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Preliminary program:

Tuesday, June 28, 2016

8:30-9:00 Registration
9:00 Opening
Opening address
Session 1: Queueing applications (chair: Giuliano Casale)
Bruce Jones, Sally McClean and David Stanford:
Caglar Tunc and Nail Akar:
10:15-10:50 Break
Session 2: Numerical methods (chair: Nail Akar)
Dario Bini, Guy Latouche and Beatrice Meini:
Gábor Horváth and Miklós Telek:
Nigel Bean, Giang Nguyen, Malgorzata O'Reilly and Vikram Sunkara:
Dario Andrea Bini, Stefano Massei and Leonardo Robol:
12:30-14:00 Lunch
Session 3: Fluid queues (chair: Giang Nguyen)
Aviva Samuelson, Malgorzata O'Reilly and Nigel Bean:
Eleonora Deiana, Guy Latouche and Marie-Ange Remiche:
Gábor Horváth:
Sarah Dendievel and Guy Latouche:
15:40-16:15 Break
Session 4: Asymptotics and stability (chair: Peter Taylor)
Yuanyuan Liu, Pengfei Wang and Yiqiang Zhao:
Masakiyo Miyazawa:
Alexander Rumyantsev and Evsey Morozov:
18:00-20:00 Boat trip on the Danube river

Wednesday, June 29, 2016

Invited talk
Session 5: Branching processes (chair: Beatrice Meini)
Peter Braunsteins and Sophie Hautphenne:
10:15-10:50 Break
Session 6: Discrete queues (chair: Malgorzata O'Reilly)
Li Xia, Qi-Ming He and Attahiru Alfa:
Rostislav Razumchik:
Nail Akar:
Qi-Ming He:
12:30-14:00 Lunch
Session 7: Systems with multiple queues (chair: David Stanford)
Malgorzata O'Reilly and Werner Scheinhardt:
Eleni Vatamidou, Ivo Adan, Maria Vlasiou and Bert Zwart:
Giuliano Casale, Gabor Horvath and Juan F. Perez:
Kevin Granville and Steve Drekic:
15:40-16:15 Break
Session 8: Markov modulated Brownian motion (chair: Masakiyo Miyazawa)
Nigel Bean, Giang Nguyen and Federico Poloni:
Guy Latouche and Giang Nguyen:
Giang T. Nguyen and Federico Poloni:
19:00- Conference banquet

Thursday, June 30, 2016

Session 9: Risk, insurance and survival systems (chair: Srinivas Chakravarthy)
Florin Avram, Andrei Badescu, Martijn Pistorius and Landy Rabehasaina:
Mogens Bladt and Oscar Peralta:
Salim Khamis Serhan, Bo Friis Nielsen and Mogens Bladt:
10:15-10:50 Break
Session 10: Distributions, processes and fitting (chair: Guy Latouche)
Rosa Lillo, Joanna Rodriguez and Pepa Ramírez-Cobo:
Bo Friis Nielsen and Azucena Campillo Navarro:
Luz Judith Rodriguez Esparza and Fernando Baltazar-Larios:
Sophie Hautphenne and Katharine Turner:
12:30-14:00 Lunch
Session 11: Markov models (chair: Dario Bini)
Jevgenijs Ivanovs, Guy Latouche and Peter Taylor:
Haibo Yu:
Stella Kapodistria and Zbigniew Palmowski:
15:15-15:50 Break
Session 12: Population models (chair: Sophie Hautphenne)
Matthieu Simon and Claude Lefèvre:

Opening address

László Gerencsér:

An interaction between queueing and change detection

A basic feature of a single server queue is waiting time recorded at the time of arrival of a new customer, following a simple non-linear dynamics. By a remarkable coincidence the same dynamics is obtained for what is called the the Page-Hinkley detector designed for real-time change detection of stochastic processes. According to this an alarm is given if the detector (an equivalent to waiting time) exceeds a prefixed threshold.

A result on the empirical tail-probabilities of the detector under very general technical conditions will be presented, assuming no change, thus providing an upper bound for the false alarm rate. These results translate to results on empirical large deviations for waiting times. The above mentioned technical condition can be verified in a variety of interesting special cases to be briefly presented.


