Tegan Maharaj



New news! I've accepted a tenure-track position in the Faculty of Information at the University of Toronto, where I'll be an affiliate of both the Schwartz-Reisman and Vector institutes.

In the near term, I’ll be recruiting students/RAs to work on deep representation learning & predictive methods in ecological modeling and environmental risk assessment, as well as real-world generalization, learning theory, and practical auditing tools (e.g. unit tests, sandboxes). If you’re interested in those positions, interested in collaborating or chatting about those topics, or know someone who is, please get in touch!

Research Interests

My goal in research is to contribute understanding and techniques to the growing  science of responsible AI development, while usefully applying AI to high-impact ecological problems including climate change, epidemiology, AI alignment, and ecological impact assessments. My recent research has two themes (1) using deep models for policy analysis and risk mitigation, and (2) designing data or unit test environments to empirically evaluate learning behaviour or simulate deployment of an AI system. Please contact me if you're interested in collaborations in these areas.

I am broadly interested in studying “what goes into” deep models - not only data, but the broader learning environment including task design/specification, loss function, and regularization; as well as the broader societal context of deployment including privacy considerations, trends and incentives, norms, and human biases. I'm concerned and passionate about AI ethics, safety, and the application of ML to environmental management, health, and social welfare.


I started post-secondary education in biology with a focus on health and neuropsychology, but transitioned to a concentration in ecology. Analyzing results for my honour's research in bioremediation, I was introduced to programming for the first time and quickly realized I wanted to do machine learning. I recieved an NSERC scholarship to particpate in a large-scale research project on climate change, and later participated in a number of coding projects and discovered neural networks.

I began an MSc in computer science with Layachi Bentabet, studying biological realism in deep networks. During this time I was awarded a MITACS scholarship to be a machine learning research intern at iPerceptions, exploring semi-supervised learning in predictive models.

In November 2015 I completed my MSc, and in January 2016 began a PhD at Mila, a world-leading academic research institute in Montreal for AI and deep learning, where I am an NSERC and IVADO awarded scholar with Christopher Pal. I'm also a managing editor at the Journal of Machine Learning Research (JMLR), the top scholarly journal in machine learning, and co-founder of Climate Change AI (CCAI), an organization which catalyzes impactful work applying machine learning to problems of climate change. 


My CV can be found here.


(* denotes equal contribution)

Workshops and other contributions

I've co-organized several workshops:

I was a co-founder of the Montreal AI Ethics meetup, and a contributor to SOCML 2017 and 2018, as well as the Montreal Declaration for Responsible AI and the Beneficial AGI Conference.

I've received outstanding reviewer awards at every venue since NeurIPS began that practice in 2017.

Talks and presentations


I was a TA for the following classes during PhD:

During undergrad and master's:

I also worked as a tutor at the Computer Science Help Centre at the end of my BSc/beginning of MSc, and at the ITS Helpdesk (troubleshooting and tech support) throughout my BSc.