Welcome to my personal website!
I received my Ph.D. in Biostatistics from the University of Michigan in 2025, under the supervision of Professor Jian Kang. My dissertation, Bayesian Machine Learning Methods for Brain-Computer Interface Applications, focuses on developing computationally efficient Bayesian learning and sequential decision-making methods for non-invasive EEG-based brain-computer interfaces (BCIs).
Research Interests
- AI for Science (AI4S)
- Bayesian Modeling
- Reinforcement Learning and Sequential Decision-Making
- Statistical and Machine Learning for Neuroimaging and Biomedical Data
- Brain-Computer Interfaces
Education
University of Michigan | Ann Arbor, MI
Ph.D. in Biostatistics | Sep.2021 — Apr.2025
University of Michigan | Ann Arbor, MI
M.S. in Biostatistics (Health Data Science Concentration) | Sep.2019 — May.2021
Accomplishments
- JSM 2023 Student Paper Award (Biometrics Section) Link
Publications
Zhao, B., Wang, Y., Huggins, J. E., & Kang, J. (2026). A Bayesian reinforcement learning framework for optimizing the BCI-utility of P300 brain-computer interfaces. The Annals of Applied Statistics, 20(1), 744–763. doi:10.1214/25-AOAS2080
Ma, G., Zhao, B., Abu-Amara, H., & Kang, J. (2026). Bayesian image-on-image regression via deep kernel learning based Gaussian processes. The Annals of Applied Statistics, 20(1), 536–559. doi:10.1214/25-AOAS2108
Lin, Z., Choi, J., Mao, R., Zhao, B., & Kang, J. (2026). Spatial adaptive selection using binary conditional autoregressive model with application to brain-computer interface. Journal of Computational and Graphical Statistics, 35(1), 1–12. doi:10.1080/10618600.2025.2495256
Zhao, B., Huggins, J. E., & Kang, J. (2025). Bayesian inference on brain-computer interfaces via GLASS. Journal of the American Statistical Association, 120(552), 2028–2039. doi:10.1080/01621459.2025.2498088
Murdock, B. J., Park, J., Jang, D.-G., Zhao, B., Teener, S. J., Webber-Davis, I. F., Zhao, L., Feldman, E. L., & Goutman, S. A. (2025). In vitro modeling of natural killer cell cytotoxicity to inform personalized ALS therapeutics. Annals of Clinical and Translational Neurology, 12(10), 2036–2044. doi:10.1002/acn3.70127
Murdock, B. J., Zhao, B., Webber-Davis, I. F., Teener, S. J., Pawlowski, K. D., Famie, J. P., Piecuch, C. E., Jang, D. G., Feldman, E. L., Zhao, L., & Goutman, S. A. (2025). Early immune system changes in amyotrophic lateral sclerosis correlate with later disease progression. Med, 6(8), 100673. doi:10.1016/j.medj.2025.100673
Murdock, B. J., Zhao, B., Pawlowski, K. D., Famie, J. P., Piecuch, C. E., Webber-Davis, I. F., Teener, S. J., Feldman, E. L., Zhao, L., & Goutman, S. A. (2024). Peripheral immune profiles predict ALS progression in an age- and sex-dependent manner. Neurology Neuroimmunology & Neuroinflammation, 11(3), e200241. doi:10.1212/NXI.0000000000200241
Zhao, B., Zhong, Y., Kang, J., & Zhao, L. (2023). Bayesian learning of Covid-19 vaccine safety while incorporating adverse events ontology. The Annals of Applied Statistics, 17(4), 2887–2902. doi:10.1214/23-AOAS1743
Zhao, B., & Zhao, L. (2023). Mining adverse events in large frequency tables with ontology, with an application to the vaccine adverse event reporting system. Statistics in Medicine, 42(10), 1512–1524. doi:10.1002/sim.9684
Murdock, B., Zhao, B., Raue, K., Famie, J., Piecuch, C., Feldman, E. L., Zhao, L., & Goutman, S. (2023). Change in peripheral immunity precede amyotrophic lateral sclerosis progression. Journal of the Neurological Sciences, 455, 121711. doi:10.1016/j.jns.2023.121711
Zhou, Y., Wang, L., Zhang, L., Shi, L., Yang, K., He, J., Zhao, B., Overton, W., Purkayastha, S., & Song, P. (2020). A spatiotemporal epidemiological prediction model to inform county-level COVID-19 risk in the United States. Harvard Data Science Review, Special Issue 1. doi:10.1162/99608f92.79e1f45e