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!
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)
Predicting infectiousness for proactive contact tracing. Yoshua Bengio*, Prateek Gupta*, Tegan Maharaj*, Martin Weiss*, Nasim Rahaman*, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams ICLR 2021 (oral). [pdf]
COVI-AgentSim: An agent-based model for evaluating methods of digital contact tracing. Prateek Gupta*, Tegan Maharaj*, Martin Weiss*, Nasim Rahaman*, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B Muller, Yoshua Bengio. [pdf]
Toward trustworthy AI development: Mechanisms for supporting verifiable claims. Miles Brundage*, Shahar Avin*, Jasmine Wang*, Haydn Belfield*, Gretchen Krueger*, Gillian Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, JB Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung. [website] [pdf]
Tools for society [chapter in Tackling Climate Change with Machine Learning]. Tegan Maharaj, Nikola Milojevic-Dupont. Edited by David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski 2019. [website] [pdf]
Hidden incentives for self-induced distributional shift. David Krueger, Tegan Maharaj, Shane Legg, Jan Leike SafeML@ICLR2019. [pdf]
Memorization in recurrent neural networks. Tegan Maharaj, David Krueger, Tim Cooijmans PADL@ICML2017. [pdf]
Reserve output units for deep open set learning. David Krueger, Tegan Maharaj COSL@CVPR2017. [pdf]
A closer look at memorization in deep networks Devansh Arpit*, Stanislav Jastrzebski*, Nicolas Ballas*, David Krueger*, Emmanuel Bengio, Max Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien ICML2017. [pdf]
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. Evan Racah, Christopher Beckham, Tegan Maharaj, Prabhat, Samira Kahou, Christopher Pal. NeurIPS2017. [pdf] [code] [dataset]
A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, Christopher Pal. CVPR2017. [pdf]
Suprisal-Driven Zoneout. Kamil Rocki, Tomasz Kornuta, Tegan Maharaj. 2016. [pdf]
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. David Krueger*, Tegan Maharaj*, Janos Kramar, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron Courville, Chris Pal. ICLR2017. [pdf] [poster] [code]
I've co-organized several workshops:
- Rethinking ML Papers at ICLR 2021
- Climate Change: How Can AI Help? at ICML 2019
- LSMDC (Large Scale Movie Description Challenge) workshop at ECCV2016 and ICCV2017
- Joint Workshop on Storytelling with Images and Videos
- Joint Women in Deep Learning workshop at the Deep Learning Summer School 2016
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.
LSMDC2016 - Fill in the Blank Challenge. Joint 2nd Workshop on Storytelling with Images and Videos (VisStory) at ECCV. 2016/10. [slides]
Zoneout: Regularizing RNNs by randomly preserving hidden activations. Deep Learning Summer School. 2016/08. [slides]
BRAINS (anatomy, structure, function, and evolution). University of Montreal. 2016/06. [slides]
Neuroscience and biology for deep learning. University of Montreal. 2016/04. [slides]
Introducing "neurotransmitters" to an artificial neural network for modular concept learning and more accurate classification. Research week, Bishop's University, Sherbrooke, QC. (1st prize in poster competition) 2014/02.
Intelligent data analysis broadens our understanding of the world (2nd prize in oral competition) 2014/02.
I was a TA for the following classes during PhD:
- Deep Learning
- Artificial Intelligence
- Introduction to Machine Learning
During undergrad and master's:
- CSC211 Introduction to Programming
- CSC103 Interactive Web Page Design
- FIN218 Digital Imaging
- PHY101 Introductory Statistics
- BIO349 Invertebrate Zoology
- ESG226 Oceans I
- BIO110 Genetics
- BIO116 Diversity of Life
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.
Prediction and generation of sound with LSTMs: end-of-term project for a deep learning course. [website] (research blog) [code] (based heavily on johnarevalo's code in blocks for RNN-char-prediction, modified to take and generate sound)
S.E.A.N.N. (Software Engineering Artificial Neural Network) group project: Draw a digit and a trained neural network will tell you what probability it assigns to that number being [0-9]. [website] [code]