Professor of Computer Science
Adjunct Professor of Statistics
Department of Computer Science
150 South Woodlawn Avenue
Bloomington, IN 47405
Office: Lindley Hall 301F
Phone: (812) 856-1851
Download my curriculum vitae in pdf format (last updated on 12/24/2016). Google Scholar profile.
- i. ☀️ (March 2017) Vikas’ paper accepted to Human Mutation.
- ii. ☀️ (March 2017) Kym and Jose to give talks at GLBIO 2017.
- iii. ☀️ (March 2017) Kym’s paper accepted to ISMB 2017.
- iv. ☀️ (March 2017) Kym receives a travel award to attend Reproducibility of Research workshop in Washington, DC.
- v. ☀️ (February 2017) CAFA3 submission deadline passed. Predictions submitted for hundreds of algorithms. Check out the following papers about CAFA: CAFA1, CAFA2, and a gentle introduction to CAFA.
- vi. ☀️ (January 2017) Shantanu to give a talk at AAAI 2017.
- vii. (December 2016) Jose defends Ph.D. dissertation on December 12. Congratulations! Staying at Indiana on the Precision Health Initiative project!
- viii. (December 2016) Pedja awarded August-Wilhelm Scheer Visiting Professor position at Technical University of Munich in Germany, 2017. Also visited in 2016.
Awards and Honors
August-Wilhelm Scheer Visiting Professor at Technical University of Munich, 2016-2017
Senior Member, International Society for Computational Biology, 2015
National Science Foundation CAREER Award, 2007
Outstanding Young Researcher, University of Novi Sad, 1998
Board of Directors Member, International Society for Computational Biology (ISCB), 2012-
Associate Editor, PLoS Computational Biology, 2014-
Editorial Board Member, Bioinformatics, Oxford University Press, 2010-
Bioinformatics and Computational Biology
- • Protein structure and function; method development and evaluation of function prediction
- • Post-translational modifications (PTMs)
- • Mass spectrometry (MS) and MS/MS proteomics
- • Understanding and predicting molecular mechanisms of disease
- • Genome interpretation
- • Precision medicine and precision health
- • Supervised and semi-supervised learning: learning from positive and unlabeled data; learning from biased data
- • Structured-output learning and evaluation; extreme classification
- • Kernel-based inference on sequences, time-series, and graphs
Last updated: March 26, 2017