I am broadly interested in advancing experimental and computational imaging methods for multiphase flows, with a particular focus on digital inline holography (DIH) and machine learning (ML). We have recently developed a low-cost DIH sensor capable of high-resolution imaging, which has been applied to spray characterization, dental aerosol tracking, and microorganism tracking in biofilms.

Building on this work, my research will focus on three main directions.
(1) Fundamental multiphase dynamics: This includes studying droplet growth, clustering, spray droplet dynamics, breakup mechanisms, and four-way coupling in multiphase flows with background turbulence.

(2) Methodological innovation: By integrating ML models trained on synthetic hologram data, we aim to enable real-time, high-throughput tracking of droplets, aerosols and bio-particles. This approach improves tracking in complex and dense flows and supports applications such as clean-water monitoring, rare blood cell detection, and pollution tracking.

(3) Interdisciplinary applications: We aim to combine DIH and ML for applications in areas such as atmospheric science (cloud microphysics and rain formation), biomedical diagnostics (rare blood cell detection and biofilm monitoring), and pharmaceutical processes such as spray drying.

By combining experiments, computational tools, and collaborative applications, I aim to develop practical and scalable solutions that address challenges in fluid mechanics, environmental science, and healthcare. For more details on my research, please visit my website (https://sites.google.com/umn.edu/shyamkumarm/bio?authuser=0)