Machine Learning in Engineering: Panacea or Deep Trouble?

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The Montreal Chapter of the IEEE Signal Processing (SP) Society cordially invites you to attend the following talk, to be given by Prof. Kostantinos N. Plataniotis from Electrical and Computer Engineering Department at University of Toronto, on Thursday August 9th 2018, from 11h30 to 12h30pm at Concordia University (EV Building, Room 3.309).



  Date and Time

  Location

  Hosts

  Registration



  • Date: 09 Aug 2018
  • Time: 11:30 AM to 12:30 PM
  • All times are (GMT-05:00) Canada/Eastern
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  • 1515 Saint-Catherine St. West
  • Montreal, Quebec
  • Canada H3G 1M8
  • Building: EV-Biulding
  • Room Number: 3.309

  • Contact Event Host
  • Prof. Arash Mohammadi

    Concordia Institute for Information System Engineering (CIISE)

    Concordia University,

    Montreal, QC, H3G 2W1, Canada

  • Starts 16 July 2018 09:20 AM
  • Ends 08 August 2018 12:20 PM
  • All times are (GMT-05:00) Canada/Eastern
  • No Admission Charge


  Speakers

Prof. Konstantinos N. Plataniotis of University of Toronto

Topic:

Machine Learning in Engineering: Panacea or Deep Trouble?

The recent rise of artificial intelligence (AI) can be attributed to the success of deep neural networks (DNN) in tasks, such as image classification and natural language processing.  The availability of curated, large scale and diverse data sets, access to powerful computing infrastructure, and theoretical advances are the main driving factors behind the resurgence. Machine learning (ML), deep neural nets, smart analytics are all trending tools promising disruptive contributions capable of solving real-world problems. Thus, it is not surprising to see a sustained push from industry, policy makers, and government bodies towards accelerating developments in machine learning. The purpose of this presentation is to provide an environmental scan of the research landscape, introduce, in a tutorial style, aspects of the research machinery, and discuss intuition, utility and expectations.

Biography:

Konstantinos N. (Kostas) Plataniotis is a Professor and the Bell Canada Chair in Multimedia with the Electrical and Computer Engineering Department, University of Toronto, Toronto, ON, Canada. He is the co-founder and inaugural Director-Research for the Identity, Privacy and Security Institute (IPSI), University of Toronto, and he served as the Director of the Knowledge Media Design Institute (KMDI), University of Toronto, from January 2010 to July 2012. His research interests are knowledge and digital media design, multimedia systems, biometrics, image and signal processing, communications systems, and machine learning. Dr. Plataniotis is a Registered Professional Engineer in the Province of Ontario, a Fellow of IEEE, a Fellow of the Engineering Institute of Canada. He was the IEEE Signal Processing Society Vice President for Membership (2014–2016) and he has served as the Editor-in-Chief of IEEE SIGNAL PROCESSING LETTERS, as Technical Co-Chair of the IEEE 2013 International Conference in Acoustics, Speech and Signal Processing, and as General Co-Chair for the 2017 IEEE GlobalSIP Conference. Dr. Plataniotis is the General Co-Chair for the 2018 International Conference on Image Processing (ICIP-18) and the General Co-Chair for the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021).

Address:Toronto, Canada