BGSMath postdoctoral call 2017-2019

bgsm_squareThe Barcelona Graduate School of Mathematics (BGSMath) opens a call for two postdoctoral positions in the areas that include probability. Successful applicants will join the  BGSMath research group of their choice in one of the five BGSMath institutions in Barcelona, which includes our group. The deadline: January 8th 2017. For more information see here.

BGSMath has also an open call for a Banco de Santander postdoctoral position. The deadline for application is: 23rd December 2016. The main requirement for this call is that the Ph.D. degree must have been awarded between January 1st, 2011, and December 31st, 2013. For more information see here.

Manuel Gomez Rodriguez on Smart Broadcasting in Social Networks

portrait_gomezIn the next Statistics and Operation Research Seminar Manuel Gomez Rodriguez will talk about “RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks”.

When: Tuesday, November 15, at 12:00pm     Where: 20.137.

Manuel Gomez Rodriguez is a faculty at Max Planck Institute for Software Systems. Manuel develops machine learning and large-scale data mining methods for the analysis, modeling and control of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including an Outstanding Paper Award at NIPS’13 and a Best Research Paper Honorable Mention at KDD’10. Manuel holds a PhD in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid.

Local asymptotic normality for an ergodic diffusion with jumps

In a recent paper Eulalia Nualart with co-authors consider a multidimensional ergodic diffusion with jumps driven by a Brownian motion and a Poisson random measure associated with a compound Poisson process, whose drift coefficient depends on an unknown parameter. They show that the local asymptotic normality property holds in this case. The paper has just been accepted for publication in Statistics: A Journal of Theoretical and Applied Statistics. The arXiv version can be found here.

“Maximum likelihood estimation for linear Gaussian covariance models” to appear in Journal of Royal Statistical Society – Series B

In a new paper  Piotr Zwiernik, Caroline Uhler, and Donald Richards study the Gaussian models with linear structure of the covariance matrix. Maximum likelihood estimation for this class of models leads to a non-convex optimization problem which typically has many local maxima. Using recent results on the asymptotic distribution of extreme eigenvalues of the Wishart distribution, they provide sufficient conditions for any hill-climbing method to converge to the global maximum. An important consequence of this analysis is that for sample sizes n14p, maximum likelihood estimation for linear Gaussian covariance models behaves as if it were a convex optimization problem. The paper is going to appear in Journal of Royal Statistical Society – Series B; it is available here.

David Rossell joining the statistics group

01 David Rossell is a recipient of this year’s Ramon y Cajal fellowship and he will join our group in September as a visiting professor. Prof. Rossell is on leave from the University of Warwick where he is an Associate Professor in the Statistics Department. His research interests include experimental design, model building and selection, and dimensionality reduction, all with emphasis on high-throughput Bioinformatics experiments and the Bayesian approach.