Francisco Farinha

Francisco Farinha

Engineering Physics Student

University of British Columbia


I’m Francisco, a 4th year Engineering Physics student at UBC who is passionate about tackling real world problems with the exciting potential of Machine Learning. While you’re here, check out some of my projects, view my resumé, or drop me a line below.


  • Machine Learning
  • Computer Vision
  • Data Analysis


  • BASc in Engineering Physics, 2017 - 2022

    University of British Columbia



Machine Learning Intern

Longervision Technology

July 2020 – Present Surrey, BC
  • Cleaning, labelling, and augmenting client image dataset. Training YOLOv3 model with high accuracy for train platform passenger/uniformed worker detection. Implementing model in NVIDIA Jetson Nano, ensuring fast inference time.
  • Participating in OpenCV’s Spatial AI competition – proposed application of OpenCV and Intel’s stereo camera with Neural Inference for low-cost speedometer and vehicle classifier.

Computational Plasma Engineer & Programmer

General Fusion Inc.

January 2019 – April 2019 Burnaby, BC
  • Integrated Magnetohydrodynamics stability framework OMFIT into physics workflow.
  • Developed additional functionality for OMFIT – visualization tools, PBS/Torque server compatibility, parallel job submission – which decreased timeline processing by over 100%.
  • Presented DCON Stability Analysis reports to the MHD team weekly.




Guess Who implementation with GAN generated and VDSR upscaled images.

NumPy Neural Networks

Implementation of Neural Networks using only NumPy

UBC Open Robotics

Developing Software to compete in the RoboCup@Home competition.

Machine Learning Competition

Implemeted YOLO to navigate a simulated course for ENPH 353.

Artifact Removal & Biomarker Segmentation

A Project for EECE 571T - Advanced Machine Learning Tools - Where I created a pipeline to detect FOXP3+ biomarkers in follicular lymphoma TMA cores.

Paper Review

A Neural Algorithm of Artistic Style

Creating artistic images using Deep Neural Networks

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

Using a Deep CNN to achieve highly accurate single-image super-resolution