Alp Akova
PhD Candidate in Cognitive Brain Sciences
Researcher in Cognitive Aging, EEG, and Machine Learning
About Me
I am a PhD candidate at the University of Trento, specializing in cognitive aging, EEG-based biomarkers, and machine learning applications in neuroscience. My research focuses on understanding how physical activity influences brain connectivity and cognitive health in aging populations, leveraging EEG-based age prediction (brain-PAD) and advanced machine learning models to explore neural aging trajectories.
My work integrates EEG connectivity analysis, neural synchronization, and frequency-specific oscillations to identify markers of cognitive resilience. I employ deep learning, ensemble methods, and multimodal data integration to enhance the accuracy of brain age prediction models.
Beyond my doctoral research, I have experience in large language models (LLMs), NLP, and decision-making experiments, having worked at Subconscious AI and the University of Padova's Baby Lab. My expertise spans EEG analysis, signal processing, graph theory metrics, and data-driven cognitive modeling.
Research
Current Research
My primary research investigates the relationship between physical activity and cognitive aging, using EEG-based brain age prediction (brain-PAD) and advanced machine learning techniques. This work aims to develop personalized interventions for promoting brain health in aging populations.
Research Interests
- Cognitive Aging and Brain Health
- EEG Analysis and Brain Age Prediction
- Machine Learning in Neuroscience
- Physical Activity and Neural Plasticity
- Neural Synchronization and Brain Networks
- Large Language Models and NLP