About CRIM

Tom Landry, M.Sc.

Senior Advisor, Partnership and Business Development

Tom Landry
Team: Vision and Imaging

T 514 840-1235, ext. 2657
@ tom.landry@crim.ca

(M. Sc. in Electrical Engineering, Université Laval, 2012)

Tom has nearly 20 years of experience in a variety of applied computer fields including E-Learning, geomatics, industrial automation, E-Commerce and machine vision.

Prior to joining CRIM in the Internet Development and Technology team in 2006 (now Emerging Technologies and Data Science), Tom worked in IT as a computer technician for five years. He joined the Vision and Imaging team in 2008. 

His main interests are software engineering and architecture, methodologies and project management, geospatial and remote sensing, motion capture and analysis systems, cloud computing and Big Data. In recent years, he has been the driving force behind the development of vision applications for elite athlete training and competition. Tom has been a member of CANARIE's Technical Advisory Committee (STAC) since 2014. He is project manager of the PAVICS research platform, dedicated to Canadian climate scientists.

Internationally, he is CRIM's official liaison with the Open Geospatial Consortium (OGC), where he leads projects in Earth observation, cloud computing and machine learning. Since 2016, he has been contributing to the Earth System Grid Federation (ESGF) as a member of the Executive Committee (ESGF-XC) representing Canada, and as part of the Compute Working Team (ESGF-CWT).

Portrait d'expert du CRIM : Découvrez Tom Landry (In French - 5:30)

Releases

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  • ????Une série de formations TI par les événements Les Affaires @la_lesaffaires Transformation numérique, Gestion des T… https://t.co/lkcsZ9BvXs
  • AIxSPACE RT @AIxSPACE_ca: ???? [IMPORTANT] In order to guarantee the safety of all, we have decided to postpone #AIxSPACE until January 18, 2021 // [IM…

Recent Publications

  • On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification

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  • An ensemble Based Approach for Generalized Detection of Spoofing Attacks to Automatic Speaker Recognizers

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