Ji Chu Jiang

Ji Chu Jiang

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Chief Executive Officer at Neurata AI Consulting
Ottawa, Ontario, Canada

Contact Ji regarding: 
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Full-time jobs
Starting at USD9K/month
Flexible work
Starting at USD10K/hour
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Résumé


Jobs verified_user 0% verified
  • N
    Chief Executive Officer
    Neurata AI Consulting
    Dec 2021 - Current (3 years 8 months)
    - Open to considering AI, Data Science Contract/Consultant Opportunities
  • L
    Machine Learning Software Engineer
    Lytica Inc.
    Sep 2019 - Jan 2022 (2 years 5 months)
  • A
    Professor
    Algonquin College of Applied Arts and Technology
    May 2019 - Sep 2019 (5 months)
  • H
    Technical Lead
    Hatch Coding
    Jan 2016 - May 2019 (3 years 5 months)
Education verified_user 0% verified
  • University of Ottawa
    Master of Applied Science, Electrical and Computer Engineering
    University of Ottawa
    Jan 2017 - Dec 2019 (3 years)
  • University of Ottawa
    Bachelor of Applied Science, Biomedical - Mechanical Engineering
    University of Ottawa
    Jan 2010 - Dec 2015 (6 years)
  • University of Ottawa
    Bachelor of Science, Computing Technology
    University of Ottawa
    Jan 2004 - Dec 2009 (6 years)
Awards verified_user 0% verified
  • S
    Best Design Award
    Startup weekend Ottawa
    Oct 2018
    Won best design award for the Facial recognition door lock business idea
Publications verified_user 0% verified
  • E
    TabCellNet: Deep learning-based tabular cell structure detection
    Elsevier Neurocomputing
    Jun 2021
  • MDPI
    Federated learning in smart city sensing: Challenges and opportunities
    MDPI
    Sep 2020
    Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services.
  • I
    High Precision Deep Learning-Based Tabular Position Detection
    IEEE ISCC
    Jul 2020
    Documents are constantly being processed within supply chains in various industries throughout the globe. Within those documents, often times the most important content is stored in tabular format. Therefore an automated technique for supply chain document processing is highly desired. Deep learning approaches show promise to deliver an end-to-end extraction model. However, it has been shown that tabular detection accuracy is not always correlated to tabular localization accuracy. Portions of the desired tabular information can easily be cropped out due to a lack of localization accuracy. In this paper, we propose a two stage convolutional neural network-based deep learning framework to improve tabular localization accuracy.