadollahi is a Scientist at the Toronto Rehabilitation Institute – University Health Network\, where she leads the SleepdB laboratory. She is also an Assistant Professor in the Institute of Biomaterial and Biomedical Engineering\, University of Toronto and Adjunct Faculty Member in the Graduate Department of Biomedical Engineering at the University of Manitoba. Her research aims to determine the pathophysiology of sleep-related breathing disorders and to develop novel technologies for improved management of these disorders. She is particularly interested in developing innovative technologies for monitoring of physiological signals at home and implementing personalized treatments for older populations with chronic sleep-related respiratory diseases. To date\, Dr. Yadollahi has authored and co-authored more than 30 peer-reviewed publications\, had more than 60 presentations at national and international conferences\, and been invited 26 times to give presentations on her research at prominent national and international academic institutions. Her research is supported by grants from the Canada Foundation for Innovation\, Natural Sciences and Engineering Research Council of Canada (NSERC)\, Canadian Respiratory Research Network\, and Ontario Centres of Excellence\, among others. In the past 10 years\, Dr. Yadollahi has been instrumental in developing new wearable technologies for improved diagnosis and treatment of breathing disorders during sleep. At Toronto Rehab\, Dr. Yadollahi is leading SleepdB\, a Sound-proof laboratory to examine sleep-disordered Breathing. SleepdB is the first laboratory in the world dedicated to understanding the mechanisms of airway narrowing during sleep and to developing acoustic technologies to improve sleep-related respiratory disorders. This laboratory will also serve as a hub for knowledge translation and exchange between researchers and clinicians to advance clinically relevant research and implement cutting-edge assessments and treatments for breathing disorders.
URL:https://www.ieeetoronto.ca/event/developing-wearable-technologies-for-improved-management-of-sleep-related-breathing-disorders/
LOCATION:Room ENG-460\, 245 Church Street\, Toronto\, ON
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20160628T140000
DTEND;TZID=America/Toronto:20160628T160000
DTSTAMP:20260415T181921
CREATED:20210429T230403Z
LAST-MODIFIED:20210430T001609Z
UID:10000044-1467122400-1467129600@www.ieeetoronto.ca
SUMMARY:Ground Truth Bias in External Cluster Validity Indices
DESCRIPTION:June 28\, 2016 at 2:00 p.m. IEEE CIS Distinguished Lecturer James C. Bezdek will be presenting “Ground Truth Bias in External Cluster Validity Indices”. \nSpeaker: James C. Bezdek\nIEEE CIS Distinguished Lecturer \nDay & Time: Tuesday\, June 28\, 2016\n2:00 p.m. – 4:00 p.m. \nLocation: Room ENG 106\, George Vary Engineering & Computing Centre\n245 Church St.\, Toronto\, ON\, M5B 2K3\n(Intersection of Church and Gould) \nMap: http://www.ryerson.ca/maps/ \nContact: Dr. Maryam Davoudpour\, Dr. Glaucio Carvalho\, Dr. Alireza Sadeghian \nOrganizers: Signals & Computational Intelligence Chapter\, Magnetics Chapter\, Instrumentation & Measurement/Robotics & Automation Chapter \nAbstract: This talk begins with a short review of clustering that emphasizes external cluster validity indices (CVIs). A method for generalizing external pairbased CVIS (e.g.\, the crisp Rand and Jacard indices) to evaluate soft partitions is described and illustrated. Three types of validation experiments conducted with synthetic and real world labeled data are discussed: “best c” (internal validation with labeled data)\, and “best I/E” (agreement between an internal and external CVI pair). \nAs is always the case in cluster validity\, conclusions based on empirical evidence are at the mercy of the data\, so the reported results might be invalid for different data sets and/or clustering models and algorithms. But much more importantly\, we discovered during these tests that some external cluster validity indices are also at the mercy of the distribution of the ground truth itself. We believe that our study of this surprising fact is the first systematic analysis of a largely unknown but very important problem ~ bias due to the distribution of the ground truth partition. \nSpecifically\, in addition to the well known bias in many external CVIs caused by monotonic dependency on c\, the number of clusters in candidate partitions\, there are two additional kinds of bias that can be caused by an unusual distribution of the clusters in the ground truth partition provided with labeled data. The most important ground truth bias is caused by imbalance (unequally sized labeled subsets). We demonstrate these effects with randomized experiments on 25 pair-based external CVIs. Then we provide a theoretical analysis of bias due to ground truth for several CVis by relating Rand’s index to the Havrda-Charvat quadratic entropy. \nBiography: Jim received the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society)\, IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society): founding editor the Int’l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium\, IEEE CIS Fuzzy Systems Pioneer\, and IEEE technical field award Rosenblatt medals. Jim’s interests: woodworking\, optimization\, motorcycles\, pattern recognition\, cigars\, clustering in very large data\, fishing\, co-clustering\, blues music\, wireless sensor networks\, poker and visual clustering. And of course\, clustering in big data. Jim retired in 2007\, and will be coming to a university near you soon.
