Introduction

I am a post-doctoral researcher currently at the Max Planck Institute for Human Development, associated with the Lifespan Neural Dynamics Group. Prior to my doctoral studies in the IMPRS Comp2Psych program, I completed an M.Sc. in the Berlin School of Mind and Brain program at Humboldt Universität zu Berlin.

I have a broad interest in Cognitive Psychology, Computational Neuroscience, Machine Learning, and Computational Biology and employ an array of techniques, including computational modeling of behavior, electroencephalography (EEG), structural and functional magnetic resonance imaging (s/fMRI), pupillometry as well as numerical simulations.

You can find a recent CV (here) !

Research Interests

General. I have been educated and am working at the intersection between psychology, brain and data sciences. In my experimental research, I apply computational and statistical modelling techniques (e.g., machine learning) to neural recordings to illuminate mechanisms that allow humans to be remarkably flexible and robust, at unparalleled energy efficiency. Inspired by complex systems research, I leverage the power of signal analysis to improve the measurement of neural dynamics from composite brain signals. I extend available analysis methods via scientific software development, and enjoy to ponder both about mind-brain philosophy, and how neuro-computational insights can inform generalized AI development.

Dynamic characterization of neural rhythms. Neural rhythms provide insights into how the human brain coordinates information processing in time and space. However, rhythms are not constantly present in neural recordings and a major goal is to identify rhythmic periods in time to better characterize these rhythmic signals and unlock insights into their generation and function.

Characterization of ‘neural complexity’. Neural mechanisms dynamically interact across multiple temporal and spatial scales, both within and across brain regions. This gives rise to a plethora of signatures measured at the scalp. A major goal is to better characterize these fluctuations to infer the presence of different neural activity regimes.

Facing environmental uncertainty. Humans frequently face complex environments with varying degrees of uncertainty about what should receive priority in processing. A major interest of mine concerns how the brain identifies this uncertainty, and how it changes its dynamics to create an adaptive course of action.

Thalamic influences on cortical dynamics and cognition. The deep brain thalamus is ideally suited to dynamically regulate specific computations in cortex. However, its multifaceted influence on the dynamics of cortical networks in service of perception, cognition and action remains elusive. I use a multi-modal approach combining high temporal resolution in the cortical EEG with high spatial resolution fMRI to probe these relations.