Portrait de Golnoosh Farnadi

Golnoosh Farnadi

Membre académique principal
farnadig@mila.quebec
Chaire en IA Canada-CIFAR
Professeure adjointe, McGill University, École d'informatique
Professeure associée, Université de Montréal, Département d'informatique et de recherche opérationnelle
Chercheuse invitée, Google
Sujets de recherche
Apprentissage profond
Modèles génératifs
Site web
Google Scholar
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Biographie

Golnoosh Farnadi est professeure associée à l'École d'informatique de l'Université McGill et professeure associée à l'Université de Montréal. Elle est membre académique principal à Mila - Institut québécois d'intelligence artificielle et est titulaire d'une chaire CIFAR d'intelligence artificielle au Canada.

Mme Farnadi a fondé le laboratoire EQUAL à Mila / Université McGill, dont elle est l'une des principales chercheuses. Le laboratoire EQUAL (EQuity & EQuality Using AI and Learning algorithms) est un laboratoire de recherche de pointe dédié à l'avancement des domaines de l'équité algorithmique et de l'IA responsable.

Étudiants actuels

Mina Arzaghi
Doctorat - HEC
Site web
Github
Florian Carichon
Postdoctorat - McGill
Khaoula Chehbouni
Doctorat - McGill
Co-superviseur⋅e :
Jackie Cheung
Site web
Github
Google Scholar
Chataigner Cléa
Doctorat - McGill
Co-superviseur⋅e :
Pablo Piantanida
Alireza Dehghanpour Farashah
Doctorat - McGill
Co-superviseur⋅e :
Negar Rostamzadeh
Site web
Github
Marylou Fauchard
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Margarida Carvalho
Prakhar Ganesh
Doctorat - McGill
Site web
Github
Google Scholar
Aly Kassem
Collaborateur·rice de recherche - UWindsor
Site web
Github
Google Scholar
Aditi Khandelwal
Doctorat - McGill
Co-superviseur⋅e :
Siva Reddy
Google Scholar
Saber Malekmohammadi
Collaborateur·rice de recherche - McGill
Yash Raj More
Collaborateur·rice alumni - UdeM
Edie Pearman
Visiteur de recherche indépendant - McGill university
Ameline Ramesan
Collaborateur·rice de recherche - McGill
Github
Soumya Sharma
Doctorat - McGill
Co-superviseur⋅e :
Adriana Romero Soriano
Site web
Github
Google Scholar
Zhuan Shi
Postdoctorat - McGill
William St-Arnaud
Doctorat - UdeM
Co-superviseur⋅e :
Margarida Carvalho
Emma Tomiuk Tomiuk
Maîtrise recherche - McGill

Publications

Individual Fairness in Kidney Exchange Programs
Golnoosh Farnadi
William St-Arnaud
Behrouz Babaki
Margarida Carvalho
Kidney transplant is the preferred method of treatment for patients suffering from kidney failure. However, not all patients can find a dono… (voir plus)r which matches their physiological characteristics. Kidney exchange programs (KEPs) seek to match such incompatible patient-donor pairs together, usually with the main objective of maximizing the total number of transplants. Since selecting one optimal solution translates to a decision on who receives a transplant, it has a major effect on the lives of patients. The current practice in selecting an optimal solution does not necessarily ensure fairness in the selection process. In this paper, the existence of multiple optimal plans for a KEP is explored as a mean to achieve individual fairness. We propose the use of randomized policies for selecting an optimal solution in which patients' equal opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. The advantages of our proposed method over the common practice of using the optimal solution obtained by a solver are stressed through computational experiments. Our methodology enables decision makers to fully control KEP outcomes, overcoming any potential bias or vulnerability intrinsic to a deterministic solver.
2021-05-17
AAAI Conference on Artificial Intelligence (publié)
doi.org
A Unifying Framework for Fairness-Aware Influence Maximization
Golnoosh Farnadi
Behrouz Babaki
Michel Gendreau
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (voir plus)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.
2020-04-19
Companion Proceedings of the Web Conference 2020 (publié)
doi.org
Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration
Golnoosh Farnadi
Behrouz Babaki
Margarida Carvalho
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (voir plus)such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.
2019-12-31
(publié)
www.semanticscholar.org