Dr Debmalya NandyPhD, MS, MStat, BSc
Statistician | Biostatistician | Data Scientist | Bioinformatician |
Vedic Scholar | Holistic Health Practitioner & Educator | Musician
Strive with authenticity, be that outlier! ” ~Debmalya
तत्त्वमसि | tattvamasi | Thou art That
I collaborate with multiple multi-disciplinary scientific research teams, including MD doctors and PhD scientists from other biomedical/computational fields, to facilitate bringing forth meaningful inference from high-dimensional (OMICS) data obtained from real-world patients by means of cutting-edge statistical/machine learning/data science tools.
I am a (bio) statistician/bioinformatician by training and my research interests pertain to application of cutting-edge statistical tools to solve practical problems in fields such as biology and public health. My PhD dissertation involved developing a (sufficient) dimension reduction technique and a feature screening technique in the analysis of high and ultrahigh-dimensional data, often encountered in “-omics” sciences, such as genomics, transcriptomics, metabolomics, and proteomics. My master’s degree in Bioinformatics & Genomics offered a perfect avenue to apply my statistical expertise in collaborative research projects, for example analyzing a proton nuclear magnetic resonance (1H-NMR) metabolomics dataset to decipher influential metabolites produced by the gut microbiota, children’s diets, and the maternal pregnancy behavior and habits that relate to the childhood obesity epidemic.
Pursuing my interest in applied science, I served as a Postdoctoral Fellow in the Department of Biostatistics and Informatics in the Colorado School of Public Health at the University of Colorado Anschutz Medical Campus. My research revolved around developing novel statistical tools to analyze high-dimensional OMICS data, in particular, metabolomics in terms of: (1) multiple testing p-value adjustment strategies and (2) missing value imputation.
Among applied projects, I led a project on applying the ComBat data harmonization tool on dog-brain MRIs to facilitate the downstream analyses, such as classification of brain tumor-types (meningioma vs. glioma) and consulted in a COVID-19 project demonstrating the effectiveness of host-methylation profiles of affected vs. unaffected individuals in predicting the SARS-CoV-2 outcomes