Research

Bio-inspired robotics

My current work focuses on learning-enhanced control of trunk-inspired soft robots: The elephant trunk is an object of fascination for biologists, physicists, roboticists, and children alike. Its versatility relies on the intricate interplay of multiple unique physical mechanisms and biological design principles. Translating these characteristics into engineering enables a novel, robust, and inherently safe family of soft robots.

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

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Control theory for partial differential equation in the context of population models

In parallel, I work on control-theoretic questions for hyperbolic partial differential equations that describe population dynamics. These are fundamental in biology, ecology, demographics, or epidemiology, where infinite-dimensional systems capture key population behaviors over time.

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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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