Student Spotlight: Wenqing (Cathy) Zhang
Wenqing (Cathy) Zhang is a second-year ScM student in the Department of Biostatistics, with a focus on data integration for clinical evidence generation.
Wenqing (Cathy) Zhang is a second-year ScM student in the Department of Biostatistics, with a focus on data integration for clinical evidence generation.
After graduation Cathy hopes to continue toward a PhD to further explore methodological questions motivated by real-world data challenges, particularly how to make better use of existing data to generate stronger, more actionable clinical evidence.
What led you to Hopkins and choosing to study biostatistics?
My interest in biostatistics began while assisting with clinical trials during my undergraduate studies, under the supervision of Xiangrong Kong. I observed that while extensive patient data were collected, analyses often relied on only a narrow subset of variables. I realized that these rich datasets could answer many additional questions if we had better statistical methods to harness them. I became motivated to pursue a master’s in biostatistics to study and develop methods that better integrate available data across endpoints and studies to strengthen clinical evidence and eventually improve population health and well-being.
Having taken courses in the Biostatistics Department during my undergraduate years, I've experienced firsthand the rigorous training and collaborative culture here, and I believe this program provides exactly the environment I need to transform my observations into innovative statistical solutions.
Tell us about a project you are currently working on that you are excited about.
One project I’m very excited about aims to improve clinical risk prediction without requiring expensive biomarker collection from everyone. Under Chen Hu’s supervision, I’m developing a method that reliably borrows information from an ongoing trial to sharpen predictions in a completed trial with known outcomes.
Another exciting project I’m currently doing is generating evidence on how large language models (LLM) can help reduce manual effort in systematic reviews. Under the guidance of Yiqun Chen and Elizabeth Stuart, I benchmarked LLM performance across a range of extraction tasks, from clearly defined checklist items to more complex, context-dependent criteria. Our findings are currently available as a preprint.
What has been your favorite class so far?
Survival Analysis with Yuxin Zhu has been one of my favorite classes. It combines statistical rigor with clinical relevance, covering everything from foundational concepts like the hazard function to advanced methods like competing risks models, all of which are directly applicable to the clinical research I’m passionate about.
Tell us about an experience being involved in a Biostatistics Department Working Group?
I’m actively involved in the SLAM (Survival, Longitudinal And Multivariate data) working group, which has been incredibly rewarding. The group connects highly engaged students and faculty across institutions who share interests in survival analysis, longitudinal data, clinical trials, and time series. With two to three meetings per month featuring research presentations from both internal and external speakers, it offers excellent opportunities to explore diverse methodological approaches and collaborate with researchers facing similar statistical challenges.
Have you had any internships or jobs that have been helpful in your biostatistics learning journey?
As a part-time biostatistician at the Johns Hopkins Wilmer Eye Institute, I serve as the primary statistical consultant on more than 10 projects, analyzing correlated eye data and collaborating with clinicians to translate clinical questions into rigorous statistical analyses. This experience has strengthened both my technical skills in handling complex data structures and my ability to communicate statistical concepts effectively to clinical researchers.
What advice do you have for prospective students interested in the Department of Biostatistics?
Biostatistics can feel challenging at first, and it is okay to be confused. What helps the most is asking questions early and often. You’ll be joining one of the most supportive communities I’ve experienced, and faculty, staff, and fellow students are always willing to help. Don’t hesitate to reach out when you are struggling, because that openness is exactly what makes our community so strong.
