After a Bachelor in Psychology at the University of Göttingen and the Universidade Federal do Ceará, I studied Cognitive Neuroscience at Maastricht University and wrote my master thesis at the Translational Neuromodeling Unit, University of Zurich and ETH Zurich, University of Zurich and ETH Zurich. During my PhD in Computer Science at the University of Basel and the Krembil Insitute for Neuroinformatics in Toronto, I cast different symptoms of schizophrenia as instances of hierarchical Bayesian inference and used machine learning to predict clinically relevant outcomes like treatment response and transition to psychosis. Since 2022, I have joined Rick Adam's lab at UCL and I am now a senior research fellow. At UCL, I focus on increasing the biological plausibility of these computational tools, developing models to measure cell and neuroreceptor function non-invasively. This advances personalized care and tests key aetiological theories of schizophrenia directly in patients. To validate these models, I test them in pharmacological studies and in collaboration with the North American Prodrome Longitudinal Study (NAPLS) and Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortia across the schizophrenia disease trajectory and across transdiagnostic biotypes.

My long-term goal is to use these models as 'computational assays' to non-invasively test for cell or receptor dysfunction in patients with psychiatric and neurological disorders. This includes 1) Identifying new drug targets by using the models as digital twins to simulate intervention effects on individual patients, 2) Identifying critical time windows for early interventions and 3) Using model parameters as biologically explainable (XAI) predictors for treatment response and transition risk in individual patients to advance personalized care.