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ACCEPTING PHD STUDENTS
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Associate Professor Boon Thye Thomas Yeo
status-symbol ACCEPTING PHD STUDENTS
Faculty & Department
Medicine
Joint Appointments

Associate Professor, Electrical and Computer Engineering, Design and Engineering

Associate Professor, Integrative Sciences and Engineering

Associate Professor, Electrical and Computer Engineering, College of Design and Engineering

Adjunct Faculty, Harvard Medical School

Education

Doctor of Philosophy of Engineering, Massachusetts Institute of Technology, United States

Bachelor of Science in Electrical Science & Engineering, Stanford University, United States

Bio

Thomas Yeo is an Associate Professor at the National University of Singapore. He received his B.S. and M.S. from Stanford University and Ph.D. from the Massachusetts Institute of Technology. Prior to NUS, Thomas was a research fellow at Harvard University and Duke-NUS Medical School. Thomas’s lab develops machine learning algorithms to generate scientific discoveries from population-level datasets with brain MRI, behavioral, genetic and other physiological measures. Insights from these population-level studies are in turn used to develop N-of-1 mental disorder treatment in individuals. Thomas was a recipient of the MICCAI Young Scientist Award (2007), the MICCAI Young Investigator Publication Impact Award (2011), the Singapore National Research Foundation Fellowship (2017), the Singapore Neuroscience Association Young Neuroscientist Award (2018) and the OHBM Early Career Investigator Award (2019). He is a Fellow of OHBM (2024) and sits on the scientific advisory board of the OHBM. He is a “highly cited researcher” (Clarivate Analytics) since 2019, an honor awarded to 0.1% of scientists worldwide.

Contact Information
email-iconthomas.yeo@nus.edu.sg
Lab Website
Lab GitHub

There is a deluge of data across scientific disciplines. Future scientific breakthroughs will rely on algorithms to explore these massive data. Our group develops machine learning algorithms to automatically generate scientific discoveries from population-level datasets with brain MRI, behavioral, genetic and other physiological measures. We are particularly interested in mapping brain networks in individuals and using brain network features to predict individual-level behavioral traits, mental disorder symptoms and disease progression. Insights from population-level studies are in turn used to develop N-of-1 mental disorder treatment in individuals. 

Machine Learning
Artificial Intelligence
Cognitive Neuroscience
Neuroscience
Neuropsychiatry
Computational psychiatry
Brain Imaging

Consultation Frequency

How often do you typically meet your PhD students one-on-one for consultation?

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Research Group Meetings

How often do you typically hold lab meetings where your PhD students present their research work to the class?

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Contact Information
email-iconthomas.yeo@nus.edu.sg
Lab Website
Lab GitHub