2022-10-112022-11-16https://rhed.amsi.org.au/wp-content/uploads/sites/73/2020/06/amsi_rhed_v2-2.pngResearch and Higher Education200px200px
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.
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.
|Sun 5 - Thursday 9 February||ANZIAM Conference.|
|The University of Queensland||Cairns, QLD|
|Monday 13 February||Specialist||Singularly Perturbed Markovian Models: From Queues to Web Ranking and Reinforcement Learning||University of South Australia||Adelaide, SA|
|Wednesday 15 February||11am - 12pm||Specialist||Random-walk Based Sampling in Social Networks||RMIT||Access Grid Room, Level 3, Building 15, RMIT Melbourne Campus
Melbourne, VIC and online
|Friday 17 February||Specialist||Australian Bureau of Statistics||Canberra, ACT|
|Monday 20 February||Specialist||University of Newcastle||Newcastle, NSW|
|Wednesday 22 February||Public||University of Queensland||Brisbane, QLD|
|*All times are expressed in the local times of the host city|
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.
How many friends do social network members have on average? What is a proportion of a certain sub-population in a social network? Are online social network users more likely to form friendships with those with similar attributes? Such questions frequently arise in the context of social network analysis, but often crawling an online social network via its application programming interface and conducting surveys in offline social networks are resource consuming and are prone to errors. Using regenerative properties of the random walks, we describe estimation techniques based on short crawls that have proven statistical guarantees. Moreover, these techniques can be implemented in low-complexity distributed algorithms.