On September 12 Elisa Alòs will give a special invited talk at the Vienna congress on Mathematical Finance (WU Wien). The conference will bring together leading experts from various fields of Mathematical Finance.
Gábor Lugosi is going to be one of the lecturers in the upcoming 47th Probability Summer School in Saint Flour in July 2017 (more details will follow shortly). The Saint-Flour Summer School on Probability Theory and Statistics is the most famous international school on this domain in the world. The first one was held in 1971, and there was one such school each year since.
Gábor Lugosi and Piotr Zwiernik are involved in two research programs that have just received funding from BGSMath in the framework of the “María de Maeztu“ grant. BGSMath will sponsor in total three programs in 2017. Gábor will co-organize a 5-week program “Random discrete structures and beyond” and Piotr will co-organize a 4-week program “Algebraic and combinatorial phylogenetics“. More details will follow soon.
Mihalis Markakis received the “Juan de la Cierva-Incorporación” fellowship by the Spanish Ministry of Economy and Competitiveness. This is a two-year grant that offers reduction in teaching load and a budget for research expenses. Congratulations!
In the next Statistics and Operation Research Seminar Florian Simatos will talk about “Delay performance of queue-based CSMA protocols”.
When: Thursday, June 9, at 12:00pm Where: 24.021.
Dr Simatos obtained his PhD at INRIA (France) in 2009 and held research positions at the Center for Mathematics and Computer Science (CWI) and at Eindhoven University of Technology (TU/e). He is the recipient of the 2014 ACM SIGMETRICS Rising Star Researcher Award. His research lies at the boundary of the performance evaluation and applied probability.
Next week Lorenzo Rosasco (Genoa/MIT) will give a talk on “Less is more: optimal learning with stochastic projection regularization”, which aims at presenting results from the following two papers:
When: Thursday, June 2, at 12pm. Where: 24.021.
The research of Dr. Rosasco focuses on studying theory and algorithms for machine learning. He has developed and analyzed methods to learn from small as well as large samples of high dimensional data, using analytical and probabilistic tools, within a multidisciplinary approach drawing concepts and techniques primarily from computer science but also from statistics, engineering and applied mathematics.
Totally positive distributions appeared independently in many context in statistics and they make a far generalization of a bivariate distribution with a positive covariance between the components. What seems to be a severe restrictions, turns out to be a natural assumption when additional conditional independence is present.
In this paper Piotr Zwiernik and co-authors study positive dependence in the context of conditional independence and conditional independence models. This is a beginning of a greater program of understanding total positivity in the context of graphical models, which links to high-dimensional inference procedures.
Omiros Papaspiliopoulos is going to deliver a lecture in the Van Dantzig Seminar Series on 1 April at Delft University of Technology, the Netherlands.
The Van Dantzig seminar is a nationwide series of lectures in statistics, which features renowned international and local speakers, from the full width of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the “Theory of Collective Phenomena” (i.e. statistics) in Amsterdam. The seminar is held 4 to 6 times a year at various locations in the Netherlands.
Deep Learning (i.e. the return of Neural Networks part deux) is one of the most active and interesting areas in Machine Learning at the moment. Alexandros will provide an introduction to basic deep learning concepts and models, going over simple feedforward networks, convolutions, autoencoders up to recurrent networks for sequential data.
Note: There is another seminar on deep learning at UPF this week(on Friday). For more information see here.