Download my curriculum vitae in pdf format (last updated on 12/20/2022). 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; Core Faculty Member of the Institute for Experiential AI.
- i. ☀️ (March 2023) I am giving a lecture at Northestern’s Math Club on March 24th. Title: “Distance metrics for learning on discrete, real-valued and structured data”.
- ii. ☀️ (March 2023) Hoyin accepted an offer to go for Ph.D. at the tri-institutional Ph.D. program in New York City between Cornell, Rockefeller and Memorial Sloan Kettering Cancer Center. Congratulations and good luck Hoyin!
- iii. ☀️ (February 2023) On March 15, I will be giving a talk at the ACMG Annual Clinical Genetics Meeting in the session titled “Accelerating Variant Interpretation in Cancer Susceptibility Genes”. See you in Salt Lake City!
- iv. (December 2022) Dan’s and Shantanu’s paper on classification of grouped and biased data accepted to AAAI 2023. Congratulations! See the paper on arXiv.
- v. (November 2022) Michelle to start a co-op at the Broad Institute with the Single Cell Portal Team as a software engineer. Congratulations Michelle!
- vi. (November 2022) Clara receives travel fellowship to attend PSB 2023 and present her paper “An approach to identifying and quantifying bias in biomedical data”. Congratulations Clara! See her paper here.
- vii. (October 2022) Rachel receives ASHG Reviewer’s Choice Award for her abstract and poster “Sex-specific analysis of rare variant associations with quantitative traits in the UK Biobank”. Congratulations Rachel!
- viii. (September 2022) Three lab papers accepted to PSB 2023 and a collaborative paper with Sriraam Natarajan’s group. Congratulations to Clara, Shantanu, Hoyin, Rashika, and Dan! Great work together with David Haas, Sean Mooney, Lilia Iakoucheva, and Vikas Pejaver.
- ix. (August 2022) Kym’s, Hoyin’s, and Rashika’s paper connecting type II diabetes polygenic score with the level of exercise to assess risk of gestational diabetes in pregnant women published in JAMA Network Open. A large collaboration, particularly with Rafael Guerrero, David Haas, Matthew Hahn, and Sriraam Natarajan. Congratulations to all!
- x. (August 2022) Rashika’s paper on the state of affairs in the protein function prediction field appears in Bioinformatics Advances. Congratulations Rashika!
Awards and Honors
Honorary Fellow of the Institute for Advanced Study at the Technical University of Munich, 2017
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
President-elect, International Society for Computational Biology (ISCB), 2023-
Board of Directors Member, International Society for Computational Biology (ISCB), 2012-2021
Associate Editor, PLoS Computational Biology, 2014-2021
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
- • 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: March 24, 2023