Mehrnaz Mofakhami

Hello! I am a Researcher at Mila working at Gauthier Gidel's lab. I recently completed my master's at Université de Montréal and Mila under the supervision of Gauthier Gidel and Ioannis Mitliagkas. During my studies, I spent some time at ServiceNow Research as a Visiting Researcher, where I worked with João Monteiro and Valentina Zantedeschi. Prior to coming to Montreal, I received my bachelor's degree at Sharif University of Technology in Computer Science.

I am interested in the fundamentals of machine learning, where I can explore how AI can be used effectively and efficiently to improve decision-making across various domains. My research has evolved from studying predictive models that actively shape the environments they're designed to analyze to improving inference optimization techniques that balance computational efficiency with performance. Most recently, my focus has shifted to uncovering hidden vulnerabilities in large language models and how to make them more robust.

If there's anything you'd like to discuss with me, you can reach me at: mehrnaz (dot) mofakhami [at] mila.quebec

profile photo

News


August 2024
I'm excited to announce that I'm co-organizing the Women in Machine Learning (WiML) workshop at NeurIPS 2024 as the Mentorship and Networking Program Chair.
July 2024
I presented my work on "Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones" at the ES-FoMO workshop at ICML 2024 in Vienna.
May 2024
I am very grateful to have received the AI Scholarship for Excellence in Research from the University of Montreal's Graduate and Postdoctoral Studies.
March 2024
Happy to have received a full travel grant to attend the Cornell, Maryland, Max Planck Pre-doctoral Research School in Computer Science (CMMRS) in Summer 2024.
October 2023
I started an internship with ServiceNow Research at the Multimodal Foundation Models team.
Fall 2023
I got the merit-based Excellence Scholarship from DIRO (Department of Computer Science and Operational Research) at the University of Montreal for the third time in a row!
June 2023
I presented an introduction to the Performative Prediction framework and my research on this topic at a Google DeepMind Montreal Tea Talk.
September 2022
I joined the Space Committee at Mila to guide decisions surrounding the management of the space for students and profs.

Projects


A generative approach to LLM harmfulness detection with special red flag tokens
Sophie Xhonneux, David Dobre, Mehrnaz Mofakhami, Leo Schwinn, Gauthier Gidel
BuildingTrust Workshop, ICLR 2025
Paper
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas, João Monteiro, Valentina Zantedeschi
Efficient Systems for Foundation Models Workshop, ICML 2024
Paper
Tight Lower Bounds and Improved Convergence in Performative Prediction
Pedram Khorsandi, Rushil Gupta, Mehrnaz Mofakhami, Simon Lacoste-Julien, Gauthier Gidel
OPT for ML Workshop, NeurIPS 2024
Paper
Performative Prediction on Games and Mechanism Design
António Góis, Mehrnaz Mofakhami, Fernando P. Santos, Gauthier Gidel, Simon Lacoste-Julien
AISTATS 2025
Paper / Code
Performative Prediction with Neural Networks
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
AISTATS 2023
Paper / Video
Reproduction: Adversarial Example Games
Adversarial Machine Learning course
Report / Code
Reproduction: Tracking the World State with Recurrent Entity Networks
Mehrnaz Mofakhami, AmirHossein Yavari
EEML Summer School 2021 - Best poster award
Report / Code

Notes


Tutorial: An introduction to Robust and Trustworthy ML
Supplementary material for the Artificial Intelligence Course at SUT - Spring 2021

I wrote this short tutorial while I was a TA in the AI course at Sharif University of Technology. It is an introduction to the main topics in robust and trustworthy ML, including evasion and poisoning attacks, and mechanisms to defend against them.

Adversarial learning course - IFT 6164: Adversarial Examples: part 2

This scribe note is based on the lectures of Professor Gauthier Gidel in Adversarial Machine Learning course , Winter 2022.

Services