Hi, I'm a PhD student in artificial intelligence at Mila associated with Université de Montréal supervised by Aaron Courville. I finished my Master's there in 2019 and before that I graduated in 2017 with a Bachelor's in Software Engineering student from the University of Waterloo, during which I also spent an exchange term at Lund University in Sweden.
My research goal is to learn interaction with humans through language but my interests span NLP, reinforcement learning, and more
Mila's asynchronous distributed hyperparameter optimization for deep neural networks
Android app that allows you to draw an app mockup on paper, take a picture of it, and quickly turns into a real app interface using React.js, made as part of Hack the North 2015.
Android app for creating and sharing electronic business cards using NFC, made as a part of Calhacks 2014
A machine learning classifier that reviews a Yelp user's reviews. Determines the overall quality of a user's Yelp reviews against training data of 1 million reviews, made as part of Yelp Fall 2014 Hackathon.
I returned two years later to the great team at ServiceNow Research, fka ElementAI, to work on semi-supervised language as well as continue my work on language models and reinforcement learning. I worked with Issam Laradji and got a better understanding of production requirements and needs for NLP. I was happy to work on different aspects of NLP and we got two neat workshop papers out!
I finished off the break between Master's and PhD by working with Douwe Kiela on language pretraining for interaction. I was hoping to work in NYC, which then changed to Menlo Park, and finally ended up being my living room thanks to COVID-19. I still had a good time doing the internship remotely and having awesome video calls with the team in Montreal. I'm looking forward to seeing everyone in person once things settle down. It was also an interesting time to work at Facebook and seeing the discourse from both sides.
I moved one building over to work with Dzmitry Bahdanau and Harm de Vries at a vibrant Montreal startup! With the grand challenge of learning natural language database interaction, we worked on language pretraining for text-to-SQL. We had an ambitious project that ended up dealing with the fundamental expressivity of SQL and the challenge of zero-shot database adaptation. I enjoyed my time there despite the "trying times" and very fondly remember the team sitting down for lunch together.Also the kombucha.
I did consulting for NextAI's 2019 Montreal cohort giving machine learning advice to companies working on water data, supply chain management, tracking the dark web, and managing dental information! It was a great experience seeing how ML and data science can be leveraged in creative ways to make real, viable products.
I spent my last co-op term working in academia doing deep learning research! I worked on backpropogation through stochastic discrete neurons as applied to GANs and word embeddings coming up with novel ideas and models. I had a great time working at one of the biggest academic deep learning labs in the world and met many smart, insightful grad students. Montreal as a city is also really great! I love the beer, bakeries, and my French really improved!
I got the amazing opportunity to do deep learning research at one of the top places in the world! I
worked on the whole pipeline, from the proposal of an idea, figuring out a specific benchmark,
creating a pipeline for generating data, and researching different architectures for the computer
vision task. It was a great experience and I learned the basics of applying and researching deep
learning, ending up with a pretty good result.
I really enjoyed working with my fantastic mentor, Wei, going to reading groups, listening to tales of the early silicon valley, and meeting fantastic researchers. I delved even more into coffee and learned how to make a pretty decent latte (espresso machine and all), though my latte art needs some work!
I came back to San Francisco to work at a really interesting startup, changing the way we measure
the economy (and a bunch of other things!) by using an app for on-the-ground contributors, paying
them to collect data, and intelligently processing it to figure out what is happening across the
world in real time.
I worked as an everything engineering touching the data analysis pipeline, the android app, the server code, and even getting into some image processing. It was fun working with such a skilled, tight-knit team and I drank a lot of good coffee)
I spent a great four months in San Francisco working on
I worked on full-stack web development on an agile team, getting my first real taste of Silicon Valley. Then, I dove deep into the code and worked on frontend features like the new landing page and purchasing flow, as well as backend where I internationalized check-in offers. I also discovered the best chocolate.
I worked at an awesome water data startup in downtown Toronto located at CSI
I got to work on our whole backend pipeline, from scraping and web crawling, to unstructured text classification, to text parsing. I delved further into classification and really improved our recall with positive-unlabelled learning, and did some interesting work integrating ElasticSearch with MongoDB for full featured text-search through pdfs
Also the tea was great.
I worked on some statistical machine learning in fields such as pattern detection and classification, in which I even implemented a novel algorithm!