URL:https://www.ieeetoronto.ca/event/ground-truth-bias-in-external-cluster-validity-indices/
LOCATION:ENG 106\, 245 Church Street\, Toronto\, ON
CATEGORIES:Instrumentation & Measurement,Magnetics,Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20160211T130000
DTEND;TZID=America/Toronto:20160211T140000
DTSTAMP:20260415T181921
CREATED:20210429T230400Z
LAST-MODIFIED:20210429T235852Z
UID:10000025-1455195600-1455199200@www.ieeetoronto.ca
SUMMARY:Semi-automated Genome Annotation and an Expanded Epigenetic Alphabet
DESCRIPTION:Thursday February 11th\, 2016 at 1:00 p.m. Michael Hoffman\, Principal Investigator at Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics\, University of Toronto\, will be presenting “Semi-automated genome annotation and an expanded epigenetic alphabet”. \nSpeaker: Michael Hoffman\nPrincipal Investigator at Princess Margaret Cancer Centre\nAssistant Professor in the Departments of Medical Biophysics\, University of Toronto \nDay & Time: Thursday\, February 11\, 2016\n1:00 p.m. – 2:00 p.m. \nLocation: Room LG04\, George Vari Engineering and Computing Centre\nRyerson University\, Toronto\, M5B 1Z4\nPlease check before the seminar \nContact: llivi@scs.ryerson.ca \nAbstract: First\, we will discuss Segway\, an integrative method to identify patterns from multiple functional genomics experiments\, discovering joint patterns across different assay types. We apply Segway to ENCODE ChIP-seq andDNase-seq data and identify patterns associated with transcription start sites\, gene ends\, enhancers\, CTCF elements\, and repressed regions. Segway yields a model which elucidates the relationship between assay observations and functional elements in the genome. \nSecond\, we will discuss a new method to discover transcription factor motifs and identify transcription factor binding sites in DNA with covalent modifications such as methylation. Just as transcription factors distinguish one standard nucleobase from another\, they also distinguish unmodified and modified bases. To represent the modified bases in a sequence\, we replace cytosine (C) with symbols for 5-methylcytosine (5mC)\, 5-hydroxylmethylcytosine (5hmC)\, 5-formylcytosine (5fC). Similarly\, we adapted the well-established position weight matrix model of transcription factor binding affinity to an expanded alphabet. We created an expanded-alphabet genome sequence using genome-wide maps of 5mC\, 5hmC\, and 5fC in mouse embryonic stem cells. Using this sequence and expanded-alphabet position weight matrixes\, we reproduced various known methylation binding preferences\, including the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated motifs. Using these known binding preferences to tune model parameters enables discovery of novel modified motifs. \nBiography: Michael Hoffman is a principal investigator at the Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics and Computer Science\, University of Toronto. He researches the application of machine learning techniques to epigenomic data. He previously led the National Institutes of Health ENCODE Project’s large-scale integration task group while at the University of Washington. He has a PhD from the University of Cambridge\, where he conducted computational genomics studies at the European Bioinformatics Institute. He also has a B.S. in Biochemistry and a B.A. in the Plan II Honors Program at The University of Texas at Austin. He was named a Genome Technology Young Investigator and has received several awards for his academic work\, including a NIH K99/R00 Pathway to Independence Award.
URL:https://www.ieeetoronto.ca/event/semi-automated-genome-annotation-and-an-expanded-epigenetic-alphabet/
LOCATION:Room LG04\, George Vari Engineering and Computing Centre\, Ryerson University\, Toronto
CATEGORIES:Instrumentation & Measurement,Magnetics,Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20151207T160000
DTEND;TZID=America/Toronto:20151207T180000
DTSTAMP:20260415T181921
CREATED:20210429T230358Z
LAST-MODIFIED:20210429T234654Z
UID:10000039-1449504000-1449511200@www.ieeetoronto.ca
SUMMARY:Every Picture Tells a Story: Visual Cluster Assessment in Square and Rectangular Relational Data
DESCRIPTION:Monday December 7\, 2015 at 4:00 p.m. Professor Emeritus James Bezdek will be presenting “Every Picture Tells a Story: Visual Cluster Assessment in Square and Rectangular Relational Data”. \nSpeaker: Emeritus James Bezdek\nPast President of NAFIPS\, IFSA and the IEEE CIS \nDay & Time: Monday\, December 7\, 2015\n4:00 p.m. – 6:00 p.m. \nLocation: Room 1180\nBahen Center for Information Technology\n40 St. George Street\, Toronto \nOrganizer: IEEE Toronto Signals & Computational Intelligence Chapter\nDistinguished Lecturer Program \nContact: Lorenzo Livi\, Email:llivi@scs.ryerson.ca \nAbstract: The VAT/iVAT\, algorithms are the parents of a large family of visual assessment models. \nPart 1. Definitions of the three canonical problems of cluster analysis: tendency assessment\, clustering\, and cluster validity. History of Visual Clustering. Applications: role-based compliance assessment\, eldercare time series data\, and anomaly detection in wireless sensor networks. \nPart 2. Extension to siVAT\, scalable iVAT for big data. This is the basis of clusiVAT and clusiVAT+ for clustering in big data (Topic 4 below). Application: image segmentation. Extension to coiVAT for assessment of co-clustering tendency in the four clustering problems associated with rectangular relational data. Application: response of 18 Fetal Bovine Serum Treatments to the treatment of fibroblasts in gene expression data. \nBiography: Jim received the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society)\, IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society): founding editor the Int’l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium\, IEEE CIS Fuzzy Systems Pioneer\, and IEEE technical field award Rosenblatt medals. Jim’s interests: woodworking\, optimization\, motorcycles\, pattern recognition\, cigars\, clustering in very large data\, fishing\, co-clustering\, blues music\, wireless sensor networks\, poker and visual clustering. And of course\, clustering in big data. Jim retired in 2007\, and will be coming to a university near you soon.
