☀️ We have moved to Northeastern University ☀️
Download my curriculum vitae in pdf format (last updated on 09/06/2018). Google Scholar profile.
- i. ☀️ (September 2018) Gregg’s paper, from Matt Hahn’s group, on estimation of mutation rates in primates accepted in Current Biology. An early version available at bioRxiv.
- ii. ☀️ (September 2018) CAFA collaboration reveals 11 new long-term memory genes. bioRxiv
- iii. ☀️ (August 2018) Shantanu defends Ph.D. thesis. Congratulations!
- iv. ☀️ (August 2018) Pedja to attend NSF workshop in Belgrade, Serbia.
- v. ☀️ (August 2018) Our manuscript on identifying alternatively spliced proteins published in TCBB.
- vi. ☀️ (July 2018) Rafael gives a talk at CAGI 5. Overall, a good showing at CAGI and ISMB for the group!
- vii. ☀️ (July 2018) Shawn presents a proceedings paper at ISMB 2018. The paper is available here.
- viii. ☀️ (July 2018) Our paper on active feature elicitation, led by Sriraam Natarajan’s group, presented at IJCAI 2018.
- ix. (June 2018) Kym receives a travel award to present her work in CAGI 5.
- x. (June 2018) Pedja gives a keynote at the opening day of BELBI 2018.
- xi. (May 2018) Wazim’s and Kym’s paper on sequencing Serbian genome posted on arXiv. This work started as a class project in Matt Hahn’s population genomics class.
- xii. (May 2018) Our feature selection paper with Makoto Yamada as lead published in TKDE.
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
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: September 16, 2018