Dr. Surajit Deb Barma

I have several years of teaching and research experience in reputed institutions in India. I love to teach as much as I love to do research. I also love collaborative research in which my ideas and collaborators' ideas are fused to develop a novel concept. Though teaching and research sound different, one thing is prevalent: it takes years of hard work and patience to be effective at these skills.
I have also had the opportunity to supervise students both at the undergraduate and postgraduate levels. I love mentoring students, especially those who show interest and put in some effort to get things done. I do not mentor students like the traditional supervisors or guides; I think I am also a part of the projects at hand. So, I take ownership of their projects by helping them with necessary tools like programming, selecting topics, providing them with the most relevant scientific papers, and teaching them how to write articles. The level of master's students' projects is worth getting published in reputed peer-reviewed journals. One of the papers was India's top paper during March 2021 in ASCE.
I also make them aware of the need for specific skills to work in industry or pursue higher studies. I feel proud that I could get two of the master's students to pursue their PhD abroad.
When it comes to my research, I thoroughly analyze whether it could be feasible given data availability, coding skills, etc. At times, I don't shy away from seeking the help of other fellow researchers or programming experts with entirely different backgrounds—only if I am not successful in getting help from online platforms. One of our recent papers with a fellow PhD colleague was selected for the Editor's Choice award by a water journal. Some collaborative works are in the initial stages of discussion, and some are submitted to reputed journals. I can't wait for many novel research outcomes from those works in reputable journals in the next few years, especially in machine/deep learning for time series prediction of hydro-meteorological variables.
I am open to working on hydrological problems (drought, flood, compound events, time series, trend analysis, etc.) from local to global scales using statistical, process-based, and machine learning models. To achieve these, I often use gauge, satellite, and reanalysis data as inputs to the above models to answer some of the pressing hydrological and climate issues.
Finally, I am also looking for research collaborators with similar interests.