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.
- i. ☀️ (April 2021) Jose accepts a tenure track faculty position at the University of Puerto Rico - Rio Piedras. Congratulations Prof. Lugo-Martinez!
- ii. ☀️ (March 2021) An article about me in Northeastern news!
- iii. ☀️ (February 2021) Richard’s paper, from Matt Hahn’s group, on estimating sequencing error rates published in Genetics. Distinct error rates observed on reference vs. non-reference genotypes.
- iv. ☀️ (February 2021) We are editing a special issue in Human Genetics, entitled Computational Interpretation of Human Genetic Variation. Contact us about possible submissions!
- v. (January 2021) Hoyin in the Northeastern news!
- vi. (December 2020) Pedja to participate in the workshop on Establishing the Reliability of Algorithms in Biomedical Research at PSB 2021.
- vii. (November 2020) Vikas’ MutPred2 paper published in Nature Communications. Collaboration with the Mooney, Cooper, Iakoucheva, and Sebat labs.
- viii. (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!
- ix. (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!
- x. (August 2020) Jose’s paper about learning on hypergraphs, using hypergraphlet kernels, accepted in Bioinformatics. Congratulations!
- xi. (August 2020) Pedja joins Barnett Institute for Chemical and Biological Analysis as Faculty Fellow.
- xii. (August 2020) Our R01 grant on predicting risk of adverse pregnancy outcomes funded by the NIH.
- xiii. (July 2020) Prathysha Kothare joins us as an MIT Research Science Institute scholar.
- xiv. (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.
- xv. (June 2020) Yuxiang defends Ph.D. thesis. Congratulations Dr. Jiang! Good luck in Google.
- xvi. (May 2020) CAGI U24 grant funded by the NIH. We look forward to the next challenges in genome interpretation.
- xvii. (May 2020) We received a MathWorks micro-grant to improve our software for positive-unlabeled learning.
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), 2012-2021
Associate Editor, PLoS Computational Biology, 2014-
Editorial Board Member, Bioinformatics, Oxford University Press, 2010-
Editorial Board Member, Human Genetics, Springer, 2020-
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 15, 2021