Ercan E. KURUOGLU was born in Ankara, Turkey in 1969. He obtained his B.S. and M.S. degrees both in electrical and electronics engineering from the Bilkent University, Ankara, Turkey in 1991 and 1993, respectively and the M.Phil. and Ph.D. degrees in information engineering from the Cambridge University, in the Signal Processing Laboratory, in 1995 and 1998, respectively. Upon graduation from Cambridge, he joined the Xerox Research Center in Cambridge as a Permanent Member of the Collaborative Multimedia Systems Group. In 2000, he was in INRIA-Sophia Antipolis, with the Ariana Project as an ERCIM Fellow. In 2002, he joined ISTI-CNR, Pisa, Italy as a Permanent Member. Since 2006, he is an associate professor and a senior researcher. In addition, he was a Visiting Professor at Georgia Institute of Technology graduate program in Shanghai in Autumn 2007. Furthermore, Kuruoglu was a Visiting Researcher/Lecturer for extended periods at the Bogazici University, Izmir Institute of Technology (Turkey), University of Cantabria (Spain), Xidian University, and Shanghai Jiao Tong University (China). He was an Associate Editor for IEEE Transactions on Signal Processing between the period of 2002 to 2006. Currently, he is on the Editorial Board of Digital Signal Processing and an Associate Editor for IEEE Transactions on Image Processing. He has guest edited special issues in various journals on heavy tailed processes, cosmology applications of signal processing, and Bayesian source separation. Kuruoglu was the Special Sessions Chair for EURASIP European Signal Processing Conference, EUSIPCO 2005 and was the Technical Cochair for EUSIPCO 2006. He is also an Elected Member of the IEEE Technical Committee on Signal Processing Theory and Methods and a Senior Member of the IEEE. Moreover, he is the author of more than 60 peer reviewed publications and holds four US and European patents. His research interests are in statistical signal processing and information and coding theory with applications in image processing, astronomy, telecommunications, intelligent user interfaces, and bioinformatics.


Kevin KNUTH was born in Fond du Lac, Wisconsin, USA in 1965. He received his Ph.D. in physics at the University of Minnesota (1995). He held postdoctoral positions studying neuroscience at Louisiana State University Medical Center (1996), the City University of New York and the Albert Einstein College of Medicine (1997-1998), where he was later an instructor. He was also an instructor at the Weill Medical College of Cornell University (1999) where he worked on neurodatabases, and was a research scientist at the Center for Advanced Brain Imaging at the Nathan Kline Institute (1999-2001). From 2001-2005 he worked as a Research Scientist developing machine learning techniques and their applications in the Intelligent Systems Division at NASA Ames Research Center. He joined the University of Albany faculty in October 2005 with a dual appointment in the Departments of Physics and Informatics, and was promoted to Associate Professor in 2009. His scientific interests include: robotics, probability theory, astrophysics, complex systems, quantum mechanics and brain dynamics. For recreation, he enjoys hiking, birdwatching, photography, and poking around tidal pools.


Bayesian Machine Learning
This special session will focus on the application of Bayesian probability theory to a variety of practical machine learning problems. Bayesian methods provide a unique opportunity to accommodate detailed information about the signal models as well as prior information about problem-specific constraints on parameter values. The recent increase in computational power as well as the development of more powerful sampling techniques have made the application of Bayesian methods to complex problems more practical than ever. This session will focus on a variety of signal processing applications as well as machine intelligence with a focus on robotics, intelligent instrumentation, experimental design, image understanding and vehicle health monitoring. There will be a live demo of a robot employing Bayesian adaptive exploration to solve a search-and-characterize problem.