Microbiomes rule the world (and possibly space)! To achieve foundational changes in the understanding of health and wellness, there is a recognized need to advance our understanding of the key players in our ecosystems, viz. microbial communities. Furthermore, minimally invasive, non-genomic engineering of microbiomes would be crucial for restoring ecological balance in natural (soils, river, gut) and engineered (anaerobic digestion, fermentation) ecosystems. Low-cost next-generation sequencing (NGS) may not be high throughput enough to resolve dynamic changes in the structure of the microbiome over time and there is a need to develop non-invasive tools complementary to genomic approaches for designing effective operational and engineering strategies. The structure and function of microbial communities manifest as unique combinations, or ‘Cytometric Fingerprints’ of characteristics such as viability, metabolic activity, and morphology. 
      Our main interests lie in exploring the cytometric fingerprinting and machine learning-driven microbiome engineering approaches complementary to analysis intensive genomic approaches for shaping microbiota to alter the ecosystem of interest. In particular, we’re interested in developing a strong research program with the Judicious Engineering of microbiomes for Enhanced sanitation & Water resources, Agriculture and National health Upliftment: “JEEWANU”(Sanskrit for “particles of life”).
      To investigate this, we are developing a novel flow cytometry-based label-free, high throughput technique, which is sensitive to spatial and temporal changes in the structure and function of microbiomes. Our previous work suggested that cytometric fingerprinting could be used for a rapid, high-throughput understanding of microbiome dynamics thereby opening doors to in-situ microbiome engineering for actionable benefits. Following are some of the specific applications of microbiome dynamics that we are currently interested in:
⦁ Sanitation & Water Resources (Anaerobic Digestion, Digeponics, River Conservation)
⦁ Food & Agriculture (Milk Foam, Soil Testing, AgroProcessing, Aquaponics)
⦁ Public Health (Mastitis, Cytometric Fingerprinting Machine Learning-CFML)