# Mathematician

Designing new tools for high-dimensional time series.
Applying deep reinforcement learning in biomechanics and computer vision in movement analysis.
A co-founder of DeepArt - the neural style transfer company.
Constantly looking for new opportunities and challenges 🚀

# Recent projects

My main research involves development of new high-dimensional statistical tools.

However, I am also involved in a variety of other projects involving cutting-edge computational methods.

#### DeepArt - Neural style platform

Computational methods for transfering a painting style to any photo

#### FreeBoost - MRI segmentation

Better, faster and cheaper MRI segmentation using deep learning

#### Golf swing analysis app

Using modern methods for pose estimation to evaluate a golf swing

#### Learning how to run (NIPS)

My neuromuscular environments for analyzing motor control. Accepted as an official NIPS challenge in 2017

# Media

Popular press coverage of my projects

# Selected Publications

### Principal component analysis of periodically correlated functional time series

Within the framework of functional data analysis, we develop principal component analysis for periodically correlated time series of functions.
in progress, 2017.

### Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking

Orchestration load is the effort a teacher spends in coordinating multiple activities and learning processes. It has been proposed as a construct to evaluate the usability of learning technologies at the classroom level, in the same way that cognitive load is used as a measure of usability at the individual level.
IEEE Transactions on Learning Technologies, 2017.

### Dynamic Functional Principal Component

In this paper, we address the problem of dimension reduction for time series of functional data ($X_t$: $t$ ∈ $Z$). Such functional time series frequently arise, e.g., when a continuous-time process is segmented into some smaller natural units, such as days.

### MOOC video interaction patterns: What do they tell us?

For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions.
Design for teaching and learning in a networked world, 2015.

### A note on estimation in Hilbertian linear models

We study estimation and prediction in linear models where the response and the regressor variable both take values in some Hilbert space.
Scandinavian journal of statistics, 2015.

# Recent Publications

. Principal component analysis of periodically correlated functional time series. in progress, 2017.

. Orchestration Load Indicators and Patterns: In-the-wild Studies Using Mobile Eye-tracking. IEEE Transactions on Learning Technologies, 2017.

. Estimation in functional lagged regression. Journal of time series analysis, 2015.

. Dynamic Functional Principal Component. In JRSSB, 2015.

. How Do In-video Interactions Reflect Perceived Video Difficulty?. EMOOCs 2015, 2015.

. MOOC video interaction patterns: What do they tell us?. Design for teaching and learning in a networked world, 2015.

. Translating head motion into attention-towards processing of student’s body-language. EDM, 2015.

. A note on estimation in Hilbertian linear models. Scandinavian journal of statistics, 2015.

# Teaching

I was a teaching instructor for the following courses:

• CS-411: Digital education & learning analytics, fall 2014, EPFL
• MATHF309: Analyse Multivariée, spring 2014, ULB (in french)
• STATF407: Stochastic Models, fall 2013, ULB
• STATF407: Stochastic Models, fall 2012, ULB