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seminar by Professor Ian Dryden (University of Nottingham) on June 3rd (monday) at 11.15 in seminar room D123 (Exactum, Kumpula campus, University of Helsinki). Abstract.
Assistant Professor, Associate Professor or Professor in Applied Mathematics (tenure track)
Åbo Akademi University (ÅAU) is an internationally acknowledged research university with an extensive responsibility for providing education in Swedish in Finland. With its international research community and its strong Nordic ties, Åbo Akademi University has an acclaimed and recognized position within research and education both nationally and internationally.
The faculty of Science and Engineering is opening a position as Assistant Professor, Associate Professor or Professor in Applied Mathematics within the tenure-track career system, starting on January 1st 2020 (or as agreed). The position will be placed in Åbo (Turku), Finland.
The Faculty of Science and Engineering at Åbo Akademi University offers education within Biosciences, Pharmacy, Natural Sciences, Information Technology and Chemical Engineering. The education within the subject Mathematics and Statistics offers skills that are in demand within society, especially within the insurance- and financial sector, and the IT-industry. In addition to that, the education of subject teachers in mathematics and its development is one of the most important tasks within the subject, as it has an essential responsibility to supply the Swedish speaking community in Finland with subject teachers in mathematics. The research in mathematics at Åbo Akademi University covers different parts within both abstract mathematics and applied mathematics.
The tenure track-position is placed at the subject Mathematics and Statistics, which is a part of Natural Sciences. The subject is responsible for education on basic-, subject-, and advanced studies as well as research studies and thesis supervision on all levels. The research activity within the subject is dynamic and internationally recognized. The research is focused especially on functional analysis, with applications in complex analysis and system theory, stochastic processes with applications in financial and insurance mathematics as well as number theory with applications in coding theory.
The field of activity
The field of activity is in Applied Mathematics with a special emphasis on Probability Theory, Stochastic Processes and Mathematical Statistics, with applications in Financial and Insurance Mathematics. Strong candidates in other suitable fields of Applied Mathematics are also welcome to apply.
Stability and scaling limits for Markov processes and applications (1–3 cr)
Prof Matthieu Jonckheere (U Buenos Aires)
Three lectures during 24–26 Sep 2018, every day 10:15–12:00 (Mon, Tue, Wed)
Krossi lounge (M220), 2nd floor, Otakaari 1, Aalto University
Markov processes allow to study models of almost any dynamics: population dynamics, data networks, physical systems evolutions, biological networks, etc. We will study techniques to answer stability questions including techniques allowing to link some Markov processes to simpler systems (often a deterministic dynamical system) via space-time scaling.
1) Markov processes in discrete and continuous time
2) Stationary measures, reversibility
3) Martingales for Markov processes
4) Lyapunov functions
5) Scaling limits
6) Stochastic stability
Evaluation: You may get 1 cr for active participation at the lectures, and up to 3 cr by doing a project (to be agreed with the lecturer).
Registration: You can send an email to firstname.lastname@example.org or just show up at the first lecture.
Lecturer: Piotr Zwiernik (University Pompeu Fabra, Barcelona)
Latent tree models are graphical models defined on a tree, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. This lecture offers a concise introduction to the theory of latent tree models. I will emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned.
This lecture course is divided into three parts. In part 1, I will present basic combinatorial concepts related to trees and tree metrics. In part 2, I will define latent tree graphical models and discuss their basic properties. I will also discuss linear latent tree models which provide a convenient general family of distributions whose second-order moment structure is tree-like. In the last part I will present main ideas used in the design of learning procedures for this model class. This includes the structural EM algorithm and various distance based methods.
This course will be based on my book
P. Zwiernik, “Semialgebraic statistics and latent tree models”, Chapman&Hall, 2015,
and a forthcoming chapter in “Handbook of Graphical Models”, see also arXiv:1708.00847.