Hi, I'm Arnold.

I'm a machine learning engineer and data scientist.

I am an Applied Scientist at Amazon. I have also been in R&D roles at TikTok, ASMPT, and Scotiabank.

I completed my MSc in Computer Science at the University of Toronto (2018 - 2020), while conducting machine learning research at the Vector Institute for AI.

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Contact me directly at arnold [dot] ys [dot] yeung [at] gmail [dot] com

Select Machine Learning Projects

Personalized Self-Explaining Image Classifier

Developed an RL framework which provides end users with sequential personalized explanations for the behavior of an image classifier.

The AI agent will predict what explanations you'll find most helpful as you learn how to simulate its behavior.

Sponsored by the Electronics and Telecommunications Institute (ETRI) of the Republic of Korea.

Published and presented at ICML 2020 Workshop on Human Interpretability in Machine Learning (Spotlight).

[paper] [code] [deploy]

Machine Learning Prediction of Confirmed COVID-19 Cases using Non-Pharmaceutical Interventions and Cultural Dimensions

Developed an ETL pipeline for 3 datasets, containing non-pharmaceutical interventions and cultural dimensions of 114 countries.

Trained multiple machine learning models to predict the confirmed infection growth in the following 14 days. Evaluated model performance using 3 validation methods, each applicable to a different use case.

Published in Journal of Medical Internet Research, 2021.

[paper] [code]

Detecting Phonemic Confusion using an LSTM

Developed a bidirectional LSTM network with skip connections to identify phonemes in text which may be misheard by a proxy listener.

Compared with a feed-forward fully-connected network and a Maximum Likelihood Estimate baseline. Evaluated using a Levenshtein distance metric.

Published and presented at ACL-IJCNLP 2021 SIGMORPHON.

[paper] [code] [slides]

Concussion Assessment with EEG Signals using Support Vector Machine

Developed an SVM model which predicts whether a patient has had a concussion, using features from EEG readings.

Presented at IEEE Engineering in Medicine and Biology Conference (EMBC) 2015 (Late-Breaking Paper).

[paper] [poster]

Battery State-of-Health Determination

Developed a machine learning model which determines the state-of-health of mobile phone batteries using the Cadex C5100 Battery Analyzer.

Completed at and patented by Cadex Electronics, 2015.

[patent]

Political Bias Text Classifier

Implemented a text feature processing and machine learning pipeline to classify the political bias of social media posts using Reddit data.

[blog] [code]

Image Colorization with Convolutional Neural Networks

Implemented and trained two CNN architectures (vanilla and U-Net) to colorize greyscale images from the CIFAR-10 dataset.

[code]

Select Engineering Projects

3D Environment Laser Scanner

Designed a stationary laser scanner (hardware and software) capable of capturing a hemispherical representation of the environment.

[slides] [video] [code]

Comparing the Performances of Wet and Dry EEG Electrodes

Designed and constructed an EEG headcap with dry and wet electrodes and conducted human experimentation. Evaluated and compared the performances of different dry electrodes, relative to standard wet electrodes.

Published and presented at IEEE Engineering in Medicine and Biology Conference (EMBC) 2015 (Oral Presentation).

[paper]

Retail Teabag Packager

Identified critical needs of small-scale specialty tea-leaves retailers. Conceptualized and designed an electro-mechanical teabag packager. Manufactured and assembled a physical MVP prototype for demonstration purposes.

[slides]

Motion-Controlled Beat Sequencer

Designed software for a beat sequencer which is controlled by the movement of an accelerometer.

[video] [code]

Concussion Risk Assessment Tool

Developed a framework for assessing the risk of concussion in a sports team based environment.

Published in Brain Injury, 2017.

[paper]