Professor of Computer Science, Associate Chair
Adjunct Professor of Statistics
Department of Computer Science
700 North Woodlawn Avenue
Bloomington, IN 47408
Office: Luddy Hall 2048
Phone: (812) 856-1851
Download my curriculum vitae in pdf format (last updated on 12/23/2017). Google Scholar profile.
- i. ☀️ (April 2018) Our paper on active feature elicitation, led by Sriraam Natarajan’s group, accepted to IJCAI 2018.
- ii. ☀️ (April 2018) Mary Anne goes to UCSD for her Ph.D. in computer science. Good luck!
- iii. ☀️ (March 2018) Our session “Precision medicine: improving health through high-resolution analysis of personal data” approved for PSB 2019. Session organizers: Steven Brenner, Martha Bulyk, Dana Crawford, Jill Mesirov, Alexander Morgan, and myself. More details here.
- iv. ☀️ (March 2018) Shawn’s and Yuxiang’s paper on calculating cardinality of ontological output spaces accepted to ISMB 2018. A bit older version available on arXiv.
- v. (January 2018) Pedja appointed as Associate Chair of the Department of Computer Science.
- vi. (January 2018) Shantanu’s identifiability paper for skew normal distributions posted on arXiv.
- vii. (December 2017) Our feature selection paper with Makoto Yamada as lead accepted to TKDE. The paper is available on arXiv.
- viii. (December 2017) Ruiyu defends Ph.D. thesis. Congratulations!
- ix. (November 2017) Pedja gives a keynote at IEEE BIBM.
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: April 16, 2018