Zhiyuan Ma

Ph.D Student

Research fields

My research primarily focuses on artificial intelligence, computational neuroscience, and computational psychiatry, aiming to integrate both data-driven and mechanistic modeling approaches to advance our understanding of brain function and the neural basis of mental disorders. Specifically, in the domain of EEG decoding, I leverage EEG and multimodal neural signals to develop feature extraction and modeling methods for the identification and prediction of cognitive states, emotions, and mental health levels, with particular emphasis on improving model interpretability and cross-subject generalization. In terms of neural dynamics modeling, I construct multi-region coupled models grounded in neural dynamics theory, with a focus on characterizing non-periodic neural noise and 1/f properties, thereby elucidating brain activity patterns underlying normal and pathological states from a mechanistic perspective. In the AI-for-healthcare domain, I explore the application of artificial intelligence methods to the diagnosis and prognosis of psychiatric disorders such as depression, with the ultimate goal of facilitating the clinical translation and practical deployment of AI in medicine.

Education

September 2025–Present, PhD Student, School of Biomedical Engineering, Tsinghua University

September 2021 – June 2025, Bachelor of Engineering, College of Computer Science and Technology, Zhejiang University

Publication List

Sun Y, Liao W, Li J, et al. Reward-Optimizing Learning using Stochastic Release Plasticity[J]. Frontiers in Neural Circuits, 19: 1618506.