With my students and other collaborators, I explore and develop new methodologies for the structural vibration control, data assimilation, and health monitoring of complex systems, ranging across mechanical, civil, and aerospace systems. The focus is primarily on the identification of these systems from sensor data, building reliable digital twin (i.e., physics-plus-data-driven reduced-order) models for prediction, and then designing robust controller algorithms.
To meet our objectives, we often employ Bayesian modeling techniques, data assimilation techniques (such as filtering techniques), and machine learning algorithms. Our group also has ongoing collaborations with researchers from overseas universities such as the University of Sheffield, the University of California San Diego, and the Hong Kong University of Science and Technology.
Within the department, I work in close collaboration with Prof. Souvik Chakraborty.
Interested in joining our group?
Students with a strong analytical background, and an outstanding MSc/MTech degree in Civil/Aerospace/Mechanical Engineering, Control, Applied Mathematics, or a related field. Experience with coding in MATLAB/Python/Julia would be necessary. Prior experience in structural vibrations and control, differential equations, machine learning, signal processing, statistics, and learning theory will be beneficial.
There is a vacancy for a JRF position for two years in our group. The position requires a person who has a Master's degree preferably in Computer Science, Electrical, Mechanical, or Civil Engineering. A person well-conversant with MATLAB/Python would be required. A project on which the hired student will work