Author: Lois Hwang

Since I began learning the basics of science, the effects of vibrations on environments has always fascinated me. As Nikola Tesla said, “If you want to find the secrets of the universe, think in terms of energy, frequency and vibration.” It was with this aim that I started my Ph.D journey in Fall 2020.

My research has been in regards with “Metastructures”. These are unique structures which absorb vibrations in a system in particular frequency range, often called as ”bandgap”. It is because of this bandgap phenomenon that metastructures are widely used to mitigate vibration effects. Owing to large number of applications, it becomes important to estimate bandgaps in a metastructure to predict the frequency range in which the vibrations will be absorbed so that metastructures can be designed for various applications.

Through my research, I have aimed to developed new methods to estimate these bandgaps. The current methods available in the literature require a physics-based model of the metastructure (analytical model, finite/spectral element model) in order to estimate bandgaps. However, for various anisotropic materials, the material properties are difficult to quantify accurately which makes the physics-based model inaccurate. My research aims to overcome these limitations by developing methods which estimate bandgaps using purely experimental data. We have used the experimental data to study how a vibration wave is propagating through the metastructure (dispersion curve) and estimated bandgaps. Various other techniques such as substructuring, data-driven modeling algorithms were utilized. The developed techniques considerably reduced the design efforts required and made the entire design process much easier.

The funding provided by this fellowship will truly be helpful for me in putting all my energies in finishing my thesis in time and complete my Ph.D.!
I am grateful to the Graduate School for granting me this fellowship.
My advisor, Dr. Sriram Malladi has been more than helpful in guiding me through various ups and downs throughout my Ph.D journey. I am truly thankful for the relentless support he and his family has given me. Last but certainly not the least, I am thankful to my family i.e. my wife and my parents for their support in every aspect of my journey!

Growing up in a township full of scientists and engineers, I have always been curious about how things work. This led me to pursue a bachelor’s in engineering from Nehru Institute of Engineering and Technology affiliated with Anna University, Chennai. I then pursued a master’s from the esteemed Indian Institute of Technology, Madras where I delved into diverse research projects that captivated my interest. Fueled by this newfound interest, I started my journey as a Ph.D. student eager to tackle intriguing and fundamental challenges within the field of engineering.

I started working on my Ph.D. in the Fall of 2019 at the Computational Mechanics and Machine Learning Lab led by Dr. Susanta Ghosh at Michigan Tech. The focus of my research is on understanding the uncertainties associated with the predictions of computational and machine-learning models. Any model, computational or data-driven, is a representation of a physical phenomenon. We develop such models to understand the world around us better. However, predictions of such models are not always reliable due to the uncertainties associated with them. These uncertainties could arise for various reasons such as natural variability in the systems we study, assumptions in developing these models, numerical approximations, lack of data, etc. In order to use these models in real-life scenarios, quantifying these uncertainties is crucial. My research involves developing novel techniques to quantify the uncertainties, use these uncertainties to improve the model’s performance, and explain the reasoning behind the uncertainties. In my first project, we developed a Bayesian neural network-based machine-learning model that can reliably classify breast histopathology images into benign and malignant images. In addition, the model can quantify uncertainties associated with the predictions. We further developed techniques to explain the uncertainties and use them to further improve the model’s performance. In my second project, we developed novel loss functions for Bayesian neural networks and showed their advantages over the state-of-the-art in image classification problems. I am currently working on quantifying uncertainties in computational models that are used to characterize material behavior and extending the first two projects for several other applications.

I would like to thank my advisor Dr. Susanta Ghosh for giving me the opportunity to carry out this exciting research as well as for his immense help and guidance throughout the process. I thank the Graduate Dean Awards Advisory Panel and the dean for recommending me for this award. It is an honor. I thank the graduate school and the Department of Mechanical Engineering-Engineering Mechanics for their constant support.