Predrag Radivojac

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Pedja Radivojac, Stanford University, 2013

Associate Professor of Computer Science and Informatics

Adjunct Associate Professor of Statistics


Department of Computer Science and Informatics

Indiana University

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 12/21/2014). Google Scholar profile.


Post-doctoral fellow, Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, 2004

Ph.D., Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania, 2003

M.S., Electrical Engineering, University of Belgrade, Serbia, 1997

B.S., Electrical Engineering, University of Novi Sad, Serbia, 1994

Professional Activities:

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-

Recent Updates:

  1. (April 2015) Pedja promoted to full professor by IU Board of Trustees, officially starting July 1.

  2. (April 2015) Our 10 Simple Rules paper published in PLoS Computational Biology.

  3. (March 2015) Kym received a travel award from NSF to attend the Sackler Colloquium.

  4. (March 2015) Rodrigo's and Sujun's paper on proteomic identification of alternatively spliced isoforms accepted for GLBIO 2015.

  5. (February 2015) Zhiyu to join Novilytic as a post-doctoral fellow.

  6. (January 2015) Pedja's second ISCB Board of Directors term started (ends in January 2018).

  7. (September 2014) Jose's paper on edit distance graphlet kernels officially published in Network Science. Available here.

Research Interests:

Bioinformatics and Computational Biology

 Understanding protein function and method development for function prediction.

 Post-translational modifications.

 Algorithm development for mass spectrometry (MS) and MS/MS proteomics.


Biomedical Informatics and Applications to Human Health

 Development of computational models for understanding and predicting molecular mechanisms of disease.

 Candidate gene prioritization and biomarker discovery.

 Genome interpretation.


Machine Learning

 Structured-output learning and evaluation, kernel-based inference on graphs, and supervised learning from biased data.

 Parameter estimation in a semi-supervised learning framework.


Last modified: April 28, 2015