Every year the Student Council invites top scientists from the field of Computational Biology to deliver a keynote address at the symposium. We usually ask our speakers to use a part of their time to talk about their experiences with careers development in the field or to include other useful advice to young researchers in their talk. In the past, we have had the privilege of hearing from various prominent researchers and scientists including Janet Thornton, Phil Bourne, Reinhard Schneider, Julio Collado-Vides and Anna Tramontano.

Keynote Speakers

Timothy Hughes

University of Toronto, Toronto, ON, Canada

Timothy R. Hughes is a Professor in the Banting and Best Department of Medical Research at the University of Toronto. He holds a Canada Research Chair in Functional Genomics and was the recipient of the 2005 NCIC Terry Fox Young Investigator Award. Dr. Hughes studied engineering and music at the University of Iowa, and received his Ph.D. in Cell and Molecular Biology from Baylor College of Medicine. He did his postdoctoral work at Rosetta Inpharmatics (now Merck), where he played a key role developing ink-jet microarrays now marketed by Agilent Technology, and showed that microarray expression patterns can be used to correctly infer functions of novel genes. After moving to Toronto in 2001, Dr. Hughes has pioneered techniques and applications in functional genomics, microarray analysis, RNA processing, and gene regulation.

Mark Gerstein

Yale University, New Haven, CT, USA

Mark Gerstein is the Albert L Williams professor of Biomedical Informatics at Yale University. He is co-director the Yale Computational Biology and Bioinformatics Program, and has appointments in the Department of Molecular Biophysics and Biochemistry and the Department of Computer Science.

He received his AB in physics summa cum laude from Harvard College in 1989 and his PhD in chemistry from Cambridge in 1993. He did post-doctoral work at Stanford and took up his post at Yale in early 1997. Since then he has received a number of young investigator awards (e.g. from the Navy and the Keck foundation) and has published appreciably in scientific journals. He has more than 250 publications in total, with a number of them in prominent journals, such as Science, Nature, and Scientific American. His current
publication list is at http://papers.gersteinlab.org.

His research is focused on bioinformatics, and he is particularly interested in large-scale integrative surveys, biological database design, macromolecular geometry, molecular simulation, human genome annotation, gene expression analysis, and data mining.

Burkhard Rost

Columbia University, New York, NY, USA
ISCB President and ISMB 2008 Conference Chair

Burkhard Rost obtained his doctoral degree (Dr. rer. nat.) from the University of Heidelberg (Germany) in the field of theoretical physics. He began his research working on the thermo-dynamical properties of spin glasses and brain-like artificial neural networks. He moved briefly (1988-1990) in peace/arms control research designing simple non-intrusive sensor networks to monitor aircraft. He entered the field of molecular biology at the European Molecular Biology Laboratory (EMBL, Heidelberg, Germany, 1990-1995), spent a year at the European Bioinformatics Institute (EBI, Hinxton, Cambridgshire, England, 1995), returned to the EMBL (1996-1998), joined the company LION Biosciences for a brief interim (1998), and arrived at Columbia University in 1998. In 1992, Dr. Rost developed the first Internet server for structure prediction (PredictProtein), and contributed a number of methods that predict aspects of protein structure.

His major research contribution has been the combination of machine learning tools and evolutionary information. His academic research goal is to contribute toward the understanding of molecular evolution; his technical objective is to contribute toward a coarse-grained modeling of a cell. Current research focuses on the prediction of protein function from sequence and structure and structure prediction. It includes the prediction of subcellular localization, of protein-protein and protein-substrate interactions, the prediction of impacts of residues mutations, the analysis of protein networks, the development of a dynamic view of the protein sequence/structure universe, the development of improved alignment algorithms, and the development of software systems that meet today's challenges.