Research

Advancing intelligent systems for mental health through AI, neuroscience, and computational methods

My primary research interests lie in applied AI and data science, particularly AI-powered descriptive and predictive models in healthcare. I am interested in advanced AI mechanisms to automatically and objectively analyze diverse range of health data (e.g., medical images, clinical notes, EEG/ERP signals, and patient-provided information) to precisely tackle clinical prediction and description mainly focusing on mental health and neurological disorders.

The interdisciplinary nature of my research has been helping me to foster further collaborations with physicians, neuroscientists, psychiatrists, informaticians, and also software engineers to implement computational methods for modeling real-world healthcare problems. My main technical and clinical interests are as follows:

Technical Focus

  • Explainable, Interpretable, and Accountable AI and Machine Learning
  • EEG/ERP Signal Processing and Analysis
  • Neurofeedback and Brain-Computer Interfaces
  • Deep Learning for Medical Image Analysis
  • Few-Shot and Transfer Learning

Clinical Focus

  • Mental Health Disorders (ADHD, Depression, Anxiety)
  • Neurological Disorders and Neurodevelopmental Conditions
  • Cognitive-Motor Assessment and Intervention
  • Digital Health and Intelligent Diagnostic Systems
  • Personalized Healthcare and Treatment Planning

Current Research Agendas

My current computational and technical research focus spans the following key areas:

01

Multimodal Clinical Data Analysis

Integrating diverse data sources including EEG/ERP signals, medical imaging, clinical assessments, and behavioral data to create comprehensive diagnostic models for mental health and neurological conditions.

02

Explainable AI for Healthcare

Developing interpretable machine learning models that provide transparent decision-making processes for clinical applications, enabling physicians to understand and trust AI-driven diagnostic recommendations.

03

EEG/ERP Signal Processing for Mental Health

Advanced computational methods for analyzing brain signals to identify biomarkers of mental health disorders, particularly ADHD, depression, and anxiety in children and adults.

04

Cognitive-Motor Behavior Assessment

Intelligent systems for evaluating motor performance and cognitive function through computational analysis of movement patterns, reaction times, and behavioral responses in clinical and educational settings.

05

Few-Shot Learning for Medical Applications

Developing AI models that can learn from limited clinical data, addressing the challenge of rare conditions and small patient cohorts in medical research and diagnosis.

Medical Innovation Research in AI Laboratory

Further information about my research agendas and ongoing projects can be found via my research laboratory, the Medical Innovation Research in AI (MIRAI) Lab, where we focus on developing cutting-edge AI solutions for healthcare challenges, with particular emphasis on mental health diagnostics, neurofeedback systems, and intelligent clinical decision support tools.

Visit MIRAI Lab

International Collaborations

🇨🇭

Brain and Trauma Foundation

Switzerland

EEG/ERP Analysis & Neurofeedback
🇹🇷

Üsküdar University

Türkiye

AI in Mental Health & Software Engineering
🌍

International Research Network

Worldwide

Healthcare AI & Biomedical Informatics