Gerencsér, L. and Prosdocimi, C. (2011): Input-output properties of the Page-Hinkley detector. Systems and Control Letters, 60 (7). pp. 486-491.
Gerencsér, L., Prosdocimi, C. and Vágó, Zs. (2013): Change detection for finite dimensional Gaussian linear systems - a bound for the almost sure false alarm rate. In: Proc. of the 2013 European Control Conference (ECC), 2013-07-17 - 2013-07-19, Zürich, Switzerland.

Short bio:

Kousha Etessami

László Gerencsér received his M.Sc. and Doctorate degree in mathematics at  Eötvös Loránd University, Budapest, Hungary in 1969 and 1970, respectively. Since 1970 he has been with MTA SZTAKI (Computer and Automation Research Institute, Hungarian Academy of Sciences) with intermissions abroad.

His first research papers are written in graph theory, including a widely cited paper with A.Gyárfás on a generalized Ramsey problem. He started his academic carrier in operations research, from which he moved to control and systems theory, in particular to system identification.

In 1986 he held a one year visiting position at the Department of Mathematics, Chalmers University of Technology, Göteborg, Sweden. A major milestone in his academic life was a visit to the Department of Electrical Engineering, McGill University, Montreal, Quebec, Canada, from 1988 to 1991, as a visiting professor.

He was awarded a Széchenyi Professorship with  Eötvös Loránd University for the period of 1997 to 2001. His international activities include membership of the Conference Editorial Board of the IEEE Control Systems Society, 1998-2002, and his serving as an associate editor for the SIAM Journal of Control and Optimization, 1998-2004, and for the IEEE Transactions on Automatic Control, 2001-2004.

He was the IPC Chair of the 19th International symposium on Mathematical Theory of Networks and Systems (MTNS 2010) Budapest, 2010. He is also the founder of the multidisciplinary Conferences on Applied Mathematics of the János Bolyai Mathematical Society.

Invited talk

Kousha Etessami:

The computational complexity of analyzing infinite-state structured Markov chains and structured Markov decision processes

I will survey a series of results over recent years on the computational complexity of key analysis problems for several families of infinite-state structured Markov chains (MCs) and structured Markov decision processes (MDPs).

A key aspect of our results is algorithmic bounds for computing the least fixed point (the least non-negative solution) for monotone systems of nonlinear (min/max)-polynomial equations which arise for these stochastic models and MDPs.

In particular, I will describe algorithms based on Newton's method, combined with P-time preprocessing steps, which yield polynomial time algorithms (in the standard Turing model of computation) for computing, to arbitrary desired accuracy, the G matrix of quasi-birth death processes (QBDs), and the vector of extinction probabilities of multi-type branching processes (or Markovian trees).

We then consider extensions of these purely stochastic models to MDPs. We describe a Generalized Newton's Method (GNM), which employs linear programming in each iteration, and we use GNM to obtain a P-time algorithm for computing, to desired accuracy, the vector of optimal (maximum or minimum) extinction probabilities for a given Branching MDP.

We also study one-counter MDPs, which generalize discrete-time QBDs with control, and we give algorithms and complexity bounds for both qualitative and quantitative analysis of optimal termination probabilities for one-counter MDPs.

Finally, we consider a model more general than the above stochastic models and MDPs, called recursive Markov chains (RMCs) and Recursive MDPs (RMDPs). RMCs are closely related to probabilistic pushdown systems and to tree-like-QBDs. We show that, in the worst-case, any non-trivial approximation of the termination probabilities of a RMC (or tree-like-QBD) is PosSLP-hard, and thus approximation in NP would already yield a breakthrough in exact numerical computation. For RMDPs (or tree-like-QBD-MDPs), we show that computing any non-trivial approximation of their optimal termination probability is not even computable at all (with any complexity).

(This talk describes a series of results together with Alistair Stewart and Mihalis Yannakakis, as well as some older results with other co-authors.)

Short bio:

Kousha Etessami

Kousha Etessami is an associate professor (reader) in the Laboratory for Foundations of Computer Science in the School of Informatics at the University of Edinburgh. In the past he was a member of the research staff at Bell Laboratories. His research interests are in theoretical computer science, including: algorithmic game theory, equilibrium computation, algorithmic analysis and verification of probabilistic systems, Markov decision processes, stochastic games, analysis of infinite-state systems, model checking, automata, logic, and computational complexity theory.