Meet Yafei Wang: Advancing the field of statistics through collaborative problem-solving
Brad Grier - 24 April 2025
Yafei Wang, Assistant Professor in the Department of Mathematical and Statistical Sciences, 天涯社区. Photo supplied.
Yafei Wang currently holds the position of assistant professor within the Department of Mathematical and Statistical Sciences at the 天涯社区. In this role, she contributes to the department's efforts to address the evolving challenges facing the field of statistics.
Wang's research interests lie at the intersection of statistics and optimization, with a particular focus on developing methodologies for analyzing complex and structured massive data. This work has important real-world applications, such as assisting with the early diagnosis of Alzheimer's disease through the analysis of neuroimaging data. Prior to joining the 天涯社区, Wang served as a lecturer (assistant professor) at the Department of Mathematical Sciences at the University of Essex, where she honed her skills in statistical research and effective communication.
Meet Yafei Wang.
Tell us about your current role and the work you are involved in.
I am currently an assistant professor at the Department of Mathematical and Statistical Sciences in the Faculty of Science at the 天涯社区. Previously, I was a lecturer (assistant professor) at the Department of Mathematical Sciences of the University of Essex.
My research interest lies in the intersection of statistics and optimization, focusing mainly on the methodology of complex and structured massive data analysis and theoretical foundations of stochastic optimization.
What led you to pursue a career in your field?
The main reason I chose statistics is because of its feasibility and wide applicability and it can solve real problems. In addition, it allows me to work with researchers from different fields, such as clinics, psychology, operations control and so on, which makes me never get bored.
Was there a particular moment or influence that sparked your interest?
The particular moment that sparked my interest in functional data analysis was when I realized that functional data analysis can be used to analyze real-world neuroimaging data of Alzheimer's disease. I believe that this research area can help with diagnosis of Alzheimer's disease in its early stages and help many patients and their families suffering from this disease.
How does your work or research contribute to the high-level goals of your department or unit?
Statistics today faces the challenge from data-rich and complex environments and pressure from data science and machine learning. This requires statistical methods being more scalable and adaptive while providing theoretical and performance guarantees, and statisticians to be more independent and strong in problem-solving. My current research area is in line with such new requirements and can help the department with the design of new courses and student training programs.
What do you find most rewarding about your job?
The most rewarding part of being a statistician is helping people make informed decisions, ranging from personal decisions to critical decisions made by governments, companies, etc. Specifically, it can help identify the problems, design and monitor progress, predict cause and effect, draw conclusions and design the decision.
Can you share a project or initiative you are particularly proud of? What was its impact?
One of my proudest works is “M-estimation for varying coefficient models with a functional response in a reproducing kernel Hilbert space”. This work has been accepted by Bernoulli. We propose an M-estimation framework for the varying-coefficient model with a functional response that encompasses both mean and quantile regression. What needs to be highlighted is that the proposed methodology can be used to quantify the regions related to Alzheimer's disease and, in particular, can help with diagnosis of Alzheimer's disease in its early stages.
What skills or experiences have been most valuable to you in your work?
I find that the most valuable skill is communication. When I was a student, I was mainly focused on developing theory and methodology, with less attention to communication. And I realized that communication is also important. Effective communication skills are not only aimed at communicating effectively with peers, but also with general audiences. On the one hand, as a statistician I need to be able to explain statistical concepts, problems, and motivations to colleagues. Effective communication skills can help to make this discussion smoother and more effective. On the other hand, when working with researchers with little or no expertise in statistics, I need to be able to explain the analyses, models and results in a way that is accessible to a general audience. Poor communication skills could lead to misunderstanding the requests, misinterpreting the results or, more importantly, misleading conclusions.
How do you stay current with advancements and new trends in your field?
One of the effective ways I find to stay with current advancements and new trends is by attending workshops and conferences in statistics and other relevant research areas. These can help me stay updated with what the currently emerging research areas and new developments are. Also, exploration of the funding opportunity can also help as it represents what problem faced and needed to be solved by the company, government, etc. Cooperation with researchers from other areas is also an important way, which can let me know what problems are most important and how we can play a role in helping solve the problem.
What is one thing that people would be surprised to know about you?
Quite a few of my colleagues know that I am good at planting. I was raised in a town, and I helped my family with planting vegetables, fruit, etc. when I was a child. Currently, I plant garlic, leeks and lettuce at home.