# Home

Welcome to my homepage. Since September 2015, I am an Assistant Professor at ENSAI, Bruz, France and an academic member of the Crest lab. I am working in the field of statistical learning/non-parametric statistics. I focus in particular on the problem of hyper-parameter tuning for learning algorithms. I try to obtain methodological improvements from an in-depth theoretical understanding of model selection issues. So far, I studied the so-called Slope Heuristics as well as resampling (penalization) methods. A central theoretical aspect of this line of research, based on Empirical Process Theory, is to obtain precise concentration bounds for the excess risk of M-estimators.

I also work on the related subjects of learning from Markov chains, robust learning (i.e. learning with heavy tailed and/or corrupted data), sketching for massive data, Bregman clustering, Small-ball approach in Learning Theory and non-parametric statistics, as well a some probabilistic inequalities linked to log-concave measures.

Since 2016, I am an elected member of the "groupe stat math" which is a group of researcher aiming at promoting mathematical statistics in France and on behalf of the French statistical society (Société Française de Statistique, SfDS).

Since 2016, I am also an elected member of the Crest lab council ("conseil de laboratoire du Crest").

Since sept. 2015, I co-organize the Statistical seminar in Rennes.

Email: asaumardatgmaildotcom

Phone: **+33 (0)2 99 05 32 10**

### Conference Deep Learning: from theory to practice

**Research topics**

- statistical learning theory
- model selection
- slope heuristics
- resampling methods
- small-ball approach in learning theory
- learning from Markov chains
- robust learning (MOM principle)
- sketching for learning with massive data
- Bregman clustering
- empirical process theory
- concentration inequalities
- functional inequalities
- Stein's method