As an Assistant Professor in Cognitive Computational Neuroscience at Maastricht University, my research mission is to develop a deeper, more integrated understanding of the brain's computational principles. I achieve this by developing and applying biophysics-informed deep learning frameworks. This novel approach constructs trainable neural networks from biologically plausible units, such as laminar-resolved cortical columns with realistic internal dynamics and connectivity. By embedding function within models constrained by neurobiology, I create powerful in silico platforms to bridge the gap between neural mechanisms and cognitive function, allowing us not just to simulate, but to test, refine, and integrate fundamental theories of neural computation. My primary testbed is visual neuroscience, where I apply these methods to investigate low- and high-level processing in both health and disease. My teaching activities span Bachelor's and Master's levels, including course development, lecturing on computational neuroscience and related topics, and providing supervision. I actively mentor Bachelor's, Master's, and PhD students. As co-promoter I have guided numerous PhD trajectories focused on cutting-edge computational modeling and theoretical neuroscience.

My main research goal is to better understand the underlying mechanisms of the visual system by developing models that strike an effective balance between biological realism and cognitive function. My interest in visual perception, the visual hierarchy, and predictive coding theories was sparked during my Bachelor's in Cognitive and Neurobiological Psychology. The opportunity of modeling these processes in the brain excited me even more during my Master's in AI, where I was introduced to brain-inspired models and computer vision. As a PhD candidate in Cognitive Neuroscience at Maastricht University, I now explore these possibilities by developing a personalized, brain-derived generative model of mental imagery. My current focus is on creating a novel, biophysics-informed modeling approach that uses laminar-resolved cortical columns as building blocks for continuous-time neural networks. This approach allows embedding functionality while biological realism is maintained through neuroanatomical constraints. I thoroughly enjoy integrating my interests and expertise in this project, as well as working collaboratively in an interdisciplinary team in which we together aim to unravel the mechanism behind mental imagery.

Coming from an engineering background, my bachelor thesis on bio-inspired computer vision steered me towards trying to understand the brain better. To further this interest, I pursued a Master's degree in Cognitive Neuroscience at Maastricht University. During my master thesis, I worked on developing a macroscopic model of saccade generation under the supervision of Dr. Gorka Zamora-Lopez and Prof. Gustavo Deco at UPF Barcelona. As a PhD candidate, I am involved in developing biophysically-plausible models of the visuomotor system and currently focus on target selection. My broad interest lies in using dynamical systems theory to understand neural mechanisms of cognition.