I am broadly interested in how conscious processing is reflected in neural data and how computational models, as entailed by e. g. measures of complexity, may inform this relationship. So far, my work has been strongly interdisciplinary, intersecting machine learning, neuroscience, as well as philosophy. Most recently, I have been investigating information integration in variational inference in order to explore its potential to inform sensory processing within the framework of predictive processing, using a dynamical complexity measure for time-series models as well as a black-box variational inference procedure. Before, I was involved in an EEG-based BCI study to investigate bodily consciousness in a self-paced embodiable neurofeedback motor-imagery training for stroke patients. In my current doctoral work at the Sackler Center for Consciousness Science, I aim to apply mathematical models based on information theory to neural data and use machine learning techniques to investigate how measures of complexity and emergence may inform an account of differences in conscious states.
Besides science, I have worked as a data analyst, teaching assistant for German for refugees, and psychologist for unaccompanied refugee minors. I like volunteering, and, most recently, have been engaged in environmental sustainability at GrowNYC, New York.