Mehrnaz Mofakhami

I am a Visiting Researcher at ServiceNow Research and a master's student at Université de Montréal and Mila, where I am supervised by Prof. Gauthier Gidel and Prof. Ioannis Mitliagkas. Prior to coming to Montreal, I got my bachelor's degree at Sharif University of Technology in Computer Science.

I am interested in the fundamentals of machine learning, inference optimization and the development of efficient algorithms. I get excited about exploring how AI can be leveraged effectively and ethically to address real-world challenges and improve decision-making processes across various domains. My master's research revolved around Performative Prediction, a framework for learning models that actively shape the environment they interact with through their predictions. The primary objective of my research was to push the boundaries of this framework, exploring and identifying new domains where its application can be extended.

You can reach me at: mehrnaz (dot) mofakhami [at] mila.quebec

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Recent News

  • August 2024: I'm excited to announce that I'm serving as the Mentorship and Networking Program Chair for the Women in Machine Learning (WiML) workshop at NeurIPS 2024.
  • 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.
  • June 2024: I served as a reviewer for the ES-FoMO workshop during ICML 2024, and previously at AISTATS 2024.
  • 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.
  • November 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
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

We provided a method for performance control in early exiting and showed that larger models coupled with early exiting using this method can achieve lower prediction errors for the same computational budget as smaller models.

Performative Prediction with Neural Networks
Mehrnaz Mofakhami, Ioannis Mitliagkas, Gauthier Gidel
AISTATS 2023
paper / video

We analyzed the performative prediction framework in the presence of Neural Networks with non-convex loss function using functional analysis.

A preliminary version of this work was presented at NeurIPS 2022 workshop on Distribution Shifts.

Reproduction: Adversarial Example Games
Adversarial Machine Learning course
Report / code

In this project, I reproduced the simple setup described in the Adversarial Example Games paper on a binary classification task with logistic regression.

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.

Many thanks to Jon Barron!