Assist. Prof. Dr. Aiying Zhang | Computational Neuroscience | Best Researcher Award

Assist. Prof. Dr. Aiying Zhang, University of Virginia, United States.

Dr. Aiying Zhang is a rising scholar in the field of mental health data science, currently serving as an Assistant Professor at the University of Virginia and a Faculty Member at the UVA Brain Institute. Her academic foundation spans statistics, biomedical engineering, and clinical biostatistics, acquired from esteemed institutions including USTC, Tulane University, and Columbia University. Her research focuses on developing advanced computational and statistical tools—such as graphical models and multimodal fusion—to decode complex brain data from imaging and genetics. She applies these innovations to better understand and predict psychiatric conditions such as schizophrenia and Alzheimer’s disease. Her work is distinguished by its interdisciplinary nature, translational relevance, and potential to reshape clinical approaches to mental health.

Profile

Google Scholar

 

🎓 Early Academic Pursuits

Aiying Zhang’s journey into the realm of data science and mental health research began with a strong foundation in quantitative sciences. She earned her Bachelor of Science degree in Statistics from the prestigious School for the Gifted Young at the University of Science and Technology of China (USTC) in 2014. Driven by a passion for biomedical innovation and its intersection with human health, she pursued a Ph.D. in Biomedical Engineering from Tulane University, which she completed in 2021. Her graduate years were marked by deep inquiry into statistical modeling and neuroimaging, laying the groundwork for her later interdisciplinary research. She further honed her expertise through postdoctoral training in Clinical Biostatistics and Psychiatry at Columbia University Irving Medical Center, where she blended statistical rigor with clinical insight.

💼 Professional Endeavors

Dr. Zhang is currently an Assistant Professor of Data Science at the University of Virginia, where she has been on the tenure-track faculty since August 2023. She also holds a concurrent position as a Faculty Member at the UVA Brain Institute, underscoring her active role in advancing brain research across institutional boundaries. Prior to her academic appointment at UVA, she served as a Research Scientist II at the New York State Psychiatric Institute, contributing to high-impact psychiatric research. Her professional journey also includes research assistantships at Tulane University and the University of Florida, roles in which she cultivated strong collaborative and translational research skills.

🧠 Contributions and Research Focus

Dr. Zhang’s research lies at the intersection of data science, neuroscience, and mental health. She specializes in developing advanced statistical and computational methodologies to investigate the biological underpinnings of psychiatric and neurodevelopmental disorders. Her work prominently features the use of graphical models—both directed and undirected—and machine learning techniques to analyze complex datasets, such as MRI, DTI, fMRI, MEG, and various genomic modalities including SNP and DNA methylation. Her research has contributed to a deeper understanding of conditions like schizophrenia, Alzheimer’s disease, obsessive-compulsive disorder, and anxiety disorders, through the lens of multimodal data fusion and integrative neurogenetics.

🧪 Innovation in Mental Health Data Science

A distinctive hallmark of Dr. Zhang’s scholarship is her innovative application of multimodal fusion techniques to disentangle the complexities of typical and atypical brain development. Her work leverages high-dimensional neuroimaging and genetic data to draw meaningful inferences about mental health trajectories. She is particularly focused on building interpretable models that bridge the gap between data and clinical insight, thereby enabling earlier and more precise diagnostics. By combining machine learning with biomedical expertise, her contributions pave the way for next-generation tools in psychiatry and neuroscience.

🏅 Accolades and Recognition

Throughout her academic and professional trajectory, Dr. Zhang has earned widespread respect for her analytical acumen and interdisciplinary collaborations. Her postdoctoral role at Columbia, a hub for clinical psychiatry and biostatistics, positioned her among leaders in the field and enriched her research portfolio with translational applications. Her selection as faculty at a leading institution like UVA further reflects recognition of her scholarly excellence and her potential to drive future innovations in mental health data science.

🌍 Impact and Influence

Dr. Zhang’s work has significant implications for both the scientific community and clinical practice. Her methods empower researchers and clinicians alike to draw meaningful patterns from multimodal datasets, thereby advancing precision psychiatry. Moreover, her collaborative efforts across biomedical engineering, statistics, and clinical disciplines have fostered integrative frameworks that extend beyond academic settings into real-world applications. Her contributions are helping to shape a more data-driven and personalized future in mental health care.

🔮 Legacy and Future Contributions

As she continues her academic journey, Dr. Zhang aims to expand her research frontiers by exploring dynamic brain-behavior associations and improving the interpretability of AI models in clinical contexts. With a commitment to mentorship and open science, she is building a legacy rooted in intellectual rigor, innovation, and societal relevance. Her future contributions are expected to not only deepen our understanding of mental health disorders but also inspire a new generation of data scientists dedicated to neuroscience and human well-being.

Publication

• Leverage multimodal neuro-imaging and genetics to identify causal relationship between structural and functional connectivity and ADHD with Mendelian randomization
  C Ji, S Lee, S Sequeira, J Jin, A Zhang — 2025

 

• Integrated brain connectivity analysis with fmri, dti, and smri powered by interpretable graph neural networks
  G Qu, Z Zhou, VD Calhoun, A Zhang, YP Wang — 2025

 

• Altered hierarchical rank in intrinsic neural time-scales in autism spectrum disorder
  A Solomon, W Yu, J Rasero, A Zhang — 2025

 

• A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
  Y Zhang, L Wang, KJ Su, A Zhang, H Zhu, X Liu, H Shen, VD Calhoun, … — 2025

 

• A Novel GNN Framework Integrating Neuroimaging and Behavioral Information to Understand Adolescent Psychiatric Disorders
  W Yu, G Qu, Y Kim, L Xu, A Zhang — 2025

 

• A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
  A Zhang, G Zhang, B Cai, TW Wilson, JM Stephen, VD Calhoun, YP Wang — 2024

 

• Exploring hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer’s disease
  A Zhang, K Wengler, X Zhu, G Horga, TE Goldberg, S Lee — 2024

 

• Time‐varying dynamic Bayesian network learning for an fMRI study of emotion processing
  L Sun, A Zhang, F Liang — 2024

 

• Altered hierarchical gradients of intrinsic neural timescales in mild cognitive impairment and Alzheimer’s disease
  A Zhang, K Wengler, X Zhu, G Horga, TE Goldberg, S Lee, … — 2024

 

• Associations Between Brain Connectivity and Psychiatric Symptoms in Children: Insights into Adolescent Mental Health
  D Mutu, K Ji, X He, S Lee, S Sequeira, A Zhang — 2024

 

🧾 Conclusion

Dr. Zhang’s journey exemplifies a seamless integration of data science and neuroscience to address pressing mental health challenges. Her innovative use of multimodal data and machine learning not only contributes to scientific advancement but also enhances real-world clinical decision-making. As she continues to pioneer research at the intersection of computation and psychiatry, her influence is poised to grow, shaping the future of precision mental health care and empowering both academia and clinical practice through data-driven insights.