I combine computational and experimental approaches to investigate dynamic and control problems in soft biological systems. I particularly focus on revealing mechanisms, through which soft slender-bodied animals can combine embodied control principles and neural sensory and feedback to achieve complex motion behaviors.
To this end, I started by establishing a computational framework for the modeling and simulation of complex musculoskeletal architectures. This framework pioneers the use of one-dimensional, elastic slender body assemblies (via Cosserat rods theory) to construct active, heterogeneous, and three-dimensional biological layouts. I have demonstrated the utility of this approach across biophysical scenarios, scales, and environment, from human joints to full-scale aquatic, terrestrial and aerial creatures. I futher combine it with theory to reveal a unified mechanism that explains the natural gait selection of limbless locomotion of snakes, and deploy it for engineering control.
In conjunction with compuatational endeavors, I also combine the musculoskeletal solver with evolutionary algorithms to aid in the design and realization of both walking and swimming bio-hybrid robots. These systems combine living materials (muscles and neurons) with artificial scaffolds to achieve autonomous and controlled locomotion. Recapitulating the musculoskeletal features encountered in nature, they also represent ideal platforms to probe principles of biological control, sensing and actuation.
My current research also focuses on decoding the inner workings of biological neural networks and streamlining their use in computing and robotics. I have been leading the development of a fully customizable, versatile and scalable system for multimodally interacting with in-vitro neurons across scales and environments. This work provides a technological foundation for deploying neural intelligence in engineering applications, while paving the way to a novel neural computing paradigm.