Dr. Parida’s research focuses on developing data-driven physics-based modeling frameworks that estimate model parameters in a Bayesian machine learning framework and forecast behavior of dynamic systems probabilistically. This eliminates the complacency of a purely empirical approach based only on observed data, while ensuring that physics of the problem is captured. Moreover, any uncertainty associated with the system is also robustly quantified.
His teaching interests closely relate to research interests. Broadly they are in theoretical and computational solid mechanics with emphasis on geomechanics, application of probability theory in civil engineering, and data assimilation and stochastic filtering for civil engineers.
His teaching interests closely relate to research interests. Broadly they are in theoretical and computational solid mechanics with emphasis on geomechanics, application of probability theory in civil engineering, and data assimilation and stochastic filtering for civil engineers.
Ph.D. - Doctor of Philosophy in Civil Engineering: Geomechanics, Geomechanical and Geoenvironmental Engineering, University at Buffalo
M.S. - Master of Science in Civil Engineering, University at Buffalo