Portrait de Tegan Maharaj

Tegan Maharaj

Membre affilié
Professeure titulaire, University of Toronto
Site web
Google Scholar

Biographie

Je suis professeur adjoint à la Faculté de l’information de l’Université de Toronto.

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 intéressé 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 préoccupé et passionné 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.

Publications

COVI White Paper-Version 1.1
Hannah Alsdurf
Yoshua Bengio
Tristan Deleu
Prateek Gupta
Daphne Ippolito
Richard Janda
Max Jarvie
Tyler J. Kolody
Sekoul Krastev
Tegan Maharaj
Robert Obryk
Dan Pilat
Valerie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
Irina Rish
Jean-franois Rousseau
abhinav sharma
Brooke Struck … (voir 3 de plus)
Jian Tang
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
2020-01-01
(publié)
www.semanticscholar.org