BoG Member, IEEE ITS Society
Universidad de Las Palmas de Gran Canaria, Spain
Research field: application of Evolutionary Computation, Data mining and Parallel Computing to Intelligent Transportation Systems.
Dr. Sanchez-Medina earned his Engineering Master Degree at the Telecommunications Faculty on 2002, and his PhD at the Computer Science Department on 2008. His PhD dissertation versed on the use of Genetic Algorithms, Parallel Computing and Cellular Automata based Traffic Microsimulation to optimize the Traffic Lights Programming within an Urban Traffic Network.
His research interests include mainly the application of Evolutionary Computation, Data mining and Parallel Computing to Intelligent Transportation Systems. He has a wide experience on the development of traffic models and simulation platforms.
Javier Sanchez-Medina has been volunteering for several years at many international conferences related to Intelligent Transportation, Computer Science, Evolutionary Computation, etc. He is reviewer for some Transportation related journals..
He is also very active as a volunteer of the IEEE ITS Society. Since 2010, we has served for the IEEE ITS Society organizing the TBMO 2010 Workshop at ITSC2010, co-organizing the “Travel Behavior Research: Bounded Rationality and Behavioral Response” Special Session at ITSC2011, being Publications Chair at the IEEE FISTS2011, Registration Chair at the IEEE ITSC2012, Workshops and Tutorials Chair for IEEE ITSC 2013, Panels Chair at IEEE VTC2013-Fall, Program co-Chair at IEEE ITSC2014, program co-Chair at IEEE ITSC2016, publicity chair at IEEE IV2016, program chair at IEEE ICVES 2017, program chair at IEEE ITSC2018 and General Chair at IEEE ITSC2015. Currently he also is EiC of the ITS Podcast, the ITS Newsletter and Vice-president of the IEEE ITSS’s Spanish chapter.
He has widely published his research with more than 25 international conference articles and more than 15 international journal articles.
Datamining applied to Intelligent Transportation Systems
Datamining or Knowledge Discovery in Databases (KDD) has become a very popular and active field in the last years in very different environments! From Stock-market forecasting, to Astronomy, the very same principles are being exploited intensively. Essentially, when there is a high volume of data coming out of any human process, that brings along the potential of extracting useful and unexpected information out of it, provided with the right tools and techniques.
Intelligent Transportation Systems are no exception to that! In this broad field, huge amounts of information are being produced every second and there is extremely interesting knowledge waiting there for us data scientists discovering and exploiting it!
In this course we will talk about the general concepts involved in this field, particularizing on how ITS is a perfect target for KDD.
Waikato Environment for Knowledge Analysis (WEKA) Tutorial
Additionally we will give an introductory tutorial, including some case study, to a very useful tool to get started with KDD, which is Waikato University’s WEKA, a very complete open source tool-kit to get the taste of Data science first hand with it.