Portrait of Golnoosh Farnadi

Golnoosh Farnadi

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, McGill University, School of Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Visiting Faculty Researcher, Google
Research Topics
Deep Learning
Generative Models

Biography

Golnoosh Farnadi is an assistant professor at the School of Computer Science, McGill University, and an adjunct professor at Université de Montréal. She is a core academic member of Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair.

Farnadi founded and is a principal investigator of the EQUAL lab at Mila / McGill University. The EQUAL lab (EQuity & EQuality Using AI and Learning algorithms) is a cutting-edge research laboratory dedicated to advancing the fields of algorithmic fairness and responsible AI.

Current Students

PhD - HEC Montréal
Postdoctorate - McGill University
Research Intern - McGill University
Master's Research - McGill University
Co-supervisor :
Collaborating researcher
Master's Research - Université de Montréal
Principal supervisor :
Collaborating researcher - UWindsor
PhD - McGill University
Co-supervisor :
Collaborating researcher - McGill University
Collaborating Alumni - Université de Montréal
Research Intern - McGill University
Master's Research - Polytechnique Montréal
Postdoctorate - McGill University
PhD - Université de Montréal
Co-supervisor :
Master's Research - Université de Montréal
Postdoctorate - Université de Montréal
Independent visiting researcher - HEC Montréal

Publications

Individual Fairness in Kidney Exchange Programs
William St-Arnaud
Behrouz Babaki
A Unifying Framework for Fairness-Aware Influence Maximization
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… (see more)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.
Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (see more)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.