

I'm a PhD candidate at Mila, researching interpretability for Natural Language Processing, primarily focusing on ensuring interpretability methods provide valid explanations. My supervisors are prof. Sarath Chandar and prof. Siva Reddy. Before that, I was known for being an independent researcher, also in interpretability.
Neural networks are very complex and their logic is not transparent to users or developers. Providing explaantions of neural networks is called interpretability, and belive that machine learning in some areas is socially irresponsible without this. Unfortunately, I don't think there is enough research in this area, as most research revolves around beating the state-of-the-art. I want to change that. My compass is to ground my research in real-world settings based on my past experiences as a freelancer in machine learning.
I've published 1) At ICLR 2020, where I received a spotlight award. 2) In the Distill.pub journal. 3) And have two publications in review. – I've been interviewed several times about my publications and work.
In the past, I worked as a Freelancer in Machine Learning for 3 years. One of my projects was implementing clinic.js, which has become the de-facto profiling tool in JavaScript and won awards. Additionally, I was also a very active open-source contributor in JavaScript. I have helped developed Node.js such as major core components, infrastructure, and was part of several steering committees. Finally, my own open-source modules were downloaded 57 million times in 2020.
I've written a blog post about my life as an Independent Researcher that went quite viral.