LACNEM2017
 
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Keynotes
We expect to announce more keynote speakers later in the year

Keynote 1:
Prof. Pablo Zegers was born in Santiago, Chile. He received his Bachelor in Engineering and his Professional Engineer degree from the Pontificia Universidad Católica, Chile, in 1992, his M.Sc. from The University of Arizona, USA, in 1998, and his Ph.D., also from The University of Arizona, in 2002. He is currently an Associate Professor in the School of Engineering and Applied Sciences of the Universidad de los Andes, Chile. His interests are artificial intelligence, machine learning, neural networks, and information theory. From 2006 to 2010 he was the Academic Director of this School, and for a brief period at the end of 2010, its Interim Dean. He is a Senior Member of the IEEE, and was part of the board of the Chilean IEEE section.

Deep Learning: The talk will present the history of neural networks and its current and famous version: deep learning. We will discuss the evolution of architectures and how extremely powerful systems have finally been found. It will also be explained how these techniques are affecting our daily lives and shaping the economic markets.

Keynote 2:
Prof. Ricardo Contreras (tbc) is an associate professor in the Department of Computer Science at the University of Concepción, Chile. His research focuses on artificial intelligence and evolutionary computation. His current research interests include bio-inspired system for solving optimization problems. He received an MSc in computer science from the Pontifical Catholic University, Rio de Janeiro, Brazil.

Metaheuristics for Pattern Recognition
Meta-heuristics, are problem-independent techniques. As such, they do not take advantage of any specificity of the problem and, therefore, can be used as black boxes. This talk will present some artificial life based approaches our research team has been dealing with, that can help to solve an interesting set of optimization problems. Specific developments will be presented, to illustrate the benefits of this approach. In particular, this talk will exhibit some results obtained by using genetic algorithms, ant colony algorithms and a new technique that all explore the bacteria metaphor.

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