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Learning in Non-stationary Environments

Sunday, August 30th, 2015

October 6, 2015 at 11:00 a.m. Cesare Alippi, IEEE Fellow & Professor of Information Processing Systems with the Politecnico di Milano, will be presenting a distinguished lecture, “Learning in Non-stationary Environments” at Ryerson University.

Speaker: Cesare Alippi
IEEE Fellow
Professor of Information Processing Systems with the Politecnico di Milano

Day & Time: Tuesday, October 6, 2015
11:00 a.m. – 12:00 p.m.

Location: George Vari Centre for Computing and Engineering
Ryerson University
Room: ENG287
245 Church Street, Toronto, Ontario M5B 2K3
Click here to see the Map – Look for ENG

Organizer: IEEE Signals & Computational Intelligence Toronto Chapter

Contact: E-mail: Lorenzo Livi

Abstract: Most of machine learning applications assume the stationarity hypothesis for the process generating the data. This amenable assumption is so widely –and implicitly- accepted that sometimes we even forget that it does not generally hold in the practice due to concept drift (i.e., a structural change in the process generating the acquired datastreams). The ability to detect concept drift and react accordingly is hence a major achievement for intelligent learning machines and constitutes one of the hottest research topics for embedded systems. This ability allows the machine for actively tuning the application to maintain high performance, changing online the operational strategy, detecting and isolating possible occurring faults to name a few relevant tasks. The talk will focus on “Learning in a non-stationary environments”, by introducing both passive and active approaches. The active approach will be deepened by presenting triggering mechanisms based on Change point methods and Change detection tests. Finally, the just-in-time detect&react mechanism is introduced where, following a detected change, the system immediately reacts with a strategy depending on the available information.

Biography: Cesare Alippi received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN).
Alippi is an IEEE Fellow, Distinguished lecturer of the IEEE CIS, Member of the Board of Governors of INNS, Vice-President education of IEEE CIS, Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Trans. Instrumentation and Measurements, IEEE-Trans. Neural Networks, and member and chair of other IEEE committees.
In 2004 he received the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2013 he received the IBM Faculty Award. He was awarded the 2016 IEEE TNNLS outstanding paper award.
Among the others, Alippi was General chair of the International Joint Conference on Neural Networks (IJCNN) in 2012, Program chair in 2014, Co-Chair in 2011. He was General chair of the IEEE Symposium Series on Computational Intelligence 2014.
Current research activity addresses adaptation and learning in non-stationary environments and Intelligence for embedded systems.

Alippi holds 5 patents, has published in 2014 a monograph with Springer on “Intelligence for embedded systems” and (co)-authored more than 200 papers in international journals and conference proceedings.
Home Page: http://home.dei.polimi.it/alippi/