Download my curriculum vitae in pdf format (last updated on 06/20/2020). 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, general machine learning, graphs and hypergraphs, genome interpretation, protein function, mass-spectrometry proteomics, and post-translational modifications. Also see our review papers.
- i. ☀️ (November 2020) Vikas’ MutPred2 paper published in Nature Communications. Collaboration with the Mooney, Cooper, Iakoucheva, and Sebat labs.
- ii. ☀️ (November 2020) Richard’s paper, from Matt Hahn’s group, on estimating sequencing error rates accepted in Genetics and is on bioRxiv. Distinct error rates observed on reference vs. non-reference genotypes.
- iii. ☀️ (September 2020) Our lab alumna, Haleigh Eppler, now in the Jewell lab at the University of Maryland, receives an F31 award from the National Institute of Allergy and Infectious Diseases. Congratulations!
- iv. ☀️ (August 2020) Hoyin becomes a Civic Digital Fellow. Will spend 10 weeks at the Office of Data Science and Emerging Technologies, the National Institute of Allergy and Infectious Diseases. Congratulations!
- v. ☀️ (August 2020) Jose’s paper about learning on hypergraphs, using hypergraphlet kernels, accepted in Bioinformatics. Congratulations!
- vi. ☀️ (August 2020) Pedja joins Barnett Institute for Chemical and Biological Analysis as Faculty Fellow.
- vii. ☀️ (August 2020) Our R01 grant on predicting risk of adverse pregnancy outcomes funded by the NIH.
- viii. (July 2020) Prathysha Kothare joins us as an MIT Research Science Institute scholar.
- ix. (June 2020) Shawn’s and Shantanu’s paper on decoy-free false discovery rate estimation in MS/MS proteomics accepted at ECCB 2020. Collaboration with the Ivanov and Vitek groups.
- x. (June 2020) Yuxiang defends Ph.D. thesis. Congratulations Dr. Jiang! Good luck in Google.
- xi. (May 2020) CAGI U24 grant funded by the NIH. We look forward to the next challenges in genome interpretation.
- xii. (May 2020) We received a MathWorks micro-grant to improve our software for positive-unlabeled learning.
- xiii. (April 2020) Richard’s paper, from Matt Hahn’s group, on mutation accumulation in macaques and offspring sociability accepted in Genome Research. Available on bioRxiv.
- xiv. (April 2020) Moses’ paper on revisiting the ortholog conjecture (OC) accepted for ISMB 2020. Also available on bioRxiv. The results confirm the original doubts in the OC and quantify the value of paralogs when predicting protein function.
- xv. (February 2020) Jay’s paper giving a citation analysis of computational biology conferences published in Bioinformatics. Available here.
- xvi. (December 2019) Shantanu’s paper on identifiability of skew normal mixtures published in Scandinavian Journal of Statistics. The paper available here.
- xvii. (November 2019) CAFA3 paper published in Genome Biology. More than a hundred methods evaluated for protein function prediction and more than a thousand genes, from three species, assigned experimental Gene Ontology annotations. Coverage in Genome Web.
- xviii. (November 2019) Two papers accepted for AAAI 2020. Congratulations Shantanu, Dan, Justin, and Himanshu!
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
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: November 20, 2020