About


I am a Machine Learning practitioner with passion in actualizing the value of AI and proven experience in implementing ML solutions in large, complex enterprises.

I am a lifelong student.

I am a Machine Learning Engineer at Nextdoor in San Francisco.

I am an avid reader. My favourite books include The Brothers Karamazov, The Count of Monte Cristo, East of Eden, Flowers for Algernon, Lolita, One Hundred Years of Solitude.

I enjoy dancing salsa and bachata.


Personal Photo
Photo Credits to K.M.


I was a Machine Learning Engineer at Dessa (formely deeplearni.ng) in Toronto.

I have a B.Sc from the University of Toronto in Math & Physics. I was a member of the UofT Data Science Team.

I was a math teaching assistant at UofT since my second year and have collaborated with faculty to develop curriculum for a data science course. I have also mentored participants and judged projects at HackOn(Data) 2017 — a Toronto data science hackathon.

I played guitar, jazz & classical piano.



Side Projects

Residential Real Estate Valuation

Extracted, cleaned and pre-processed over 13 million records from remote SQL database. Trained XGboost valuation model on AWS EC2.

Guide: Spark with Jupyter on AWS

A guide on how to set up Spark with Jupyter on AWS EC2 instances with S3 I/O support. Presented at Toronto Apache Spark #19.

HackOn(Data) 3rd Place Project: Optimal Digital Map Placement in Toronto

A solution for determining the most optimal placement of location-based information maps throughout Toronto.

Data Science Bowl Preprocessing on AWS *

I use python multiprocessing to preprocess Lung CT Images efficiently on all available CPU cores on AWS compute instances.

Dstl Satellite Image Exploration on AWS *

An exploration of satellite images using AWS S3 and boto3 for the kaggle DSTL Satellite Imagery Feature Detection challenge.

* As a member of the University of Toronto Data Science Team (UDST).



Teaching

Toronto Machine Learning Series Presenter — ‘Modern Agile for Machine Learning’

How machine learning teams can apply Modern Agile and Extreme Programming engineering principles to deliver high-quality, flexible and low cost-of-change ML projects that yield a net reduction in development time and production time.

Toronto Deep Learning Series Presenter — ‘Google BERT’

Technical presentation on state-of-the-art NLP model Google BERT

University of Toronto School of Public Health Guest Lecturer

Practical and theoretical methodologies for applying deep learning to real-world applications, including public health sciences, based on techniques employed in real-world contexts.

University of Toronto Teaching Assistant

MAT245: Mathematical Methods in Data Science: An introduction to the mathematical methods behind scientific techniques developed for extracting information from large data sets.



Honors & Awards

Name Year
3T0 M. & P. and Associates Scholarship 2018
Norman Stuart Robertson Scholarship in Mathematics 2017
Coexter Scholarship in Mathematics 2017
C.L. Burton Scholarships for Mathematics and/or Physical Sciences 2017, 2016
Third Place at HackOn(Data) 2016
NSERC Undergraduate Student Research Award 2016 (UToronto)
2015 (UWaterloo)
Dr. James A. & Connie P. Dickson Scholarship in Science and Math 2015
University of Toronto Scholar
Joseph Alfred Whealey Incourse Scholarship
2015
Howard Ferguson Provincial Scholarship 2014
2015*, 2016*, 2017*

* Renewed



Misc

Data Science Resources

A list of useful data science resources.

Book List

What I’ve read.



Acknowledgements

This website template is based off Patrick Steadman’s website. It uses the Lanyon jekyll theme, and is hosted on Github Pages.


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