Hello! I am Jimmy Wong, a multi-skilled professional in data science 💻, machine learning 🤖, and neuroscience 🧠, with 5+ years of working experience managing, cleaning, integrating, analyzing large multimodal datasets.
My graduate education in biomedical engineering (brain imaging), strong programming skills (Python, R, SQL, Bash), and years of practical experience of statistics and ML (Scikit-learn, Pandas, Scipy) have equipped me to excel in the biomedical and big data technologies space. My commitment to lifelong learning has also driven me to pursue additional courses in TensorFlow, CNN, and Google Cloud Platform (GCP) through Coursera.
My Non-Linear Path to Data Science/ ML
I discovered my passion for Data Science and Engineering in 2016, during my fourth year as a Psychology and Neuroscience major, when I had the opportunity to work in a neuroimaging lab. This experience opened up the world of engineering, image processing, and data analytics to me, igniting my passion for programming and machine learning.
Over the past five years, I have worked at leading hospitals in Toronto and been applying my knowledge in neuroscience, signal processing, machine learning, and statistics to transform and analyze large multimodal datasets. These datasets include clinical, physiological, imaging, and biological information in various formats, structures, and systems. I earned my Master of Applied Science (MASc.) in Biomedical Engineering from the University of Toronto in 2020.
I enjoy learning new technologies, reading blogs/papers, and tackle challenging problems 🧩.
Education
Master of Applied Science (MASc), University of Toronto, ‘20
Institute of Biomedical Engineering (BME)
Honours Bachelor of Science (BSc), University of Toronto, ‘17
Specialist in Psychology and Major in Neuroscience
Work Experience
Research Methods Specialist - CAMH
<Mar 2022 - Present>
⬆ Research Analyst
<Feb 2021 - Mar 2022>
Ensure effective management and integration of neuroimaging (MRI, EEG, PET), biological assay, physiological signal, cognitive assessment and clinical data across databases and servers (PostgreSQL, MS Access, REDCap, XNAT, LabKey) for a 5-year longitudinal study.
Perform data mining, wrangling, engineering, predictive modeling and statistical inference on multimodal datasets using Python (Pandas, NumPy, Scikit-learn), R (Tidyverse) and SQL.
Build storytelling, interactive, KPI-driven dashboards in Tableau and HTML reports with R Markdown to support decision making and track study progress.
Develop and automate end-to-end data flows across multiple platforms/software using API and Python.
Write Python and Bash scripts to process, analyze and visualize MRI and DICOM data.
Graduate Student - St. Michael's Hospital
<Aug 2018 - Oct 2020>
Conducted time-series analyses (regression, correlation, Fourier transform) on neuroimaging data using MATLAB and Python modules (Scikit-learn).
Applied multivariate statistical analysis (partial least squares, canonical correlation) on MRI and DTI data to study brain networks and microstructure.
Optimized fMRI preprocessing pipelines in removing nonlinear signal artifacts caused by head motion and physiological noise.
Analyzed the relationship between neural networks at rest and clinical symptoms using parametric and non-parametric tests, considering the distribution of data.
Research Assistant - Sickkids Hospital
<Aug 2017 - Jul 2018>
Processed and analyzed magnetoencephalography (MEG) and MRI data using MATLAB.
Extracted and localized brain signals via dimensionality-reduction techniques (ICA, PCA) and source reconstruction methods.
Conducted statistical analyses (linear mixed-effects model) of clinical and chemical data using R (dplyr, lmerTest) and SPSS.
SKILLS & EXPERTISE
Programming
Python, R, SQL, Bash, MATLAB
Machine Learning
Scikit-learn, TensorFlow, Supervised Learning
Neuroimaging
MRI, MEG, EEG
Statistics
SPSS, Multivariate Analysis, Probability
Data Visualization
Tableau, Matplotlib, Plotly
Version Control/ Git
GitHub, GitLab, git-annex
INTERESTS
Hiking
Traveling
Soccer
