Portrait de Tegan Maharaj

Tegan Maharaj

Membre académique principal
Professeure adjointe en apprentissage automatique, HEC Montréal, Département de sciences de la décision
Sujets de recherche
Apprentissage de représentations
Apprentissage multimodal
Apprentissage profond
Systèmes dynamiques
Théorie de l'apprentissage automatique

Biographie

Je suis professeure adjointe au Département de sciences de la décision à HEC Montréal. Mes objectifs de recherche sont de contribuer à la compréhension et aux techniques de la science du développement responsable de l’IA, tout en appliquant utilement l’IA à des problèmes écologiques à fort impact liés au changement climatique, à l’épidémiologie, à l’alignement de l’IA et à l’évaluation des impacts écologiques. Mes travaux récents portent sur deux thèmes : l’utilisation de modèles profonds pour l’analyse des politiques et l’atténuation des risques; et la conception de données ou d’environnements de tests unitaires pour évaluer empiriquement le comportement d’apprentissage ou simuler le déploiement d’un système d’IA. N’hésitez pas à me contacter pour toute collaboration dans ces domaines.

Je suis généralement intéressée par l’étude de ce qui « entre » dans les modèles profonds : non seulement les données, mais l’environnement d’apprentissage plus globalement, qui comprend la conception/spécification des tâches, la fonction de perte et la régularisation, ainsi que le contexte sociétal du déploiement, notamment les considérations de confidentialité, les tendances et les incitatifs, les normes et les préjugés humains. Je suis préoccupée et passionnée par l’éthique de l’IA, la sécurité et l’application de l’apprentissage machine à la gestion de l’environnement, à la santé et au bien-être social.

Étudiants actuels

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :

