Team Members

Chris holds a PhD in Medical Research focusing on brain circuitry alterations in psychotic and affective disorders. At LMU Klinikum, he supports the early recognition outpatient service and works on early detection tools for individuals at high risk for psychosis.

Keywords: diagnostics, psychosis, risk, clinical presentation, MRI, personality

Lisa holds a PhD in Medical Sciences, MSc in Cognitive and Clinical Neuroscience with a specialization in Neuropsychology, and BSc in Psychology. Her research focuses on mechanistic precision psychiatry, integrating neuroimaging data (sMRI, fMRI) with neurotransmitter distribution maps to investigate the underlying biology of psychotic disorders, particularly schizophrenia. An additional line of research explores the role of thyroid-stimulating hormone (TSH) in the pathophysiology of psychotic disorders. She is also part of the NeuroMiner development team.

Keywords: machine learning, neuroimaging, sMRI, fMRI, neuromapping (JuSpace), BrainAGE

Mafe holds PhD and a MSc in Neuro-Cognitive Psychology from LMU. She has been actively involved in the collection of PRONIA data, and she is involved in the early recognition outpatient service at LMU Klinikum. Her research focuses on sex differences and multimodal data integration in the early psychiatric spectrum, with the goal of improving women’s mental health.

Keywords: sex differences, machine learning, schizotypy, multimodal data, women’s mental health, PRONIA, early recognition

Julia Fietz holds a PhD in Medical Research as well as an MSc and BSc in Psychology. She is currently in training as a cognitive behavioral therapist. Her research focuses on the integration of multimodal data—including neuroimaging, psychophysiology, genetics, and neurocognition—with clinical information to improve the understanding and prediction of mental health outcomes. A key area of her work is the use of machine learning techniques to enhance the prediction of suicidal risk in young adults.

Keywords: Multimodal Machine learning, Neuroimaging, transdiagnostic approaches, suicidal risk 

Liisi Promet earned her B.Sc. in Biology from the University of Tartu, Estonia, and her M.Sc. in Neuroscience from the International Max Planck Research School in Göttingen, Germany. She is interested in disentangling the biological heterogeneity in psychosis from the clinical high-risk state to established schizophrenia. Her current projects include a systematic review on biological subtypes in schizophrenia, predicting conversion to psychosis using proteomics data, and investigating the longitudinal course and treatment response in neuroanatomical subtypes of schizophrenia. 

Keywords: schizophrenia, psychosis, heterogeneity, subtyping

Elif Sarisik is a medical doctor with a degree from Istanbul University. Her research includes developing machine learning approaches to identify EEG-based signatures of schizophrenia, depression, and aberrant brain aging, as well as predicting risk factors for mental disorders in transdiagnostic populations. Her work emphasizes the integration of multimodal data, such as EEG and phenomenological analyses, to advance predictive and preventive psychiatric interventions. 

Keywords: EEG, schizophrenia, machine learning, transdiagnostic, sparse partial least squares (SPLS)

Link to individual research

John earned his BA in Psychology at the University of Colorado – Boulder and his MSc in Neuro-cognitive Psychology at the Ludwig-Maximilians-University Munich. He is a PhD candidate with a particular focus on schizotypy and its relationship with neuroimaging, including structural and resting-state modalities. He also works as part of the group’s early recognition service for the ProNET project.

Keywords: depersonalization, derealization, schizotypy, sparse partial least squares (SPLS), sMRI, rs-fMRI, gyrification

Maria holds a MD from the National and Kapodistrian University of Athens and a MSc in Biomedical Engineering from Université de Paris – PSL – Arts et Métiers (BMEParis), while she has prior experience in computational neuroscience. She is a PhD candidate of the International Max Planck Research School for Translational Psychiatry, studying deep mechanistic modeling of treatment response in psychosis as part of the European Virtual Brain Twins project.

Keywords: recent onset psychosis, symptom trajectories, virtual brain twins 

Renata holds both a M.Sc. in Computer Science from the University of Campinas, Brazil, and a B.Sc. in Computer Science from the São Paulo State University, Brazil. With a background in machine learning and deep learning, her current research focuses on investigating differences in structural and functional MRI patterns related to auditory verbal hallucinations in patients with mental disorders. She serves as the Coordinator of the Multicenter Collaborative Study of the German Center for Mental Health (DZPG) and the Medical Informatics Initiative (MII), where she also leads data and IT management tasks. Additionally, she is responsible for the IT and data management within our working group.

Keywords: data management, IT management, data quality assurance, project coordination, data science, machine learning

Richard holds an MD from Charité Berlin and is currently pursuing an M.Sc. in Robotics, Cognition, and Intelligence at TU Munich. He is training in psychiatry at LMU Klinikum, where he supports the early recognition outpatient clinic. His research focuses on applications of large language models in psychotherapy and mental healthcare, with the goal of improving the accessibility and reach of behavioral health services.

Keywords: deep learning, large language model, natural language processing, cognitive behavioral therapy, clinical reasoning

Dr. Lenka Krcmar holds an MD and a PhD in Medical Research with a focus on Translational Psychiatry from the International Max Planck Research School in Munich. She is currently undergoing specialty training in psychiatry at LMU Klinikum.

She is involved in a research project aimed at uncovering multivariate associations between brain structure and function and cognitive performance in individuals with recent-onset psychosis (ROP). Identifying neurobiological correlates of cognitive functioning may support early diagnosis, improve prognostic accuracy, and inform personalized treatment strategies.

Keywords: recent onset psychosis, cognitive disturbances (COGDIS), sparse partial least squares (SPLS), multivariate analysis, sMRI

Nikos studies medicine at LMU Munich. His current research focuses on computational MRI-based subtyping of depressive disorders using machine learning methods. As an MD student in the FöFoLe-Program, an elite research training program of the LMU Faculty of Medicine, he specializes in data management and preprocessing of neuroimaging and clinical datasets, developing MATLAB-based workflows for the lab's research cohorts.

Esther holds a PhD in Signal Processing, along with a B.Eng. and M.Eng. in Audiovisual Systems and Multimedia Communications. Her current research focuses on developing generalizable, fair, and interpretable Deep Learning models to predict transition to psychosis in clinical high-risk patients. 

Personal Website / Contact: https://erituert.github.io/

Keywords: machine learning, deep learning, transformers, computer vision, mental health, smri

Jana holds an MD from LMU & TU Munich and an M.Sc. in Neuroscience from King’s College London. She trains in child and adolescent psychiatry and currently supports the early recognition service at LMU. In her research, she employs transdiagnostic and multimodal machine learning approaches to identify early markers of psychiatric disorders at different developmental stages, eventually aiming to facilitate timely and targeted interventions in youth mental health. 

Keywords: youth mental health, adolescence, transdiagnostic, prevention, early intervention