Zehang “Richard” Li joined the Baskin School of Engineering fall quarter 2020. He is currently teaching Statistical Learning and High Dimensional Data Analysis, a course he proposed. Li is interested in statistical methods and tools to address scientific questions in demography, epidemiology, and public and global health. Before joining Baskin Engineering, he was a postdoctoral researcher working in the Department of Biostatistics at the Yale School of Public Health.
When did your interest in the field of statistics begin?
A little background, I did my undergraduate major in risk management science, which was under the Statistics Department but was more finance-based. That was my first introduction to the field of statistics, and I really enjoyed it. I then decided to pursue a Ph.D. in statistics at the University of Washington. It was during my Ph.D. program that I began the work I’m doing now which is using statistics to solve public and global health problems.
Tell me about your research.
A big part of my research deals with how I can use statistics to solve global and public health problems. I primarily focus on modeling “messy data” in public health. It is a common situation in many of the low- and middle-income countries where basic demographic information and health indicators have not been recorded properly. Usually there isn't accurate information on the number of births, deaths, and causes of death. I work on projects to estimate the fraction of people who’ve died due to different causes by extracting signals from messy survey data. I also work on survey methods to estimate mortality rates in small areas in developing countries.
What course are you teaching this quarter and what has your experience been like so far?
It’s been challenging being completely remote, but also rewarding getting the chance to have smaller class sizes and interact with the students more. The class I’m teaching this quarter is a new class I proposed. It’s Statistical Learning and High Dimensional Data Analysis. It looks at different types of statistical techniques that have been developed to analyze high-dimensional, large datasets for prediction, structure learning, and inference. The course connects the ideas and principles in the fields of statistics, machine learning, and data science.
What’s your favorite thing about teaching at the Baskin School of Engineering?
Definitely the students. They’re motivated and eager to learn new things. What fascinates me is the way students are able to make connections from the material covered in class to topics from their own research and work experiences.
Since fall quarter 2020 was your first quarter here at UCSC, what else would you like the UCSC community to know about you?
On a more research-related aspect, I would really like to be more involved in the UCSC community and make connections with other departments across Baskin Engineering, as well as other departments across the university. I work in an interdisciplinary area and there is a lot of room to collaborate with the broader research communities at UCSC.
On a more personal level, I really enjoy exploring the local Santa Cruz community, finding new coffee shops and bakeries in the area. I’m living in San Jose, so I’m slowly exploring more and more of Santa Cruz.