Research Interests
Keywords: real world data, machine learning, artificial intelligence, bioinformatics, model interpretability, genomics, transcriptomics
My current research focuses on methods development for health data analysis, integrating statistical, machine learning, and AI techniques, as well as real-world health data and curated biomedical knowledge. My work emphasizes and explores model interpretability, scalability, and data-driven discovery. Health and biomedical data in general are large, high-dimensional, and multi-modal, providing many opportunities for advancement, from basic scientific understanding to improved care.
In addition to research, I am passionate about education and mentorship. I’ve developed and taught courses, workshops, tools, and textbooks that combine theory with practical skills in programming and data analysis. These efforts aim to prepare both students and professionals to utilize computational resources proficiently, enhancing their ability to contribute to scientific discoveries and applications.
