Duncan Moss Email & Phone Number

Portland, Or, Us

Seattle, Washington, United States

Oregon, United States

Lead Architect for Megh's Deep Learning technologies.Continuous Model Training - Continuous training (CT) is a process of fine-tuning AI models with new data to keep them up-to-date and accurate. - CT is especially useful in dynamic environments where the underlying data distribution can change rapidly, such as in intrusion and fraud detection. - Megh's CT framework allows models to adapt to the environment quickly and dynamically, virtually eliminating false positives. Contextual Analytic Framework - Contextual analytics is a data analytics method that takes into account the setting in which data is generated, collected, and analyzed. - The goal of contextual analytics is to provide a more complete and accurate understanding of the data by considering the context in which it was created. - The result is a more nuanced understanding of the data that can help organizations make more informed decisions and take more effective action.Nimble Application Framework - Nimble is a fast and lightweight service-based framework for implementing CPU, GPU, and FPGA video analytics pipelines. - It is designed to scale from single edge camera deployments to larger cloud-based systems. - Nimble is highly configurable and can be partitioned into multiple containers. This provides customers with unparalleled flexibility in how their solution is deployed and ultimately the cost of the pipeline.FPGA Deep Learning Engine - The Deep Learning Engine (DLE) has been designed from the ground up for streaming inference. - It consists of a library of high performance, mixed precision DL primitives that are drop-in replacements for TensorFlow and PyTorch layers. - The DLE compiler directly parses TensorFlow and PyTorch models, creating an optimal DLE configuration. - The quantized layers flow is 100% compliant with the TensorFlow Quantization Specification with floating point fallback.

United States

Working as a Deep Learning Engineer in the Deep Learning Accelerator Team.

Sydney, Australia

High Frequency Signal Analysis using Dedicated Hardware

Hillsboro, Oregon

Developed machine learning workloads for the Xeon+FPGA

Sydney, Australia

- Constructed lightweight GPS and proximity devices for animal tracking and interaction studies. - Aided researchers by applying machine learning techniques to gain a richer understanding of animal behaviour.

Sydney, Australia

Developed and deployed a thermal transfer laboratory for remote teaching in high school classrooms.

Anzac Pde

- Creating an infrastructure for Co-Verification between an S2E Virtual Machine and a SystemC Testbench.- Research into SystemC and how various Testbenches are implemented as well as the S2E Virtual Machine and examining symbolic execution.- Collaboration with colleges on the progress, direction and state of the project.

I was responsible for the safety and enjoyment of the 50 students, as well as the 3 coaches who assisted me in running the sport.Managed and organised all in-school orienteering events that the students participated in.Communicated with the parents about the progress that their son was making, as well as any administration issues.Managed a team of coaches to ensure that the students were looked after and any problems are deal with swiftly.

1 Martin Place

As a student in Computer Engineering I was given computer related tasks, as well as some administration tasks. These were: - Developing a maths parser using ANTLR in C++, which included a Python interface. This was developed using the boost.python package. - Research and presentation of findings on the application of Haskell language to the specification and valuation of financial derivatives. - Electronically capturing, organising and archiving the paper copies of all Macquarie's official derivatives model Approvals.

I assisted in the ongoing refinement of the St John’s Granville web-based Event Manager. This web service provided an interface where the volunteers could sign up and register inventory that would be used at specific events. This job involved: - Using HTLM and PHP to create several small modules for the Event Manager that revolved around the organisation of events and the logging of inventory. - Assisted in the creation and maintenance of a LAMP based web server on which the Event Manager was hosted.

Doctor Of Philosophy - Phd, Electrical And Electronics Engineering

Activities and Societies: FPGA, Machine Learning, Low Precision, High Frequency SignalsFPGA Architectures for Low Precision Machine Learning

Bachelor Of Engineering (B.Eng.), Bachelor Of Science(B.Sci), Computer Engineering, Physics, Mathematics

Computer Engineering: Hardware and Software, Microprocessor Technology, Embedded Systems

Higher School Certificate

High School