Invited Speakers


John Shawe-Taylor

University College London
Centre for Computational Statistics and Machine Learning

Talk Title: Margin Based Structured Output Learning

Short Bio: John Shawe-Taylor is a professor at the University College London where he directs the Centre for Computational Statistics and Machine Learning and heads the Department of Computer Science. His research has contributed to a number of fields ranging from graph theory through cryptography to statistical learning theory and its applications. However, his main contributions have been in the development of the analysis and subsequent algorithmic definition of principled machine learning algorithms founded in statistical learning theory. He has co-authored two influential text books on kernel methods and support vector machines. He has also been instrumental in coordinating a series of influential European Networks of Excellence culminating in the PASCAL networks.


Kristian Kersting

Technical University of Dortmund
Computer Science Department

Talk Title: Collective attention on the web

Presentation: Download here the PDF presentation

Short Bio: Kristian Kersting is an Associate Professor in the Computer Science Department at the Technical University of Dortmund. His main research interests are data mining, machine learning, and statistical relational artificial intelligence, with applications to medicine, plant phenotpying, traffic, and collective attention. He gave several tutorials at top conferences and co-chaired BUDA, CMPL, CoLISD, MLG, and SRL as well as the AAAI Student Abstract track and the Starting AI Research Symposium (STAIRS). Together with Stuart Russell (Berkeley), Leslie Kaelbling (MIT), Alon Halevy (Goolge), Sriraam Natarajan (Indiana) and Lilyana Mihalkova (Google) he cofounded the international workshop series on Statistical Relational AI. He served as area chair/senior PC for several top conference and co-chaired ECML PKDD 2013, the premier European venue for Machine Learning and Data Mining. Currently, he is an action editor of JAIR, AIJ, DAMI, and MLJ as well as on the editorial board of NGC.


Gianluca Bontempi

Université Libre de Bruxelles
Director of Interuniversity Institute of Bioinformatics in Brussels

Talk Title: Perspectives of feature selection in bioinformatics: from relevance to causal inference

Presentation: Download here the PDF presentation

Short Bio: Gianluca Bontempi is Full Professor in the Computer Science Department at the Université Libre de Bruxelles (ULB), Brussels, Belgium, co-head of the ULB Machine Learning Group and Director of (IB)2, the ULB/VUB Interuniversity Institute of Bioinformatics in Brussels. His main research interests are big data mining, machine learning, bioinformatics, causal inference, predictive modelling and their application to complex tasks in engineering (forecasting, fraud detection) and life science. He was Marie Curie fellow researcher, he was awarded in two international data analysis competitions and he took part to many research projects in collaboration with universities and private companies all over Europe. He is author of more than 200 scientific publications, associate editor of PLOS One, member of the scientific advisory board of Chist-ERA and IEEE Senior Member. He is also co-author of several open-source software packages for bioinformatics, data mining and prediction.

ALT Invited Speakers


Avrim Blum

Carnegie Mellon University, Pittsburgh
Professor of Computer Science

Talk Title: Learning about Agents and Mechanisms from Opaque Transactions

Short Bio: Avrim Blum is Professor of Computer Science at Carnegie Mellon University. His main research interests are in Foundations of Machine Learning and Data Mining, Algorithmic Game Theory (including auctions, pricing, dynamics, and connections to machine learning), the analysis of heuristics for computationally hard problems, and Database Privacy. He has served as Program Chair for the IEEE Symposium on Foundations of Computer Science (FOCS) and the Conference on Learning Theory (COLT). He was recipient of the Sloan Fellowship, the NSF National Young Investigator Award, the ICML/COLT 10-year best paper award, and the Herbert Simon Teaching Award, and is a Fellow of the ACM.


Gábor Lugosi

Department of Economics, Pompeu Fabra University, Barcelona
ICREA research professor

Talk Title: How to Estimate the Mean of a Random Variable?

Short Bio: Gábor Lugosi is an ICREA research professor at the Department of Economics, Pompeu Fabra University, Barcelona. His research main interests include the theory of machine learning, combinatorial statistics, inequalities in probability, random graphs and random structures, and information theory. He has co-authored monographs on pattern classification, on online learning, and on concentration inequalities.