Hello World


About Me

Hi, I'm Frank Hui, a high school student in Burnaby, B.C. I'm passionate in computer science, photography, and graphic design.

  • Name: Frank Hui
  • Email: huifrank@live.com

0 Repositories

0 Hackathons

0 Years of Programming

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High School

Burnaby North Secondary School


CMPT 125, CMPT 127

Simon Fraser University




Machobear Studio

At this hackathon, our team built a blockchain clicker game that utilised decentralisation to create unique items and weapons that evolved over time and could not be destroyed. My role was implementing the blockchain into the game.


hackseq 2018

University of British Columbia

At this hackathon, our team built a project using blockchain to record and track cases of infectious diseases. My role was creating statistical analyses from blockchain data.


nwHacks 2019

University of British Columbia

At this hackathon, our team built an android app that analysed gait using accelerometer data to identify the person walking using deep learning AI. My role in this hackathon was designing and training the deep learning model.


hackseq 2019

University of British Columbia

Using machine learning, our team was able to identify which strains of the influenza virus were most succesful, with an accuracy of 86% compared to the WHO accuracy of 40-60%. My role was to design vaccines and compare them to WHO-selected seasonal vaccines using the 'augur' pipeline. In addition, I visualised data gathered from our predictions.


Local Hack Day- Build Day 2019

University of British Columbia

Our project is called Ubique, the Latin name for 'Anywhere'. Our project involves using a router and an 1 TB SSD to store all of Wikipedia and The Gutenberg Project, then serve them via an internet connection that you can bring anywhere. We used Flask and JS to build this application. My role was implementing The Gutenberg Project into Ubique.


Volunteer Intern Researcher

Simon Fraser University

I am currently working at Simon Fraser University as a volunteer intern researcher in the Computer Science Department. My previous project was on implementing an improved version of the Robinson-Foulds metric calculation. My role in the project was porting, packaging, and performance testing code.


Science Fair Participant

University of British Columbia, GVRSF

From my freshman to senior year, I have been participating in the annual science fair. I have won an award every year of participation.




VikingsDev is the Burnaby North Hack Club branch. We run mini-hackathons every meeting and build custom workshops for Burnaby North Seconday School students to follow so they can learn how to build a project. My role as the president is overseeing all aspects of the club.

Go to Website


Vice President and Director of Outreach


vhHacks will be the first high school hackathons to run on the West coast of Canada. We will be running on March 14-15 at UBC. My role in the non-profit organization is to oversee all aspects of operation, especially in finding and negotiating with sponsors.

Go to Website



Git, Github
Inkscape, GIMP, Adobe Lightroom
Bash, Linux, Ubuntu

Programming Languages

Java, Python, R, C

Vector Graphics Design

Over 4 years.


Over 8 years.


April 2016, 2017

Honourable Mention x2

Greater Vancouver Regional Science Fair

My projects in freshman and sophomore year were on bucket sorting and swarm intelligence.

CODE: J 135 F


CODE: M 095 N


January 2018

Halite II

Rank 33

Top High School Student in Canada
Rank 33 out of 5,832 players worldwide

Online Coding Competition by 2 Sigma

Halite II was an AI competition where players built bots to defeat others in a game of planetary conquest.

Download Certification

April 2018

Analysing Traffic Problems Using Properties of Emergence Through the Use of Simulation

Silver Medal

Best Mathematical Sciences Project
Best Computer Science Project
Greater Vancouver Regional Science Fair

For this project, I built a traffic simulator with Processing Java, and tested different road formations for the fastest traffic flow.

January 2019

Top 20 Project

nwHacks 2019

At nwHacks 2019, we built an Android app that identified people based on their gait using accelerometer data from phones with deep learning AI. Our project won the Top 20 award out of 132 submitted projects and 708 participants.


April 2019

Accurate Early Detection of GBM Brain Cancer With Deep Learning

Silver Medal

Best Computer Science Project
BC Game Developers Innovation Award
Greater Vancouver Regional Science Fair

In this project, I built a deep learning image recognition AI that expedited the diagnosis of brain cancer by processing MRI scans using a Convolutional Neural Network.


