About
I am an enthusiastic microbiologist hailing from Tamil Nadu, India. With agriculture deeply rooted in my family, I developed a keen interest in agricultural science. This passion led me to pursue a bachelor's degree in Agricultural Biotechnology at the Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University.
During my undergraduate studies, I gained hands-on experience with essential techniques in plant genetic engineering, such as gene cloning, PCR, SDS-PAGE, ELISA, and Western blotting. This practical training, combined with a strong theoretical foundation, deepened my understanding of the molecular mechanisms involved in plant responses to biotic and abiotic stresses.
My exposure to honey bee ecology and the natural role of microbes in enhancing crop health during this time sparked my interest in exploring the potential of nature for agricultural benefits. Consequently, I transitioned to Agricultural Microbiology for my master's degree, where I focused on the effect of foliar application of phyllosphere bacteria on rice. This research deepened my understanding of the molecular mechanisms behind bacterial-mediated induced systemic tolerance in rice and provided me with hands-on experience using advanced tools such as qPCR, HPLC, GC-MS, and scanning electron microscopy.
Driven by a desire to expand my knowledge in microbial ecology, I pursued a PhD in Microbiology at the University of Tartu, Estonia. My doctoral research focused on the effects of varying degrees and durations of warming on the soil microbiome and nitrogen cycling potentials in subarctic Icelandic grasslands. This study revealed a potential shift in microbial communities towards those that emit nitrous oxide, thereby broadening my understanding of how soil microbial communities adapt to warming in cold grasslands. In addition to advancing my knowledge of microbiology, my doctoral studies equipped me with specialized skills in processing amplicon sequence data, applying data integration techniques, and utilizing robust statistical and machine learning tools.