Over 110 students have graduated from the Department of Biomedical Informatics (25+ PhD, 50+ MS, 25+ Certificate). The diversity of careers available to DBMI alumnus is evident in their biographies. Many of our graduates are teaching and performing research in academic institutions, such as Vanderbilt University, Arizona State University, and New York University while others have entered private industry with companies such as Cerner Corporation and Boston Scientific; some have positions in government agencies, such as the NIH and AHRQ, while others are at major medical centers, serving in roles such as Chief Medical Information Officer. We maintain a database of the career paths of our graduates. If you are an alumnus, please contact us if you would like to submit or update information!
Being an engineer at heart, and scientist by training, I constantly strive to bring cutting edge research to fruition in the form of robust, scalable and useful systems. At DBMI, I was presented with ample opportunities to develop and hone the skills required to carry out this endeavor with precision and rigor. Through many fruitful, cross-disciplinary collaborations I learned to navigate and meaningfully contribute to the complex space of healthcare R&D / technology. Everyone around me was an endless source of inspiration that prompted me to do good each day, and better the next. I feel fortunate to be a part of this incredible institution and will cherish the relationships I forged here.
Ruggles K.V., Tang Z., Wang X., Grover H., Askenazi M., Teubl J., Cao S., McLellan M.D., Clauser K.R., Tabb D.L., Mertins P., Slebos R., Erdmann-Gilmore P., Li S., Gunawardena H.P., Xie L., Liu T., Zhou J.Y., Sun S., Hoadley K.A., Perou C.M., Chen X., Davies S.R., Maher C.A., Kinsinger C.R., Rodland K.D., Zhang H., Zhang Z., Ding L., Townsend R.R., Rodriguez H., Chan D., Smith R.D., Liebler D.C., Carr S.A., Payne S., Ellis M.J., Fenyo D. An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer. Mol Cell Proteomics. March 2015, 15(3): 1060-71.
Shi, L., Grover, H. Balasubramanian, J. B., Kolli, K., Shriver, C., Gopalakrishnan, V. A Flexible Feature Selection Framework for Improving Breast Cancer Classification from Sparse Spectral Count Proteomic Data. In Proceedings of the International Conference on Data Mining. July 2014.
Grover, H., Wallstrom, G., Wu, C.C., Gopalakrishnan, V. Context-sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry. OMICS: A Journal of Integrative Biology. February 2013, 17(2): 94-105.
Grover, H., Gopalakrishnan, V. Efficient Processing of Models for Large-scale Shotgun Proteomics Data. International Workshop on Collaborative Big Data, October 2012, Pittsburgh.
Zeng, X., Hood, B.L., Zhao, T., Conrads, T.P., Sun, M., Gopalakrishnan, V., Grover, H., Day, R.S., Weissfeld, J.L., Siegfried, J.M., Bigbee W.L. Lung Cancer Serum Biomarker Discovery Using Label Free Liquid Chromatography-Tandem Mass Spectrometry. J of Thoracic Oncology (2011) 6(4):725-34.
Lustgarten, J.L., Gopalakrishnan, V., Grover, H., Visweswaran, S. Improving Classification Performance with Discretization on Biomedical Datasets. In Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2008) 445-9.
Lustgarten, J.L., Visweswaran, S., Grover, H., Kimmel, C.P., Ryberg, H., Bowser, R.P., Gopalakrishnan, V. Using a novel resource to decrease proteomic biomarker identification time. In Proceedings of the AMIA Annu Symp (Nov 2008).
Grover, H., Lustgarten, J.L., Visweswaran, S., Gopalakrishnan, V. Improving Peptide Identification via Validation with Intensity-based Modeling of Tandem Mass Spectra. In Proceedings of the International Conference on Bioinformatics, Computational Biology, Genomics and Chemoinformatics (BCBGC-08). (2008) 56-63.
Lustgarten, J.L., Visweswaran, S., Grover, H., Gopalakrishnan, V. An Evaluation of Discretization Methods for Learning Rules from Biomedical Datasets. In Proceedings of the International Conference on Bioinformatics and Computational Biology (BIOCOMP’08). (2008) 527-532
Grover, H., Aphinyanaphongs, Y. A Distributed Microservices Platform for Clinical Data Processing. AMIA iHealth Clinical Informatics Conference, Philadelphia (May 2017).
Grover, H., Fenyo, D. A Computational Framework for Large-scale Proteomic Data Management and Mining. Biological Data Science Meeting, Cold Spring Harbor Laboratory (Nov 2014)
Grover, H., Fenyo, D. Computational Infrastructure for Mining “Big” Proteomic Data. ASMS Conference on Mass Spectrometry and Allied Topics, 2014 (Baltimore, MD. June 2014)
Grover, H., Keegan, S., Li, S., Wang, X., Murthy, S., Karger, B., Ivanov, A.R., Fenyo, D. Spectral Library Searching for Samples where Amount of Sample is Severely Limited. ASMS Conference on Mass Spectrometry and Allied Topics, 2014 (Baltimore, MD. June 2014)
Grover, H., Keegan, S., Giuffrida, J., Li, S., Brusic, V., Murthy, S., Karger, B., Ivanov, A.R., Fenyo, D. Improved Recovery of Information from Mass Spectrometry Data When the Amount of Sample is Severely Limited. ASMS Conference on Mass Spectrometry and Allied Topics, 2013 (Minneapolis, MN. June 2013)