Publications

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Shoaib Ahmed Siddiqui
Nitarshan Rajkumar
Sara Hooker
Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or r… (voir plus)aw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
Reinald Kim
Mirella Lapata. 2020
Un-611
Emmanuel Bengio
Maxinder S. Kan-620
Asja Fischer
Somnath Basu
Roy Chowdhury
Chao Zhao
Tanya Goyal
Junyi Jiacheng Xu
Jessy Li
Ivor Wai-hung Tsang
James T. Kwok
Neil Houlsby
Andrei Giurgiu
Stanisław Jastrzębski … (voir 22 de plus)
Bruna Morrone
Quentin de Laroussilhe
Mona Gesmundo
Attariyan Sylvain
Gelly
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Funtow-900 Joe
Sam Davison
Patrick Shleifer
Von Platen
Clara Ma
Yacine Jernite
Julien Plu
Canwen Xu
Opinion summarization research has primar-001 ily focused on generating summaries reflect-002 ing important opinions from customer reviews 0… (voir plus)03 without paying much attention to the writing 004 style. In this paper, we propose the stylized 005 opinion summarization task, which aims to 006 generate a summary of customer reviews in 007 the desired (e.g., professional) writing style. 008 To tackle the difficulty in collecting customer 009 and professional review pairs, we develop a 010 non-parallel training framework, Noisy Pair-011 ing and Partial Supervision ( NAPA ), which 012 trains a stylized opinion summarization sys-013 tem from non-parallel customer and profes-014 sional review sets. We create a benchmark P RO - 015 S UM by collecting customer and professional 016 reviews from Yelp and Michelin. Experimental 017 results on P RO S UM and FewSum demonstrate 018 that our non-parallel training framework con-019 sistently improves both automatic and human 020 evaluations, successfully building a stylized 021 opinion summarization model that can gener-022 ate professionally-written summaries from cus-023 tomer reviews. 024
Tackling Climate Change with Machine Learning
Priya L. Donti
Lynn H. Kaack
Kelly Kochanski
Alexandre Lacoste
Kris Sankaran
Andrew Slavin Ross
Nikola Milojevic-Dupont
Natasha Jaques
Anna Waldman-Brown
Alexandra Luccioni
Evan David Sherwin
S. Karthik Mukkavilli
Konrad Paul Kording
Carla P. Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (voir 2 de plus)
Jennifer T Chayes
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (voir plus) we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
Predicting Infectiousness for Proactive Contact Tracing
Prateek Gupta
Nasim Rahaman
Martin Weiss
Tristan Deleu
Meng Qu
Victor Schmidt
Pierre-Luc St-Charles
Hannah Alsdurf
Olexa Bilaniuk
gaetan caron
pierre luc carrier
Joumana Ghosn
satya ortiz gagne
Bernhard Schölkopf … (voir 3 de plus)
abhinav sharma
andrew williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing
Prateek Gupta
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
gaetan caron
pierre luc carrier
satya ortiz gagne
Marc-Andre Rousseau
Joumana Ghosn
Yang Zhang
Bernhard Schölkopf
Joanna Merckx
COVI White Paper
Hannah Alsdurf
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (voir 3 de plus)
Martin Weiss
Yun William Yu
COVI White Paper-Version 1.1
Hannah Alsdurf
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (voir 3 de plus)
Martin Weiss
Yun William Yu
The SARS-CoV-2 (Covid-19) pandemic has resulted in significant strain on health care and public health institutions around the world. Contac… (voir plus)t tracing is an essential tool for public health officials and local communities to change the course of the Covid-19 pandemic. Standard manual contact tracing of people infected with Covid-19, while the current gold standard, has significant challenges that limit the ability of public health authorities to minimize community infections. Personalized peer-to-peer contact tracing through the use of mobile applications has the potential to shift the paradigm of Covid-19 community spread. Although some countries have deployed centralized tracking systems through either GPS or Bluetooth, more privacy-protecting decentralized systems offer much of the same benefit without concentrating data in the hands of a state authority or in for-profit corporations. Additionally, machine learning methods can be used to circumvent some of the limitations of standard digital tracing by incorporating many clues (including medical conditions, self-reported symptoms, and numerous encounters with people at different risk levels, for different durations and distances) and their uncertainty into a more graded and precise estimation of infection and contagion risk. The estimated risk can be used to provide early risk awareness, personalized recommendations and relevant information to the user and connect them to health services. Finally, the non-identifying data about these risks can inform detailed epidemiological models trained jointly with the machine learning predictor, and these models can provide statistical evidence for the interaction and importance of different factors involved in the transmission of the disease. They can also be used to monitor, evaluate and optimize different health policy and confinement/deconfinement scenarios according to medical and economic productivity indicators. However, such a strategy based on mobile apps and machine learning should proactively mitigate potential ethical and privacy risks, which could have substantial impacts on society (not only impacts on health but also impacts such as stigmatization and abuse of personal data). Here, we present an overview of the rationale, design, ethical considerations and privacy strategy of ‘COVI,’ a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada. Addendum 2020-07-14: The government of Canada has declined to endorse COVI and will be promoting a different app for decentralized contact tracing. In the interest of preventing fragmentation of the app landscape, COVI will therefore not be deployed to end users. We are currently still in the process of finalizing the project, and plan to release our code and models for academic consumption and to make them accessible to other States should they wish to deploy an app based on or inspired by said code and models. University of Ottawa, Mila, Université de Montréal, The Alan Turing Institute, University of Oxford, University of Pennsylvania, McGill University, Borden Ladner Gervais LLP, The Decision Lab, HEC Montréal, Max Planck Institute, Libéo, University of Toronto. Corresponding author general: richard.janda@mcgill.ca Corresponding author for public health: abhinav.sharma@mcgill.ca Corresponding author for privacy: ywyu@math.toronto.edu Corresponding author for machine learning: yoshua.bengio@mila.quebec Corresponding author for user perspective: brooke@thedecisionlab.com Corresponding author for technical implementation: jean-francois.rousseau@libeo.com 1 ar X iv :2 00 5. 08 50 2v 2 [ cs .C R ] 2 7 Ju l 2 02 0