Francisco Farinha

Francisco Farinha

Engineering Physics Student

University of British Columbia

Hi,

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.

Interests

  • Machine Learning
  • Computer Vision
  • Data Analysis

Education

  • BASc in Engineering Physics, 2017 - 2022

    University of British Columbia

Experience

 
 
 
 
 

Machine Learning Specialist

Flash Forest Inc.

September 2020 – April 2021 Toronto, ON
  • Developed Machine Learning segmentation pipeline to aid in planting missions.
  • Compiled, cleaned, and maintained dataset of orthomosaic images.
  • Implemented efficient inference scripts to run in QGIS.
 
 
 
 
 

Machine Learning Intern

Longervision Technology

July 2020 – November 2020 Surrey, BC
  • Cleaned, labelled, and augmented client image dataset. Trained YOLOv4 model with high accuracy for train platform passenger/uniformed worker detection and implemented TensorRT model on NVIDIA Jetson Nano platform.
  • Established SFM pipeline for 3D reconstruction of client drone footage using OpenMVG and OpenMVS.
 
 
 
 
 

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.

Projects

*

GAN Who

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

Contact