GUNEY, EMRE

Coordinator of Advanced Programming, Algorithms and Data Structures


Emre Guney is a Senior Research Fellow at the Research Programme on Biomedical Informatics (GRIB), at the Pompeu Fabra University (UPF) and Hospital del Mar Research Institute (IMIM) in Barcelona, Spain. He also holds secondary appointment at the Pharmacology & Personalised Medicine department at Maastricht University in the Netherlands and works for a start-up located in Barcelona. He obtained his B.S. degree in Computer Engineering from Middle East Technical University (METU) and Ph.D. in Biomedicine (supervised by Professor Baldo Oliva) from Pompeu Fabra University (UPF). He has spent several years as a postdoctoral research associate in the Center for Complex Network Research at Northeastern University and the Center for Cancer Systems Biology at Dana Farber Cancer Institute in Boston, USA. He was awarded various fellowships such as TUBITAK Fellowship for Graduate Studies, AGAUR FI Fellowship, EU-cofunded Beatriu de Pinos Fellowship and has participated in EU and NIH funded projects as an investigator. His work is internationally well recognized, reflected by a number of high-impact publications and scientific committee involvements. He has also taught several courses at Koc University and UPF and provided scientific guidance to graduate students at Pompeu Fabra University, Northeastern University and Maastricht University.

Research lines

Dr. Guney has extensive experience on computational analysis of biomedical data, predictive modeling, graph theory and have a solid track record on conducting multidisciplinary research. His research interests include disease bioinformatics and translational systems medicine with a particular focus on network-based approaches to understand biological processes perturbed in complex diseases and to develop effective therapeutics targeting these perturbations. His research is focused on the mechanistic understanding of human genetic diseases from a systems bioengineering perspective, where the main emphasis is on building robust and scalable predictive models that combine data from high-throughput protein interaction screening, gene expression profiling and genome wide association studies.