Erjia Cui, PhD ’23, is a tenure-track assistant professor in the Division of Biostatistics and Health Data Science at the University of Minnesota.
In March 2024, Erjia co-published the book Functional Data Analysis with R, with Jeff Goldsmith, Andrew Leroux, and his thesis advisor Ciprian Crainiceanu. He has additionally published three papers in the Journal of Computational and Graphical Statistics as the first author.
Describe your current position and responsibilities in a way that will inform current and prospective students about career opportunities in biostatistics.
I am currently an assistant professor in Biostatistics at the University of Minnesota. The goal of my research is to develop novel and reproducible statistical methods such as Functional Data Analysis to help us better understand how physical activity and other biomarkers play a vital role in our daily lives. As modern wearable technology evolves, the data sets we can now collect are becoming more and more complicated. Therefore, the development of computationally feasible methods and corresponding software becomes increasingly important. In addition to my research, I teach graduate level courses for biostatistics students at my institution and provide guidance on their thesis research.
What has been your most satisfying job experience using your biostatistics background?
It is great to see that the functional data analysis methods I developed have been used by other scientists in different fields, including areas I am less familiar with. As mentioned above, I develop software to implement all my statistical methods. It's always rewarding to see people enjoying my software. Additionally, I enjoy sharing my knowledge of biostatistics with students in the classroom or after class and watching them grow into independent biostatisticians.
How did you get interested in the field of biostatistics?
I did my undergraduate training in mathematics and statistics. While they were both very interesting topics and I still love them, there seemed to be a gap between what I learned in class and what happened in real life. After gaining some research experience in biostatistics, I realized that this is a field that uses statistical knowledge to solve real-world problems, which is closely related to my career goals. All these factors led me to apply to graduate school in biostatistics.
The inclusive atmosphere of our department is awesome.
How did your degree in Biostatistics prepare you for your career? What aspects of the Hopkins program did you find most useful?
The PhD training in Biostatistics at Johns Hopkins is very comprehensive. As a PhD student, I spent most of my time doing research and learning different aspects of biostatistics. I started without any background in functional data analysis and learned most of what I knew in the field by reading papers and discussing with my advisors and friends. This helped me develop a deeper understanding of the field and helped me position myself, which I found very helpful when starting my career. Through being a teaching assistant in various classes at Hopkins, I learned how to teach students at different levels. Specifically, I like the PhD-level courses offered by Hopkins, which provide rigorous trainings in probability theory, statistical inference, and statistical methods. These trainings paved the way for my future research, and I found them to be most useful.
What are your favorite memories of your time at Johns Hopkins Biostatistics?
I enjoy discussing different projects and ideas with my advisor Ciprian Crainiceanu and other lab members. I feel like I always learn something from these great people and that is my favorite memory. I also enjoy playing table tennis with my friends at Genome Café where we had a lot of fun. Additionally, the inclusive atmosphere of our department is awesome.
What was your favorite thing about living in Baltimore?
My favorite thing in the Baltimore city is eating blue crabs with friends. Outside the city, Ellicott City has good Korean food.
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