Adarsh Jay

Biography

I was 13 years old when I first decided to be a scientist. Even at that age I knew it was Biology that called to me the most. Watching different kinds of cells and microbes under a microscope always left me awestruck. Having put a lot of effort into studying many different subjects and having cleared multiple medical and engineering entrance exams in India, it was a horrifying idea to me that were I to specialise in one, I might lose touch with other domains in a few years time. That is why in 2018, I chose to go for an Integrated BS-MS degree at Indian Institute of Science Education and Research, Thiruvananthapuram (IISER-TVM), as it ensured that I would be in constant exposure to multiple fields of science at all times. It was at this time that I got my first computer and it was like looking into a microscope all over again. Softwares, hardwares, the internet – all these things I previously had only studied about, were now within my reach to tinker with. I got the opportunity to carry out a bioinformatic intensive project between 2022 and 2023 for my Masters which involved identifying structural and genetic variations in yeast when exposed to various kinds of stress through a Whole Genome Sequencing approach. At the end of this period I was sure that I wanted to explore the myriad of topics that exist at the boundary of Biology and Computer Science. I believe that every productive research endeavour is always of an interdisciplinary nature and these two fields have immense capabilities of complementing each other. Hence with a belief that revolutionary discoveries are only a few keystrokes away, I joined the Landry Lab as a PhD student in the Fall of 2023. 

Research interests

I have worked extensively with NGS data and am familiar with many essential bioinformatics tools. Although I am a big fan of the R-programming language, I have experience in handling big data and developing machine learning models in MATLAB and Python respectively. Using these tools at hand, I intend to study the evolution of anti-fungal resistance in pathogenic yeasts via population genomics studies of clinical and experimentally evolved strains of Candida spp. Eventually, I aim to develop machine learning tools for prediction of resistance based on patterns of polymorphism in the fungal genome and make such tools accessible to public health labs for implementation. I find the act of discerning order in the chaotic world of biological systems immensely satisfying. The fact that it is possible to achieve this within the confines of relatively a few lines of codes is very thrilling.