Faculty Profiles

Structural Health Monitoring; Applied Mechanics; Geometrically Exact Modeling; Continuum Mechanics; Risk Analysis; Uncertainty Quantification; Machine Learning for Digital Twinning; Bayesian Decision Theory; Value of Information Analysis; Bayesian Optimization; Sensor Placement Design.

Capsule Bio:

Dr. Chadha's research encompasses a wide spectrum of ideas, ranging from fundamental applied mechanics to practical data-driven engineering decision-making. Dr. Chadha's work extensively utilizes diverse mathematical tools and modeling techniques to study the lifecycle of large structures, construct digital twins with specific targets in mind, establish decision-making frameworks for engineering applications, devise sensor optimization frameworks, quantify the Value of Information for Structural Health Monitoring (SHM) systems, use machine learning to build physics-based hybrid forecasting models, perform model updating via Bayesian inference as new information becomes available, and perform risk analysis and uncertainty quantification. His multifaceted research approach blends theoretical underpinnings with practical applications to address critical challenges in the field of engineering.

Dr. Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California San Diego. Before joining as an Assistant Adjunct Professor, Dr. Chadha conducted his postdoctoral research at UC San Diego. His postdoctoral research work focused on the development of Bayesian model parameter and model class optimization solutions for inland waterway civil infrastructure systems, as well as a framework for risk-based optimal sensing. This included developing advanced optimal sensor design strategies for SHM (Structural Health Monitoring) by explicitly and precisely formulating the problem in terms of Bayes risk and implementing a numerical optimization strategy to create an efficient solution for the sensor-design problem. He also worked on the investigation of uncertainty quantification, risk analysis, and evaluating the Value of Information in SHM-related applications. In one of his other projects, he contributed to building a machine learning-enabled calibration of hydrological model parameters for better prediction of river discharges. He was also involved in the development of a hybrid physics-based machine learning time series prediction model to forecast the river's discharge. Overall, Dr. Chadha's goal is to conduct multidisciplinary research and provide “modern” solutions to “modern” engineering problems.

Education:

University of California San Diego, Postdoctoral fellowship in Structural Engineering 2023

University of California San Diego, Ph.D. in Structural Engineering 2019

PSG College of Technology, Anna University, B.E in Civil Engineering 2014

YouTube Channel: https://www.youtube.com/@doctorchadha

Google Scholar: https://scholar.google.com/citations?user=t1xIWUwAAAAJ&hl=en&oi=ao