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: levent [dot] sagun [at] epfl [dot] ch

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*

**Scaling description of generalization with number of parameters in deep learning**
[arXiv]

Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, Stephane d'Ascoli, Giulio Biroli, Clement Hongler, Matthieu Wyart

* Preprint, 2019 *

**A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks**
[arXiv]

Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban

* Preprint, 2019*

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]

A short twitter thread on duplicate images in CIFAR100 with different labels:

1 - Rethinking about "Understanding deep learning requires rethinking generalization" @beenwrekt and colleagues after some curious results of ResNets on CIFAR100 that required measuring the training accuracy: It was never 100%!

— Levent Sagun (@leventsagun) September 16, 2018

Here are the pairs of indices of duplicates as provided by the default `torchvision.datasets.CIFAR100`

(note that this is only on the training set, I didn't check the test set for duplicates):

```
[[4348, 30931], [8393, 36874], [8599, 22657], [9012, 31128], [16646, 31828], [17688, 41874], [18461, 46752], [20635, 32666], [23947, 33638], [25218, 46851], [27737, 47636], [28293, 41860], [30418, 47806], [31227, 34187]]
```