My Projects

See all projects


Accurate early detection of GBM Brain Cancer with AI

GVRSF 2019 - Silver Medal

GVRSF 2019 - Best Computer Science Project

GVRSF 2019 - BC Game Developer's Innovation Award

Despite great advances in the field of oncology, glioblastoma multiforme’s extreme aggression still results in a grim prognosis. A median survival length of a mere 11-15 months, couples with one of the lowest survival rates of all cancers, at ~4%.

Simultaneously, detection models have made great progress with non-small cell lung cancer and Alzheimer's in the medical diagnostics industry, while the current conventional detection of brain tumors involves human inspection of radiological imagery for tissue abnormalities. Our project aims to utilize convolutional neural networks on MR imaging for the same purpose. Work was primarily split into three categories: data extraction and preprocessing of imagery, convolutional neural network training, and the machine learning classification period.

The automation of early-stage tumor detection drastically reduces the workload of radiologists, aids with patient outcomes through earlier treatment, and may provide insight into the characteristics of high-grade astrocytomas. MR Imaging and ML algorithms look promising regarding their potential applications in the medical field, particularly in the field of medical diagnoses.

Go to Repository


Optimising and refining traffic through simulation

GVRSF 2018 - Silver Medal

GVRSF 2018 - Best Computer Science Project

GVRSF 2018 - Best Mathematical Sciences Project

In our current traffic system, congestion has become a major problem. However, the problem of traffic creates a dilemma for governments as they often cannot afford to construct more roads due to their high cost and maintenance fees. Furthermore, paving new roads can lead to habitat segregation and environmental damage as large areas of landscape are cleared for their construction. As the demand for transportation grows, the problem of continued traffic congestion will inevitably follow the demand. To attempt to solve this problem, we set out to find the optimal and most efficient road system through simulated swarm intelligence as it would allow a smoother flow of traffic with the same construction and maintenance costs.

In our search for the answer to congestion, we coded a digital environment to simulate the flow of traffic with differing types of road systems. Each car has an objective and uses a typical breadth-first path-finding algorithm to navigate to its set destination. Furthermore, as our end goal was to create a program that could be feasibly used within real life scenarios, we implement the feature to create customized road networks to simulate traffic in cities. Due to the nature of computerized simulations, we had the ability to monitor and control each individual variable very precisely. This allowed us to maintain a very high level of consistency between each one of the trials.

The grid system, orbital system and tributary trees were tested to see which one was the most efficient. Each system was simulated 24 times, each one for a total of 30 001 ticks. After each trial, all the conditions would be reset so as not to affect future trials. Time was also kept to keep track of the performance of each trial.

The results prove that the grid system is the most efficient road system as it had an average of 4.125 cars remaining at 30 001 ticks. In comparison, the orbital system had an average of 12.167 cars remaining and the tributary trees had 41.75 cars remaining. Observing the simulation, we could determine that the cars in the grid system had a variety of routes that allowed each individual car to have multiple paths at its disposal. Overall, this allowed the grid system to perform the best that supports our original hypothesis.

Go to Repository

Stride ML

Identification via gait accelerometer data on a phone using ML

nwHacks 2019 - Top 20 Award

Stride is a machine learning project based on security. To protect against theft, we built an app that could track your phone's accelerometer data to determine who is carrying the phone. If the walking patterns are not consistent with the owner's walking pattern, the phone will alert the owner's smart watch. The core technology is the neural network that can identify the person walking, which can be applied to many smart devices, provided they have accelerometers. In the future, we hope to embed this in a tag that can be placed with bags, backpacks, wallets, etc. to protect your valuable belongings.


I've been working with Inkscape for 4 years now, and I design logos for myself and other people, but when I have free time, I make art. I've also been doing photography for over 8 years now.

Download Art Portfolio


Contact Me

Email Address