Maurice Filo

Maurice Filo is a Postdoctoral researcher at the Department of BioSystems Science and Engineering at ETH Zurich. His research interests lie in exploring the intersection of various scientific disciplines with control theory and dynamical systems. Earlier in his academic career, he earned a master's degree in electrical and computer engineering. He later transitioned to mechanical engineering, which ultimately resulted in him obtaining another master's degree and a PhD. More recently, Maurice's focus has shifted to biology, particularly synthetic biology, where control theory is being used to revolutionize various biological fields. In his research, he frequently moves back and forth between electronics, mechanics, and biology, as they can be linked via control and dynamical systems theory, and provide inspiration for each other.

During his postdoctoral research, Maurice's main focus lied at the intersection of control theory, chemical reaction network theory, and genetic circuit design. He developed a series of advanced biomolecular controllers that can provide adaptation capabilities to biological systems in the face of their inherent noisy environments. He used chemical reaction networks as building blocks to mathematically realize fundamental control-theoretic architectures, such as proportional-integral-derivative (PID) feedback controllers. Maurice used a variety of deterministic and stochastic tools to analyze and tune these controllers, aiming to improve their dynamic performance and reduce cellular variability and noise. He worked closely with experimentalists in the wet lab to translate the theoretical designs into genetic circuits, which were then tested and validated to ensure they adhered to the predictions of the theory. His collaboration with biologists resulted in several papers bridging theory and experiments and two patents that are currently pending.

Maurice's Ph.D. research covered a diverse range of topics, including developing a control-theoretic framework for analyzing structured stochastic uncertainty and investigating instabilities in the ear. He also focused on optimal path planning for mobile sensors in stochastic or deterministic distributed environments, designing the most effective trajectories for maximal estimation accuracy. He explored two sensing schemes and assimilated data obtained from measurements with governing physical laws described by partial differential equations, ultimately formulating an optimal control problem. The large-scale optimal control problem resulting from this research led to further investigations into new numerical methods for solving general optimal control problems.