I am currently a postdoctoral associate in the department of Mechanical Engineering at MIT working with Prof. Turitsyn (Kostya Turitsyn's group) on autonomous climate control in buildings. In this project I am working on developing physics-constrained reinforcement learning algorithms to design data-efficient coordinated controllers for buildings HVAC systems. Prior to my postdoctoral appointment, I received my Ph.D. from MIT in 2018 and M.Eng. from NTU of Singapore in 2013, both in mechanical engineering. My Ph.D. research focused on nonlinear vibration energy harvesting; from fundamental limits to practical approaches. I studied maximal power limits of vibratory energy harvesters and how we could leverage nonlinearity and instability to approach these limits in practice. I also studied the robustness issues under parametric and environmental uncertainties in passive and active harvesters.
In general, my research passion is to solve complex real-world problems in dynamics and control by developing novel techniques using insights from numerous different disciplines of science and engineering. My research activities began with classical structural vibration analysis and control and have transitioned towards more unconventional approaches in dynamical systems. My current research goal is to augment classical approaches in dynamics and control with novel tools from nonlinear dynamics and machine learning to extend the applicability and scalability of conventional methods to new and interesting problems across different disciplines. Some interesting applications include designing tunable structures and fast motion actuators by exploiting nonlinearity and structural instabilities. Another potential application that I am interested in is real-time structural health monitoring and vibration control of complex structures by leveraging distributed measurements and machine learning.