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Co-Founder @ Mash
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Currently I'm at Mash, enabling revenue teams with access to their most critical knowledge. Before this, I was working at Amazon as an Applied Scientist, building on-device NLP algorithms/models for Alexa.
University of Toronto
MSc (Applied Computing), Computer Science
January 1, 2015 – January 1, 2017
Western University
BESc, Software Engineering
January 1, 2010 – January 1, 2014
Mash
Co-Founder
June 1, 2024 – Present
Mash
Co-Founder
May 1, 2021 – June 1, 2024
Amazon
Applied Scientist (Alexa)
November 1, 2019 – May 1, 2021
Toronto, Canada Area
ROSS Intelligence
Machine Intelligence Engineer
March 1, 2017 – November 1, 2019
Toronto, Canada Area
Rakuten Kobo Inc.
Applied Research Intern (Big Data)
May 1, 2016 – February 1, 2017
Toronto, Canada Area
University of Toronto (Department of Computer Science)
Teaching Assistant
September 1, 2015 – April 1, 2016
Toronto, Canada Area
Statflo
Software Engineer
December 1, 2014 – September 1, 2015
Toronto, Canada Area
The Codery Inc.
Full-Stack Software Developer
May 1, 2014 – November 1, 2014
London, Canada Area
Autodata Solutions
Software Developer
September 1, 2013 – April 1, 2014
Autodata Solutions
Software Developer (Intern)
May 1, 2013 – August 1, 2013
IO Industries Inc.
Software Developer (Intern)
May 1, 2012 – August 1, 2012
London, Canada Area
Matrix Factorization with Neural Networks and Stochastic Variational Inference
December 1, 2016 – Present
• Researched ways to apply deep learning methods to recommender systems. • Implemented a pre-existing algorithm for matrix factorization with neural nets. • Designed an extension to this model which makes use of stochastic variational inference. • Used TensorFlow, evaluated on the MovieLens 100K and 1M data sets.
An Evaluation of Topic Modelling Techniques for Twitter
April 1, 2016 – Present
• Researched existing text topic models (specifically for short documents). • Implemented models designed to work well with short documents. Included: the "biterm topic model" and a model which makes use of word2vec and a mixture of Gaussians. • Explored methods to evaluate topic models objectively (specifically the coherence of generated topics). • Completed experiments on the implemented models, and compared their performance to LDA.
A Survey of Machine Learning Techniques for Road Detection
December 1, 2015 – Present
• Used the NumPy and scikit-learn Python libraries to process sets of images from car-mounted cameras. • Generated multiple models of the data using various machine learning algorithms. • Analyzed the performance of the models in segmenting and identifying roads within new images.
DripDrop: An Autoscaler & Dynamic Load Balancer for DigitalOcean
December 1, 2015 – Present
• Designed an autoscaler and dynamic HTTP load balancer for VMs deployed to DigitalOcean. • Implemented a prototype using the Go programming language and nanomsg socket library. • Analyzed the system’s performance in comparison to industry-standard HTTP load balancers.
Deep Reinforcement Learning Nanodegree
Udacity
June 24, 2026 – Present
Course certificate: Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Deep Learning Nanodegree
Udacity
June 24, 2026 – Present
Course certificate: Software as a Service (CS-169.1x)
edX
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across startups (Mash, ROSS Intelligence, Statflo, The Codery Inc.), large corporations (Amazon, Rakuten Kobo Inc.), and academic institutions (University of Toronto) demonstrates adaptability to various work environments. Their entrepreneurial ventures as a Co-Founder suggest a proactive, innovative, and risk-taking mindset, which could be a strong cultural fit for dynamic and growth-oriented teams. The range of projects, from deep learning research to full-stack development and system design, indicates a broad interest and willingness to tackle different challenges.
Soft Skills & Operational Fit
The candidate's experience as a Co-Founder and in roles requiring architectural design and project management suggests strong leadership, problem-solving, and independent work capabilities. Their involvement in teaching also indicates good communication and mentorship potential. The detailed project descriptions imply a structured approach to problem-solving and a focus on measurable outcomes.