I am passionate about answering meaningful questions through the use of software development and data science. I strongly value communicating complex concepts in a way that is easy to understand.
My past experiences involve machine learning and deep learning research, applied in healthcare.
I’ve tackled three questions:
If we train deep learning models (CNN) on biological images (CytoImageNet), does it learn more biologically-meaningful image features than models trained on images of every day objects (ImageNet)?
Can we improve ultrasound-based prediction of kidney disease, by adapting image-based deep learning models (2D CNNs) to take in all of a patient’s data over time?
Does dimensionality (PCA) affect clustering (K-Means) of medical images under small sample sizes?
I find it fascinating how a plethora of sophisticated tools can come together to create a simple yet impactful story.
LANGUAGES: [Python, SQL, R, Java, Shell Script, C, HTML/CSS, MATLAB, Assembly]
Python Libraries: [pandas, dask, numpy, matplotlib, tensorflow, keras, pytorch, sklearn, xgboost, lightgbm, open-cv]
R Libraries: [rvest, ggplot2, tidyverse, dplyr, blogdown, knitr, shiny, flexdashboard]
H.BSc. Computer Science & Bioinformatics, 2023, 3.85/4
University of Toronto
A hub for exploring any concept under the sun.