Research

Control in cardiovascular health

The heart is essential to our health, yet the automatic control aspect in cardiovascular health is still premature. Each heart is different, every patient has a different health and history, and especially the female heart anatomy and physiology is often underrepresented in research. What if we could innovate control systems for optimized interaction of soft cardiovascular pumps and wearable biofeedback systems with the human body?

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Soft robot control

The elephant trunk is an object of fascination for biologists, physicists, roboticists, and children alike. Its versatility relies on the interplay of multiple unique physical mechanisms and biological design principles. Translating these characteristics into engineering result in very cool soft robots that are especially promising when interacting with fragile environments, but their control poses great challenges.

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My previous works in robotics dealt with disturbance rejection to control Festo’s Bionic Soft Arm.

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PDE control for population dynamics

Population dynamics are fundamental in biology, ecology, demographics, or epidemiology, where infinite-dimensional systems capture the populations’ behavior over time. Their mathematical representation as integro-partial differential equations result in many interesting control problems that we tackle with control Lyapunov functions, backstepping, and neural operators.

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Multi-sensor tissue differentiation in oncology

Precise differentiation of pathological tissue during surgery is crucial in oncology. The current gold standard, histopathological analysis, involves delays due to tissue processing, impacting real-time decision-making. Intraoperative sensors measuring electrical, optical, or mechanical properties can help surgeons to make an informed decision.

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Material fingerprinting

To understand the physical behavior of materials and accurately simulate their mechanical behavior under complex shapes and loading conditions, it is essential to develop mathematical models that accurately describe the materials’ mechanics. This mechanical characterization of materials traditionally involves solving an optimization problem. With our new approach, material fingerprinting, we avoid such potentially non-convex optimization problems and present a new method for the rapid discovery of mechanical material models.

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