Daniel Hauke joined King’s College London in 2026 as an AI+ Senior Academic Research Fellow and Wellcome Early Career Award holder. His research in computational psychiatry integrates cutting-edge computational modelling, artificial intelligence and neuroimaging with clinical expertise to address critical challenges in psychiatry such as patients subtyping, treatment response prediction, and prognosis. As part of King’s AI+ Initiative, he contributes to advancing responsible and clinically meaningful applications of artificial intelligence in healthcare research, policy, and education.

Daniel completed a BSc in Psychology at the University of Göttingen and the Universidade Federal do Ceará, followed by an MSc in Cognitive Neuroscience at Maastricht University, where he conducted his master’s research at the Translational Neuromodeling Unit, University of Zurich & ETH Zurich. He earned his PhD in Computer Science at the University of Basel and the Krembil Institute for Neuroinformatics in Toronto.

Before joining King’s, Daniel was a Senior Research Fellow in Prof Rick Adams’s lab at University College London. His work at UCL focused on increasing the biological plausibility of computational models by developing methods to infer cell and neuroreceptor function from non invasive data. He validated these models in pharmacological studies and through collaborations with major international consortia, including NAPLS and B SNIP.

Daniel’s Wellcome project aims to identify biologically meaningful subtypes of schizophrenia characterised by distinct patterns of cell dysfunction, combining machine learning with biophysical modelling of cell and receptor function. His long term goal is to establish “computational assays” that test for cell or receptor abnormalities in 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.

Research Interests

• Computational Psychiatry
• Computational Neuroscience
• Artificial Intelligence
• Machine Learning
• Schizophrenia

Expertise and Public Engagement 

• Mentored students in the In2stem program
• Worked on film project with high-school students to learn about the effects of drugs on the brain
• Published talks and tutorials on YouTube
• Interviewed schizophrenia experts and provided interview for popular online blog psypost

Key Publications

Hauke, Rodriguez-Sanchez, Oloye, Berndt, Pinotsis, Friston, Mathalon, & Adams (2026). A Canonical Microcircuit for Estimating Excitation/Inhibition (E/I) Balance. Translational Psychiatry (accepted).

Rodriguez-Sanchez*, J., Hauke*, D. J., Pinotsis, D., Berndt, L. C., Oloye, H., Nicholas, S. C., ... & Mathalon, D. H. (2026). Biophysical modeling of excitation/inhibition balance and conversion to psychosis in the clinical high risk syndrome. Biological Psychiatry.

Charlton*, Hauke*, Litvak, Wobmann, de Bock, Andreou, Borgwardt, Roth, & Diaconescu (2026). Neural Dynamics of Social Cognition: A Single-trial Computational Analysis of Learning under Uncertainty.

Charlton*, Hauke*, Wobmann, Andreou, Mackintosh, de Bock, Andreou, Borgwardt, Roth & Diaconescu (2025). Localizing Hierarchical Prediction Errors and Precisions During an Oddball Task with Volatility: Computational Insights and Relationship with Psychosocial Functioning in Healthy Individuals. Imaging Neuroscience.

Hauke, Wobmann, Andreou, Mackintosh, de Bock, Karvelis, Adams, Sterzer, Borgwardt, Roth, & Diaconescu, (2024). Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis. Computational Psychiatry, 8(1), 1-22.

Hauke, Charlton, Schmidt, Griffiths, Woods, Ford, Srihari, Roth, Diaconescu,* & Mathalon* (2023). Aberrant hierarchical prediction errors are associated with transition to psychosis: A computational single-trial analysis of the mismatch negativity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(12), 1176-1185.

Hauke, Roth, Karvelis, Adams, Moritz, Borgwardt, Diaconescu & Andreou (2022). Increased belief instability in psychotic disorders predicts treatment response to metacognitive training. Schizophrenia Bulletin, 48(4), 826-838.

Dwyer, Buciuman, Ruef, Kambeitz, Dong, Stinson, ...Hauke, ... & Koutsouleris for the PRONIA Consortium (2022). Clinical, brain, and multilevel clustering in early psychosis and affective stages. JAMA Psychiatry.

Hauke,* Schmidt,* Studerus, Andreou, Riecher-Rössler, Radua, ... & Borgwardt for the PRONIA Consortium (2021). Multimodal prognosis of negative symptom severity in individuals at increased risk of developing psychosis. Translational Psychiatry.

Diaconescu, Hauke, & Borgwardt (2021). Models of persecutory delusions: a mechanistic insight into the early stages of psychosis. Molecular Psychiatry.

Das, Borgwardt, Hauke, Harrisberger, Lang, Riecher-Rössler, Palaniyappan, & Schmidt (2018). Disorganized gyrification network properties during the transition to psychosis. JAMA Psychiatry, 75(6), 613-622.