Aaron Mishkin

Il - He/him

Expertise

Optimization, Machine Learning
I am a postdoctoral fellow working with Martin Jaggi in the Machine Learning and Optimization laboratory at EPFL. I previously completed my PhD at Stanford, where I was supervised by Mert Pilanci, and my master's at the University of British Columbia, where I was supervised by Mark Schmidt. I am broadly interested in optimization for machine learning. My current research uses hidden convexity to understand optimality and sensitivity in neural networks. 

Outside of work, I can usually be found climbing, hiking, or skiing. 

Education

Computer Science

|

2013 – 2018 University of British Columbia
Directed by David Poole

Computer Science

| Optimization for Machine Learning

2018 – 2020 University of British Columbia
Directed by Mark Schmidt

Computer Science

| Optimization for Machine Learning

2020 – 2026 Stanford University
Directed by Mert Pilanci

Awards

Best Poster

ICCOPT

2025

Graduate Research Fellowship

National Science Foundation

2020

Canada Graduate Scholarships - Doctoral

Natural Sciences and Engineering Research Council of Canada (NSERC)

2020

Canada Graduate Scholarships - Master's Program

National Science and Engineering Research Council of Canada (NSERC)

2018

Selected publications

Exploring the loss landscape of regularized neural networks via convex duality

Sungyoon Kim, Aaron Mishkin, Mert Pilanci
Published in International Conference on Learning Representations in 2025

Directional Smoothness and Gradient Methods: Convergence and Adaptivity

Aaron Mishkin*, Ahmed Khaled*, Yuanhao Wang, Aaron Defazio, and Robert M. Gower
Published in Neural Information Processing Systems in 2024

Optimal Sets and Solution Paths of ReLU Networks

Aaron Mishkin and Mert Pilanci
Published in International Conference on Machine Learning in 2023

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates

Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Published in Neural Information Processing Systems in 2019