- Computational Systems Biology
- m6A and epitranscriptome functions
- cancer genomics and precision oncology
- KSHV biology
- Artificial Intelligence and deep learning
- Interpretable DL models for genomics
- Human reasoning and decision making
- Brain Computer Interface
- EEG-based prediction of cognitive states
- Passive BCI systems
Yufei Huang received his Ph.D. degree in electrical engineering from the State University of New York at Stony Brook in 2001. Since 2002, he has been with the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), where he is now Professor. He is also an adjunct professor at the Dept. of Population Health Science at the University of Texas Health San Antonio. He has been a visiting professor at the Center of Bioinformatics, Harvard Center for Neurodegeneration & Repair.
Dr. Huang’s expertise is in the areas of computational biology, brain-computer interface, machine learning, and artificial intelligence. One of the current focuses is to study the functions of mRNA methylation using machine learning and high throughput sequencing technologies, where his lab developed several widely used m6A data analysis pipelines. His lab also develops artificial intelligence systems for precision medicine and passive EEG-based brain-machine-interaction systems for an understanding of human cognitive behaviors. He was a recipient of the National Science Foundation (NSF) CAREER Award, UTSA Presidential Achievement Award on Research Excellence, Best Paper Award IEEE Biomedical and Health Informatics Conference, Best Paper Award of Artificial Neural Networks in Engineering Conference, and Best Paper Award of IEEE Signal Processing Magazine. He is a member of the UTSA Academy of Distinguished Researchers. His research has been supported by NSF, NIH, Air Force Office of Scientific Research, Army Research Lab, Department of Defense, and Qatar National Research Fund. He serves on IEEE Biomedical and Health Informatics Technical Committee and in the role of Associate Editor for multiple journals including IEEE Transactions on Signal Processing, IEEE Transactions on BHI, BMC Systems Biology and Neurocomputing.
To prospective students:
Research assistant positions are available for highly motivated students, who are committed to pursuing Ph.D. degrees in the area of computational biology and AI. Students must have strong machine learning, mathematics, statistics, and programming skills. Preference will be given to those with MS degree