Since October 2014, I am a post-doctoral researcher on a Research in Paris fellowship at the INRIA offices in Paris, affiliated with the computer science department of the Ecole Normale Supérieure. There, I work with Alexandre d'Aspremont in the SIERRA team on topics at the intersection of optimization and statistics.

I obtained my Ph.D. early in 2014 working with Pierre-Antoine Absil and Vincent Blondel as an FNRS research fellow at the Unversité catholique de Louvain, in the department of mathematical engineering (part of the ICTEAM). My dissertation is entitled optimization and estimation on manifolds.

I look into the theory and applications of optimization, especially, optimization on manifolds (for which I develop a toolbox called Manopt) to solve computational problems. A reference in this field is the book Optimization Algorithms on Matrix Manifolds. This led me to study some applications, including curve fitting on manifolds (master thesis), low-rank matrix completion and synchronization of rotations (doctoral thesis). I collaborate with Amit Singer and his group at PACM (the program in applied and computational mathematics at Princeton University) on synchronization problems and semidefinite relaxations.

I hold a master's degree in mathematical engineering from the Ecole polytechnique de Louvain (ingénieur civil en mathématiques appliquées) (2008-2010). During my master's, I worked on video restoration in Benoît Macq's lab at UCLouvain (2008) and on mathematical seismology with Laurent Demanet in the mathematics department at MIT (2009).

Manopt: a Matlab toolbox for optimization on Manifolds

Manopt, available at manopt.org, is a user-friendly, open source and documented Matlab toolbox which can be used to leverage the power of modern Riemannian optimization algorithms with ease. Manopt won the ORBEL Wolsey Award 2014 for best open source operational research implementation.

Tell me more/less

Synchronization: estimating rotations from relative measurements

Synchronization is the problem of estimating elements $g_1, \ldots, g_N$ in a group $G$, given measurements of relative quantities: $h_{ij} \approx g_i^{}g_j^{-1}$. These elements are best visualized on a graph (undirected), where each element $g_i$ is a node and there exists an edge between two nodes $g_i$ and $g_j$ if a measurement $h_{ij}$ is available. I focus on $G = \mathrm{SO}(n)$, the group of rotations:

$$\mathrm{SO}(n) = \{ R \in \mathbb{R}^{n\times n} \colon R^TR = I_n \ \mathrm{ and } \ \operatorname{det}(R) = +1 \}.$$

SynchronizeMLE is the distribution of Matlab codes for this project, available under BSD license. It contains code both to perform the estimation and to compute Cramér-Rao bounds.

Tell me more/less

Riemannian Staircase: semidefinite programming with diagonal block constraints

We propose an algorithm to compute KKT points for problems of the form $$\min_X f(X)$$ with $X$ a symmetric matrix of size $n\times n$ such that $$X\succeq 0 \textrm{ and } X_{ii} = I_d \forall i,$$ meaning that the $d\times d$ diagonal blocks of $X$ are identity matrices. The cost function $f$ must be twice continuously differentiable. If $f$ is convex, KKT points are global optimizers.

The main idea is to attain a solution by tracking intermediate solutions of low-rank, increasing the rank as needed. This is in contrast with interior point methods, which work with full-rank matrices to ultimately converge to (often) low-rank solutions.

Here is our Matlab code for what we call the Riemannian staircase method. It is readily usable to solve such problems with $f(X) = \operatorname{Trace}(CX)$ and a pseudo-Huber-loss smoothed version of $f(X) = \sum_{(i,j)\in E} \|C_{ij}Y_j - Y_i\|_F$ (notice the absence of square). These two functions are concave (the linear cost is also convex), which promotes solutions at extreme points. The latter are typically of low rank. See also my slides.

Tell me more/less

Low-rank matrix completion

Let $M \in \mathbb{R}^{m\times n}$ be a matrix with low rank $r \ll \min(m, n)$. Low-rank matrix completion is the task of estimating (or recovering) $M$ from measurements $\hat M_{ij} \approx M_{ij}$ of a few entries $(i, j) \in \Omega$.

At NIPS 2011, we proposed RTRMC, a Riemannian trust-region method for low-rank matrix completion:


Tell me more/less

Curve fitting on manifolds: interpolation and regression

In this project, which was the topic of my master's thesis, we are given time-labeled points on a Riemannian manifold $\mathcal{M}$ (for example, on a sphere, on the group of rotations, on the set of positive-definite matrices, etc.): $p_1, \ldots, p_n$, associated to timestamps $t_1 \leq \ldots \leq t_n$. The goal is to propose a curve (a model) on the manifold, $\gamma \colon [t_1, t_n] \to \mathcal{M}$, such that the curve fits the data (exactly for interpolation, reasonably for regression): $\gamma(t_i) \approx p_i$ and such that $\gamma$ is smooth in some suitable sense. Interpolation and regression are fundamental operations in signals processing. They serve the goals of denoising and resampling acquired data. These tasks are well understood when the data belongs to a Euclidean space such as $\mathbb{R}^n$, but much less so when the data belongs to a nonlinear manifold.

Tell me more/less





Journal papers


Conference papers





Nicolas Boumal
23 Avenue d'Italie
75013 Paris, France

Office: 61 (5th floor)
E-mail: nicolasboumal@gmail.com

Random stuff

My officemate at UCL was Romain Hollanders.

Romain, Karim and I won 2nd place in the SIAM Math Matters: Apply It! 2011 contest, with our poster introducing the large public to the importance of math in monitoring fetal ECG's.

My Erdös number is 3, courtesy of my co-author and advisor Vincent Blondel.

Research will get you places! It got me in: Palo Alto, Boston, Princeton, London, Prague, Cannes, Lisbon, Milan, Dagstuhl, Granada, Sierra Nevada, Valencia, Berlin, Les Houches, Costa da Caparica, Paris, Florence, San Diego, Bordeaux, Montréal... and various places in Belgium (Louvain-la-Neuve, Leuven, Liège, La Roche, Mons, Knokke, Daverdisse, Spa, Namur...).

Teaching at UCL

  • Mathématiques 1 (FSAB1101), TA, autumn 2008, autumn 2009
  • Projet 1 (FSAB1501), TA, autumn 2010
  • Théorie des Matrices (INMA2380), TA, spring 2011, autumn 2013
  • Signaux et Systèmes (LFSAB1106), TA, autumn 2011
  • Analyse numérique : approximation, interpolation, intégration (LINMA2171), TA, autumn 2011 and 2012
  • Mathématiques 2 (LFSAB1102), TA, spring 2012
  • Modélisation et analyse des systèmes dynamiques (LINMA2370), TA, autumn 2012
  • Projet en ingénierie mathématique (LINMA2360), TA, spring 2012 and 2013
  • Projet en mathématiques appliquées (LINMA1375), TA, spring 2013
  • Systèmes dynamiques non linéaires (LINMA2361), TA, autumn 2013