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AMSI-ANZIAM Lecture Tour, UniSA

AMSI-ANZIAM Lecture Tour, UniSA

Start Date

February 13, 2023

Registration

Closed

Image Credit: Australian Mathematical Sciences Institute (AMSI)

2023 ANZIAM Lecturer

The AMSI-ANZIAM Lecture Tour invites a distinguished international academic in an Applied Mathematical field to speak at universities across Australia after the conclusion of the ANZIAM Conference. It includes a series of talks including Specialist and Public lectures. The tour is organised biennially by AMSI and is supported by ANZIAM.

Date: 13 February 2023
Time: 11am–12pm ACDT
Venue: Room F1-24, University of South Australia, Mawson Lakes campus, Adelaide, SA
Title: Singularly Perturbed Markovian Models: From Queues to Web Ranking and Reinforcement Learning

Professor Konstantin Avrachenkov
National Institute for Research in Digital Science and Technology (INRIA)

Konstantin Avrachenkov received his Master degree in Control Theory from St. Petersburg State Polytechnic University (1996), Ph.D. degree in Mathematics from University of South Australia (2000) and Habilitation from University of Nice Sophia Antipolis (2010). Currently, he is a Director of Research at Inria Sophia Antipolis, France. He is an associate editor of the International Journal of Performance Evaluation, Probability in the Engineering and Informational Sciences, ACM TOMPECS, Stochastic Models and IEEE Network Magazine. Konstantin has co-authored two books “Analytic Perturbation Theory and its Applications”, SIAM, 2013 and “Statistical Analysis of Networks”, Now Publishers, 2022. He has won 5 best paper awards. His main theoretical research interests are Markov chains, Markov decision processes, random graphs and singular perturbations. He applies these methodological tools to the modeling and control of networks, and to design data mining and machine learning algorithms.

Talk Abstract: Markov chains represent a versatile tool for modelling phenomena in nature and technology. Many phenomena unfold on several time scales. In this talk I first give an accessible introduction to Markov chains and in particular to singularly perturbed Markov chains, which are stochastic dynamical models with several time scales. Then, I demonstrate the application of singularly perturbed Markov chains to queueing systems, web ranking and reinforcement learning.