Lukasz Kurgan
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Lukasz Kurgan


Lukasz Kurgan received M.Sc. degree (with honors) from the AGH University of Science and Technology in Poland in 1999 and Ph.D. degree from the University of Colorado at Boulder, U.S.A. in 2003. He was recognized with the Gold Medal of Stanislaw Staszic and the Outstanding Graduate Student Award for his M.Sc. studies.

His research interests include development and application of bioinformatics methods with focus on the analysis of sequences, structures and functions of proteins and RNAs. Dr. Kurgan is also interested in the analysis of complex clinical data (mixture of genomic, proteomic, and electronic medical records) to develop diagnostic and prognostic systems.

Dr. Kurgan joined the Department of Computer Science at the Virginia Commonwealth University in Richmond in September 2015 as the Qimonda Endowed Professor. Before that he was a Professor with the Department of Electrical and Computer Engineering at the University of Alberta in Edmonton. He heads the (un)Structural Bioinformatics laboratory. Dr. Kurgan is the Structural Bioinformatics area Editor of the BMC Bioinformatics and Associate Editor of Current Protein and Peptide Science, Intrinsically Disordered Proteins, PeerJ, PLOS ONE, Neurocomputing, Protein and Peptide Letters, and Journal of Biomedical Science and Engineering, and serves(ed) on program committees of numerous conferences and workshops in bioinformatics.

Bioinformatics Computational Biology Molecular Biology

Work details

Qimonda Endowed Professor

(un)Structural Bioinformatics Laboratory, Virginia Commonwealth University
Department of Computer Science
Our (un)structural bioinformatics lab focuses on: 1) Structure and function prediction and modeling of proteins and small RNA, 2) Discovery and characterization of sequence-structure/disorder-function relationships in proteins, 3) Binding of small ligands (including drugs), peptides, RNAs and DNA to proteins, 4) Prediction and functional characterization of intrinsic disorder in proteins, 5) Development of advanced diagnostic and prognostic systems from complex clinical data

PeerJ Contributions