Surface Roughness Effects in Squeeze-Film Dampers: Our study investigates the impact of isotropic surface roughness on hydrodynamic squeeze-film damper (SFD) performance. We develop a novel predictive framework combining numerical simulations and machine learning to model SFD behavior as a nonlinear oscillator. By training ML models on a large dataset of simulated damping forces for diverse surface topographies, we can accurately predict damping performance under real-world conditions. This innovative approach enables engineers to optimize designs, predict performance deviations, and ensure system reliability in industries like aerospace and turbo-machinery.

(Current) Tribological Performance of SiC Nanoparticles and Nanowires: This study investigates the effects of tribological performance by adding SiC nanoparticles and nanowires to a base polymer, focusing on wear rate reduction. We want to find the effects of SiC additives that can effectively enhance the wear resistance of the tested base polymer, making them suitable for applications requiring high tribological performance.

This research initiative undertakes an in-depth qualitative investigation into African American perceptions of healthcare technology, probing user needs, preferences, and the far-reaching health consequences of product design. By shedding light on these critical factors, our study aims to inform the development of culturally responsive health interventions that prioritize equity and scalability. This is apart of the Digital Technologies for Equitable and Scalable Healthcare Initiative at Rice. 

We pioneered a novel integration of ASTRO, an autonomous drone platform, with RENEW, a next-generation Massive MIMO base station, to unveil the previously unseen dynamics of wireless interactions between drones and MIMO systems. By harnessing the strengths of both technologies, we unlocked new avenues for understanding the complex relationships between high-dimensional interactions in these emerging communication networks.