Tegan Maharaj

    

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Latest blog post     Research Interests     Biography     CV     Publications     Workshops     Talks     Teaching     Software

Latest blog post (April 7, 2025): My thoughts on risk from a decade of expertise (also, where have I been the last 6 months?)

Research Interests

Having entered AI in the era where we we definitely didn't call it that, I've seen us go from exciting curiosity-led empirical science to Silicon Valley Corporatopia's Exploitamatic McProduct. It's painfully obvious to me that we are doing AI really wrong, and I don't mean the architecture or optimizer or presence/absence of reasoning module. My most general career goal currently is to explore fundamentally different ways of creating and understanding AI in order to make the future suck less. I'm looking forward to joining the Abundant Intelligences initiative, starting an open makerspace at Mila, establishing art-tech collaborations with SAT, and writing more fiction. Some things I've been consistently interested in and influenced by over the last few years are: Truth and reconciliation, chaotic dynamical systems, Black feminism, ecological futurism, risk, transition design, queer and disability justice, curation, community agency.

In terms of AI, I'm broadly interested in studying “what goes into” deep models - not only data, but the learning environment including task design/specification, loss function, and regularization; as well as the wider 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.

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 biodiversity, climate change, epidemiology, AI alignment, and ecological impact assessments. My recent research has three themes (1) using deep models for policy analysis and risk mapping; (2) designing data or unit test environments to empirically evaluate learning behaviour or simulate deployment of an AI system; (3) foundations of anticolonial AI. Please contact me if you're interested in collaborations in these areas. For more detail on ongoing projects, hiring/recruiting, etc., please see my ERRATA*™ Lab Website.

Biography

I started post-secondary education in biology with a focus on health and neuropsychology, but transitioned to a concentration in ecology as I learned about invertebrates, microbiology, and all the amazing interactions of life around us. Analyzing results for my honour's research in the community ecology of bioremediation, I was introduced to programming for the first time, and quickly realized I wanted to use machine learning to understand and model complex ecosystems. 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 (aka clickstream prediction aka discovering firsthand how right jeff hammerbacher was/is)

In November 2015 I completed my MSc, and in January 2016 began a PhD at Mila (then Lisa), a world-leading academic research institute in Montreal for AI and deep learning, where I was an NSERC- and IVADO-awarded scholar with Christopher Pal. I did a lot of different projects aimed at understanding how deep learning systems perform in "real world" conditions, and my thesis reviews factors that affect generalization in deep learning. I was involved in creating and structuring the Montreal Declaration on Responsible AI. I also helped start the Mila Lab Representatives, became a managing editor at the Journal of Machine Learning Research (JMLR), the top scholarly journal in machine learning, and was a co-founding member of Climate Change AI (CCAI), an organization which catalyzes impactful work applying machine learning to problems of climate change.

Beginning June 2021 I accepted a tenure-track position at the University of Toronto, in the Faculty of Information, where I was an affiliate of Vector, co-founding member of the Toronto Climate Observatory and honoured to be a fellow of the illustriously multidisciplinary Schwartz Reisman Institute for Society and Technology. Information is a unique and curious "field"; I will forever be grateful that I began my career in a place so deeply embodying the idosyncratic and very human pursuit of knowledge and understanding.

After a wonderful 3 years at University of Toronto, in fall 2024 I moved home to Montreal as a core member of Mila, with a tenure-track position at HEC. Tiohtià:ke/Montréal is on unceded Indigenous lands. I recognize the Kanien’kehá:ka Nation as the custodians of the lands and waters in this place I call home. To them I offer my deep thanks, my joy at the beauty and diversity here, and my committment to truth and reconciliation (of which this statement is one small step).

CV

My CV can be found here.

Papers

(* denotes equal contribution)

Despite my best efforts to keep this updated, and desire to have better tools for research tracking and credit assignment, my scholar profile remains most reliable for my latest research.

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

Woefully outdated, but watch this space! Updates, teaching materials, and other fun stuff coming soon.

Teaching

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.

Software

This blog is publshed under a Creative Commons CC BY-NC-SA license. This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. You may not, in general use any text of this blog to train or prompt a language model or similar AI system, because they are commercial products, whose outputs are not licensed BY-NC-SA, and you cannot guarantee they or their derivitives will attribute credit correctly or at all. If you develop an AI system that respects BY-NC-SA, go ahead and use my work for it! Also, please let me know about your research :).