Professor of Computer Science

Khoury College of Computer Sciences

Northeastern University

440 Huntington Avenue

Boston, MA 02115

United States of America

Office:  310D West Village H

Phone: (617) 373-7428

Email:..predrag@northeastern.edu.

Download my curriculum vitae in pdf format (last updated on 12/21/2020). Google Scholar profile.

Interested in working with us? We have broad research interests. Please check our selected three publications in the areas of semi-supervised learning, evaluation of machine learning algorithms, general machine learning, graphs and hypergraphs, genome interpretation, protein function, mass-spectrometry proteomics, and post-translational modifications. Also see our review papers.

Courtesy appointments. Department of Chemistry and Chemical Biology; Barnett Institute Fellow, Affiliate Faculty of the Roux Institute. Honorary Fellow of the Institute for Advanced Study at the Technical University of Munich.

Recent Updates

Awards and Honors

August-Wilhelm Scheer Visiting Professor at Technical University of Munich, 2016

Senior Member, International Society for Computational Biology, 2015

National Science Foundation CAREER Award, 2007

Outstanding Young Researcher, University of Novi Sad, 1998

Major Student Awards

Kymberleigh Pagel, Ian Lawson Van Toch Memorial Award, ISMB/ECCB 2017

Wyatt Clark, Ian Lawson Van Toch Memorial Award, ISMB/ECCB 2013

Professional Activities

Board of Directors Member, International Society for Computational Biology (ISCB), 2012-2021

Associate Editor, PLoS Computational Biology, 2014-

Editorial Board Member, Bioinformatics, Oxford University Press, 2010-

Editorial Board Member, Human Genetics, Springer, 2020-

Research Interests

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

Biomedical Informatics

  • Understanding and predicting molecular mechanisms of disease
  • Genome interpretation
  • Precision medicine and precision health

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 updated: February 8, 2021