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Past Recipients of the Gilbert S. Omenn Computational Proteomics Award

  • 2020: Jimmy Eng, University of Washington
  • 2019: Juergen Cox (Max Planck Institute of Biochemistry, Munich)
  • 2018: Hannes Roest (University of Toronto)
  • 2017: Alexey Nesvizhskii (University of Michigan)
  • 2016: Brendan MacLean (University of Washington)

Gilbert S. Omenn Computational Proteomics Award

This award recognizes the essential nature of computational methodology and software in proteomics. Specifically, this award acknowledges the specific achievements of scientists that have developed bioinformatics, computational, statistical methods and/or software used by the proteomics community, broadly defined. The award is named in honor of Gil Omenn, a US HUPO Past President, leader of the Human Proteome Project, and influential proteomics researcher. Nominations well be held for three years.


Nuno Bandeira, University of California, San Diego

Dr. Nuno Bandeira obtained his Ph.D. in Computational Mass Spectrometry in 2007 at the Department of Computer Science, University of California, San Diego. He is currently an Associate Professor with joint appointments with the Dept. of Computer Science and Engineering and with the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California, San Diego, where he is also a founding faculty of the Halicioglu Data Science Institute (HDSI). Dr. Bandeira is also the Executive Director of the Center for Computational Mass Spectrometry (CCMS), where he develops algorithms for large scale analysis of proteomics mass spectrometry data. Dr. Bandeira leads the MassIVE repository for proteomics mass spectrometry data and the GNPS repository and knowledge base for metabolomics and natural products mass spectrometry data, altogether serving the community with hundreds of terabytes of mass spectrometry data in over 10,000 public datasets. Building on CCMS's expertise with distributed algorithms for analysis of mass spectrometry data, MassIVE and GNPS have enabled the concept of mass spectrometry `living data', whereby public datasets are continuously reprocessed to transfer new knowledge to existing data, as well as to generate new hypotheses that are curated by active researchers to generate new knowledge (which then loops back to all other datasets). This virtuous cycle of iterative reanalysis, curation and knowledge base extension has resulted in the data-networking of thousands of researchers in 150+ countries, where it was integrated with numerous ongoing research projects and consequently improved public data annotation by over 10-fold since the time of original deposition.

Olga Vitek, Northeastern University

Dr. Olga Vitek holds a PhD in Statistics from Purdue University. After a postdoc in the Aebersold lab in the Institute for Systems Biology in Seattle, she returned to the Department of Statistics at Purdue as Assistant and then Associate Professor and University Faculty Scholar. Currently Olga is Professor in the Khoury College of Computer Sciences at Northeastern University. Her group develops, implements and evaluates methods at the intersection of statistics, machine learning, mass spectrometry and systems biology. Open-source software MSstats and Cardinal developed in her lab are used in academia and industry, and were recently recognized with the Chan Zuckerberg Essential Open Source Software for Science award. Olga's lab hosts May Institute on Computation and Statistics for Mass spectrometry and Proteomics, an educational event that each year attracts over 100 participants. Olga is a Senior Member of the International Society for Computational Biology, and an Elected Member of the Board of Directors of USHUPO and of the Council of HUPO. She serves as Associate Editor of Bioinformatics, and a member of the Editorial advisory board of Molecular and Cellular Proteomics and of Journal of Proteome Research.


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