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Eugene Demidenko, PhD
Professor of Biomedical Data Science


My background is in statistical methodology and its application to cancer research, including statistical analysis of medical images derived via cutting edge engineering technologies such as electrical impedance tomography and atomic force microscopy. Besides my own research, I have been closely involved in working with clinicians and cancer researchers on a daily basis as a member of the Biostatistics Shared Resources at the Norris Cotton Cancer Center, DHMC for more than 20 years. Recently I have been exposed to the analysis of genomic data including gene expression, methylation, and SNPs. Soon it became clear to me that my extensive statistical expertise in modeling multilevel hierarchical data and my solid background in applied mathematics and computer science could play a major role in advancing the existing methods of algorithms used in biomedical science to the next level. This confidence was boosted after publishing my first article on bioinformatics: it has been downloaded over 5 thousand times over three months after publication in January and is classified as Highly Accessed. I have an ambitious goal of redefining statistics by moving the point of inference from parameter to individual prediction and comparison. In my recent paper I introduced a new measure, D-value, as an alternative to P-value, and I want to implement this new approach to the analysis of the data to be derived in all projects of this COBRE.