Kai Zhang, Ph.D. is an assistant professor at the UTHealth Houston School of Biomedical Informatics (SBMI) and a member of the Center for Secure Artificial intelligence For hEalthcare (SAFE). Before this position, he was a postdoctoral research fellow at SBMI under the supervision of Dr. Xiaoqian Jiang. He received his PhD degree in Electrical Engineering from Texas A&M University, TX.
Dr. Zhang’s research interests are primarily in the intersection of machine learning and healthcare, and most of his work is strongly application driven. On the machine learning side, the areas Dr. Zhang being interested in currently include Gaussian processes, Bayesian neural network, methods for irregularly sampled time series, multimodal learning, survival analysis, joint modeling and methods trainable end-to-end, fairness in machine learning, reinforcement learning, and causality, especially their applications in the field of health data science via incorporating domain-specific knowledge of biological systems. He’s also interested in pragmatic questions surrounding the deployment of scalable and actionable machine learning models into the real world, for instance, scalability of causal structure learning algorithms, developing transparent, interpretable, and explainable AI tools to support patients and augment healthcare staff by offering superhuman diagnostic performance.
“AI has the ability to analyze big data sets – pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement.”
Angela M Wilkes