Due to the dramatic increase in computational power, data storage and generation capability in the last decade, the engineering landscape is morphing continuously. There is now an expectation for substantial improvement in productivity, safety and sustainability of engineered systems through big data science. Traditional fields such as control and diagnosis are being empowered and re-imagined in ways that were not anticipated before. In parallel, there are important questions regarding the relevance of domain knowledge and traditional tools in this data–centric world that need to be answered. In our group, we focus on both development of methodological tools for data science and application of such tools in several engineering domains. Methods for domain agnostic integration of prior knowledge into data science algorithms are of special interest.
Human development depends on energy. With increasing world population and developmental ambitions of all parts of the world, sustainable generation of energy becomes a critical need. Our research in this area is focused on improving the efficiency of energy conversion devices. With a clear understanding that “one size does not fit all”, we explore different energy conversion technologies such as fuel cells, flow batteries and micro-photosynthetic cells. Some of our research is also on cross-cutting technologies for energy systems (conversion and storage), with a special emphasis on primary batteries.
Fluids research is as relevant now as it has been for several decades past. Much of the recent excitement comes from understanding fluid flows in small length scales and also from the expanding application possibilities. Our fluid systems engineering research focus is on microfluidics and applications of fluidic systems in particle synthesis. We are specifically interested in droplet microfluidics, where a dispersed phase is compartmentalized as a droplet or a bubble in its surrounding continuous phase. Our explorations in this field are non-traditional and strongly rooted in a systems engineering perspective. The application areas that we envisage for droplet microfluidics are also non-traditional.
Biology is one field that is making incredible progress due to the availability of big data. Omics data at different information scales is revolutionizing this field. In our group, we explore the use of data science algorithms for understanding biological systems. Of particular interest is the development of a generalized framework that can integrate data from different networks seamlessly for enhanced understanding of biological processes. These networks could be signaling, metabolic and/or transport networks. Once developed, this framework could be used in several studies such as, for example, disease pathogenesis.