University Research Group
Understanding the mind. Improving care.
We conduct research at the intersection of mental health, clinical psychology, and population health — translating evidence into real-world impact.
Learn moreAbout us
Who we are
We are a clinical psychology research group based at the [University Name], dedicated to advancing the understanding, prevention, and treatment of mental health conditions across the lifespan.
Our core research areas include:
- Epidemiology and risk factors of common mental disorders
- Psychological treatment development and evaluation
- Digital mental health and technology-supported interventions
We are committed to open science, interdisciplinary collaboration, and translating our findings into improved healthcare policy and clinical practice.
Affiliated with
Our team
The people behind the research
Pavol Mikolas
Group Leader Medical & Managing Senior Physician
Janek Haschke
Group Leader Technical & Organisational
Gerrik Verhees
Resident Psychiatrist (UKDD)
Hannah Schwarze
MD Candidate
Fabian Huth
MD Candidate (UKDD)
Julia Weisser
MD Candidate
Vincent Meyer
MD Candidate (UKDD)
Medea Glasmeyer
MD Candidate
Guillermo Calvi
MD Candidate
Paula Gutekunst
MD Candidate
Simon Baumann
Student Assistant
Research projects
Current and recent projects
Validation and optimisation of risk classifiers for psychosis and mania in adolescents and young adults using highly heterogeneous, multinational datasets and AI-based methods
AI-supported methods for recording and analysing psychiatric symptoms from medical documentation and language
AI-supported identification of lithium responders: automated analysis of the Alda scale from electronic medical records
Improvement of the prediction of treatment outcomes for treatment-resistant depression using artificial intelligence methods
Cooperations
Partners and collaborators
We work with academic institutions, clinical partners, and public health organizations to maximize the impact of our research.
Publications
Selected recent publications
Detection of suicidality from medical text using privacy-preserving large language models.
Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features.
Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes.
Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.
Individuals at increased risk for development of bipolar disorder display structural alterations similar to people with manifest disease.
Effects of early life adversity and FKBP5 genotype on hippocampal subfields volume in major depression.
Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study
Contact
Get in touch
We welcome inquiries from prospective collaborators, students, and media. Please reach out by email — we will get back to you as soon as possible.