Career Profile

Inspired machine learning engineer with a strong interest in computer vision and natural language related deep learning areas and some specialization in hardware acceleration and computing.

Experiences

GPU Software Researcher

2016 - 2017
University of Guelph, Guelph
  • Assist in image classification and generation related research activities.
  • Develop and maintain large scale framework for deep learning on GPU cluster copper.
  • Test different parallelism for accelerated deep learning on hardware level.
  • Evaluate system bandwidth and benchmark deep learning and reinforcement learning related GPU performance on Intel-based cluster and IBM Power Systems.
  • Build, Install or update python stack manually or via Anaconda.
  • Build, Install or update popular deep learning software including Theano, TensorFlow, Caffe, Torch and DIGITS on Ubuntu and CentOS with intel and power based architectures.

Graduate Teaching Assistant

2014 - 2016
University of Guelph, Guelph

Courses include Applied Differential Equation, Electric Circuit, System & Control Theory, and Electrical Devices.

  • hold office hours and respond to email queries
  • grade assignments
  • invigilate and grade exams
  • assist instructor with preparing lab materials and organizing lab sessions

Projects

GAN eval

2017

Experimented with evaluating generated sample qualities based on some divergence metrics across different hyper parameter dimensions.

Paper Github


Generative Adversarial Parallelization

2016

Experimented with parallelized training of multiple Generative Adversarial Networks for improved mode coverage and regularization.

Paper Github


Theano-MPI

2015

Implemented distributed deep learning on ImageNet classification aiming to scale up the training of deep learning models based on data parallelism. It utilizes multiple GPUs on a computing cluster to speed up the training performance.

Paper Github


Software design for oxygen monitoring application

2012

This project aims to build a program for the oxygen monitoring system. The program running on the prototype board (FPGA and MCU) collects oxygen absorption signal and calculates real time oxygen concentration. The prototype includes a large LCD and other human interfaces for signal display, menu control and data recording purposes.