Predrag Radivojac


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Pedja Radivojac, Indiana University, 2013

Professor of Computer Science

Adjunct Professor of Statistics

Address:

Department of Computer Science

Indiana University

150 South Woodlawn Avenue

Bloomington, IN 47405

Office:  Lindley Hall 301F

Phone:  (812) 856-1851

Email:..predrag@indiana.edu..

 

Download my curriculum vitae in pdf format (last updated on 12/24/2016). Google Scholar profile.

Education:

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. (March 2017) Kym's paper accepted to ISMB 2017.

  2. (March 2017) Kym receives a travel award to attend Reproducibility of Research workshop in Washington, DC.

  3. (February 2017) CAFA3 submission deadline passed. Predictions submitted for over 300 algorithms. Check out the following papers about CAFA: CAFA1, CAFA2, and a gentle introduction to CAFA.

  4. (January 2017) Shantanu to give a talk at AAAI 2017.

  5. (December 2016) Jose defends Ph.D. dissertation on December 12. Congratulations! Staying at Indiana on the Precision Health Initiative Project!

  6. (December 2016) Pedja awarded August-Wilhelm Scheer Visiting Professor position at Technical University of Munich in Germany, 2017. Also visited in 2016.

Center Affiliations:

Center for Algorithms and Machine Learning (CAML)

Center for Bioinformatics Research (CBR)

Research Interests:

Bioinformatics and Computational Biology

 Protein structure and function; method development and evaluation for function prediction

 Post-translational modifications (PTMs)

 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 and precision medicine

 

Machine Learning

 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 modified: March 13, 2017