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

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]