URL:https://www.ieeetoronto.ca/event/every-picture-tells-a-story-visual-cluster-assessment-in-square-and-rectangular-relational-data/
LOCATION:Room 1180\, Bahen Center for Information Technology\, University of Toronto
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Toronto:20151006T110000
DTEND;TZID=America/Toronto:20151006T120000
DTSTAMP:20260415T181921
CREATED:20210429T230355Z
LAST-MODIFIED:20210429T233435Z
UID:10000050-1444129200-1444132800@www.ieeetoronto.ca
SUMMARY:Learning in Non-stationary Environments
DESCRIPTION:October 6\, 2015 at 11:00 a.m. Cesare Alippi\, IEEE Fellow & Professor of Information Processing Systems with the Politecnico di Milano\, will be presenting a distinguished lecture\, “Learning in Non-stationary Environments” at Ryerson University. \nSpeaker: Cesare Alippi\nIEEE Fellow\nProfessor of Information Processing Systems with the Politecnico di Milano \nDay & Time: Tuesday\, October 6\, 2015\n11:00 a.m. – 12:00 p.m. \nLocation: George Vari Centre for Computing and Engineering\nRyerson University\nRoom: ENG287\n245 Church Street\, Toronto\, Ontario M5B 2K3\nClick here to see the Map – Look for ENG \nOrganizer: IEEE Signals & Computational Intelligence Toronto Chapter \nContact: E-mail: Lorenzo Livi \nAbstract: Most of machine learning applications assume the stationarity hypothesis for the process generating the data. This amenable assumption is so widely –and implicitly- accepted that sometimes we even forget that it does not generally hold in the practice due to concept drift (i.e.\, a structural change in the process generating the acquired datastreams). The ability to detect concept drift and react accordingly is hence a major achievement for intelligent learning machines and constitutes one of the hottest research topics for embedded systems. This ability allows the machine for actively tuning the application to maintain high performance\, changing online the operational strategy\, detecting and isolating possible occurring faults to name a few relevant tasks. The talk will focus on “Learning in a non-stationary environments”\, by introducing both passive and active approaches. The active approach will be deepened by presenting triggering mechanisms based on Change point methods and Change detection tests. Finally\, the just-in-time detect&react mechanism is introduced where\, following a detected change\, the system immediately reacts with a strategy depending on the available information. \nBiography: Cesare Alippi received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano\, Italy. Currently\, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK)\, MIT (USA)\, ESPCI (F)\, CASIA (RC)\, A*STAR (SIN).\nAlippi is an IEEE Fellow\, Distinguished lecturer of the IEEE CIS\, Member of the Board of Governors of INNS\, Vice-President education of IEEE CIS\, Associate editor (AE) of the IEEE Computational Intelligence Magazine\, past AE of the IEEE-Trans. Instrumentation and Measurements\, IEEE-Trans. Neural Networks\, and member and chair of other IEEE committees.\nIn 2004 he received the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2013 he received the IBM Faculty Award. He was awarded the 2016 IEEE TNNLS outstanding paper award.\nAmong the others\, Alippi was General chair of the International Joint Conference on Neural Networks (IJCNN) in 2012\, Program chair in 2014\, Co-Chair in 2011. He was General chair of the IEEE Symposium Series on Computational Intelligence 2014.\nCurrent research activity addresses adaptation and learning in non-stationary environments and Intelligence for embedded systems. \nAlippi holds 5 patents\, has published in 2014 a monograph with Springer on “Intelligence for embedded systems” and (co)-authored more than 200 papers in international journals and conference proceedings.\nHome Page: http://home.dei.polimi.it/alippi/
URL:https://www.ieeetoronto.ca/event/learning-in-non-stationary-environments/
LOCATION:Ryerson University\, Room: ENG287
CATEGORIES:Signals & Computational Intelligence
END:VEVENT
END:VCALENDAR