Brendan Duke

Machine Learning Engineer at ModiFace
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us****@****om
(386) 825-5501

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Eric Morton

I first met Brendan while he was an intern and he traveled to Austin to aid in a bringup of a complicated processor chip. At that time, I had no idea he was still a student - he had the experience and knowledge that was superior to engineers that have been in the field for years. He was vital to our project and delivered in every way. Since then, I continue to be impressed by Brendan. He provides high quality support, even while only being part time during his continued studies. He clearly understands complex computer engineering features. I sincerely hope that Brendan continues to work with AMD, and know that he will be a great addition to any company.

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Experience

    • IT Services and IT Consulting
    • 1 - 100 Employee
    • Machine Learning Engineer
      • Aug 2018 - Present

      • Working with TensorFlow, PyTorch, TF.js, GCP, AWS, MLflow, TVM, Triton• Architect applied machine learning projects including data collection and annotation, model and algorithm design, and deployment to iOS, Android, and web platforms.• Model productionization including profiling, conversion, and MLOps.• Distributed training and experiment management on AWS and GCP.• Papers at top computer vision and machine learning conferences such as CVPR and ECCV.• Technology patent filing.• Team lead for a group of research scientists and engineers.

    • United States
    • Semiconductor Manufacturing
    • 700 & Above Employee
    • Software Development Engineer 2
      • Sep 2016 - May 2017

      - Software engineer developing security firmware for the AMD Secure Processor.

    • PEY - SW Platform Architecture
      • May 2015 - Aug 2016

      - Developed and documented AMD Secure Processor firmware implementation of SEV-KM (Secure Encrypted Virtualization Key Management) technology.- Aided in post-silicon bring-up, representing team at on-site initial bring-up and enabling AMD Secure Processor firmware features on AMD Ryzen/Epyc SoC architectures.- Developed a software Hardware Abstraction Layer for a Cryptographic acceleration unit. The layer is used across multiple SoC programs and firmware environments.- Implemented and designed firmware to communicate with and authenticate two hardware components over I2C and PCIe protocols -- used for feature differentiation.- Added to FPGA emulation tools and used them to verify and bring-up firmware for pre-silicon platforms.- Ported to a new SoC architecture a third party operating system, enabling a Trusted Execution Environment (TEE) utilizing ARM TrustZone technology.- Debugged integrated firmware Trusted Platform Module (TPM) to enable certification for Windows 10.

Education

  • University of Toronto
    Doctor of Philosophy - PhD, Machine Learning
    2020 - 2024
  • University of Guelph
    Masters of Applied Science, Machine Learning (School of Engineering)
    2017 - 2020
  • McMaster University
    Bachelor's Degree, Computer Science
    2014 - 2017
  • McMaster University
    Bachelor's Degree, Physical Sciences
    2010 - 2014

Community

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