Seminars

Ioannis Tsamardinos is the Head of the Bioinformatics Laboratory at ICS-FORTH, an Assistant Professor at the Department of Computer Science at University of Crete, and an Adjunct Assistant Professor at the Department of Biomedical Informatics at Vanderbilt University. He received his Ph.D. in 2001 from the Intelligent Systems Program of the University of Pittsburgh and worked as an Assistant Professor at the Dept. of Biomedical Informatics at Vanderbilt University between 2001 and 2006. His main research concerns three directions (a) the development of novel, state-of-theart algorithms and theory for Machine Learning, Bayesian Network learning, Variable Selection, and Causal Discovery; the methods are inspired by data analysis problems in biomedicine, (b) application of state-of-the-art data analysis methods in biomedical data to discover new domain knowledge, and (c) the development of automated systems for facilitating data analysis for non-expert users. Prof. Tsamardinos has over 50 publications in international journals, conferences, and books. He has also participated in several applied projects to biomedicine, including the analysis of clinical, epidemiological, microarray gene-expression, proteomics and text-categorization. Distinctions with colleagues and students include the best performance in one of the four tasks in the recent First Causality Challenge Competition, ISMB 2005 Best Poster Winner, a Gold Medal in the Student Paper Competition in MEDINFO 2004, the Outstanding Student Paper Award in AIPS 2000, the NASA Group Achievement Award for participation in the Remote Agent team and others. International recognition has led to membership to the Editorial Board Member of the Journal of Artificial Intelligence Research and about 1900 citations (as estimated by the Publish or Perish tool). Ioannis is involved in several educational activities, regularly teaches the Machine Learning, Artificial Intelligence, and Algorithms in Bioinformatics courses at University of Crete, and has presented several tutorials related to Machine Learning and Causal Analysis for biomedical data analysis in conferences and summer schools.