Homepage of Levent Sagun

As of October 2017, I am a postdoctoral fellow jointly in Paris at ENS-Paris and CEA-Saclay, and in Lausanne at EPFL. The position is part of the Simons Collaboration Cracking the Glass Problem. In May 2017, I finished my PhD at the Courant Institute of Mathematical Sciences. I also did a research internship at FAIR over the summer of 2016.

Here is my CV and Google Scholar page. Code for some of the projects above are available at [GitHub]


Email: sagun [at] cims [dot] nyu [dot] edu

Research Interests

Probability, statistical mechanics and deep learning from the energy landscape point-of-view. Applications of machine learning in social sciences.


Explorations on high dimensional landscapes [ArXiv]
Levent Sagun, V. Ugur Guney, Gerard Ben Arous, Yann LeCun
ICLR 2015 Workshop Poster

Universal halting times in optimization and machine learning [AMS] [ArXiv]
Levent Sagun, Thomas Trogdon, Yann LeCun
Quart. Appl. Math. 76 (2018), 289-301
ICML 2016 Optimization Workshop

Early Predictability of Asylum Court Decisions [SSRN] [ICAIL]
Matthew Dunn, Levent Sagun, Hale Sirin, Daniel Chen
ICAIL 2017, Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law, Pages 233-236

Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond [ArXiv] [OpenReview]
Levent Sagun, Leon Bottou, Yann LeCun
Preprint, 2016

Entropy-SGD: Biasing Gradient Descent Into Wide Valleys [ArXiv] [OpenReview]
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, Riccardo Zecchina
ICLR 2017 Conference Paper

SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine [ArXiv][data]
Matthew Dunn, Levent Sagun, Mike Higgins, Ugur Guney, Volkan Cirik, Kyunghyun Cho
Preprint, 2017

Perspective: Energy Landscapes for Machine Learning [ArXiv] [PCCP]
Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales
Physical Chemistry Chemical Physics, 19, 12585-12603, 2017

Empirical Analysis of the Hessian of Over-Parametrized Neural Networks [ArXiv] [OpenReview]
Levent Sagun, Utku Evci, Ugur Guney, Yann Dauphin, Leon Bottou
ICLR 2018 Workshop Poster

Comparing Dynamics: Deep Neural Networks versus Glassy Systems [ArXiv]
Marco Baity-Jesi, Levent Sagun, Mario Geiger, Stefano Spigler, Gerard Ben Arous, Chiara Cammarota, Yann LeCun, Matthieu Wyart, Giulio Biroli
ICML 2018 Conference Paper

Easing non-convex optimization with neural networks [OpenReview]
David Lopez-Paz, Levent Sagun
ICLR 2018 Workshop Poster

The jamming transition as a paradigm to understand the loss landscape of deep neural networks [ArXiv]
Mario Geiger, Stefano Spigler, Stephane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart
Preprint, 2018

A jamming transition from under- to over-parametrization affects loss landscape and generalization [ArXiv]
Stefano Spigler, Mario Geiger, Stephane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart
Integration of Deep Learning Theories, NeurIPS Workshop 2018


Statistical and Mathematical Methods, Center for Data Science at NYU [fall 2015, fall 2016]
Machine Learning, Center for Data Science at NYU [spring 2016]
Theory of Probability, Courant Institute [fall 2016, fall 2014]
Probability and Statistics, Courant Institute [spring 2015]
Introduction to Mathematical Analysis, Courant Institute [spring 2014]
Written Exam Workshop, Courant Institute [fall 2013]