Graduate Spotlight: Tianyi Wang, PhD, MS, BS

BH WELL celebrates Dr. Tianyi Wang for completing her PhD in Statistics from the University of Kentucky. She successfully defended her dissertation, “Bayesian and Deep Learning Latent Class Methodology for Ecological Momentary Assessment and Imaging Data” on June 23, 2026. Throughout her time with BH WELL, Dr. Wang contributed to a wide range of research projects spanning behavioral health, tobacco treatment, psychiatric care, health services research, and large-scale Medicaid data analyses, bringing both statistical expertise and a deep commitment to improving health outcomes. Dr. Wang has grown as a researcher, collaborator, and mentor, developing a passion for understanding the people and stories behind the data. Her work with BH WELL has allowed her to apply advanced statistical methods to real-world challenges while supporting interdisciplinary teams across numerous projects. 

BH WELL Executive Director, Dr. Zim Okoli, shares, “Dr. Wang has been instrumental in elevating the nature of our methodological rigor in our funded BH WELL projects. I have always been encouraged by her positive attitude, willingness to take on challenges, and mentorship in developing other statisticians with BH WELL. We are excited to see her moving on to this next stage in her career and look forward to future collaborations with her as she assumes a new role. She is a phenomenal statistician and caring, compassionate, and endearing person.”   

 In the Q&A below, Dr. Wang reflects on her academic journey, the lessons she learned through research, and the impact the BH WELL team has had on her personal and professional growth. 

Why did you choose to pursue this degree? 

Dr. Wang: I earned my master’s degree in 2017 and worked in the pharmaceutical industry for over three years. When I was a student, I always wanted to start working because I thought studying was “boring,” and I felt that not everything I learned in school was directly useful for real-world applications. 

However, after I started working, I gradually realized that although I had opportunities to learn new statistical tools, apply models, and conduct analyses on smaller projects, most of the core statistical work and major decision-making were led by statisticians with Ph.D. degrees. I did not want to simply be the person who was told what to do, especially when the work sometimes felt repetitive, like doing the same tasks over and over again without much room for independent thinking. I wanted to be someone who could contribute my own ideas in decision-making meetings and engage more deeply in research, rather than just “working for” others. 

Therefore, I decided to leave my job and apply to a Ph.D. program. I wanted to gain the training and experience needed to conduct meaningful research with real-life impact, using the knowledge and skills I had learned. 

At the same time, I had my beloved baby girl, Lava. As a dog mom, I wanted to provide the best life for her. With my job at that time, I spent almost two and a half hours each day commuting between home and work, which left me with very little time to spend with her. Pursuing a higher degree gave me the opportunity to build a career that better aligned with both my passion for research and the life I wanted to share with Lava. 

What passions have you developed during your time at UK? 

Dr. Wang: During my time at UK, one of the greatest passions I developed was a passion for people. 

As someone trained in statistics, I used to think mainly about data, models, tests, and significant results. However, through my research experience, I learned that the most meaningful part of research is not only whether a statistical test is significant, but also the story behind the data and the reason we are asking the research question in the first place. Research is ultimately about people. Behind each observation, record, or data entry, there is a real person with their own story, experience, and background. They are not just numbers in a dataset. 

This understanding changed the way I think about statistics and research. I learned that understanding the population, context, and purpose of a project is not only important from an ethical perspective, but also important from a methodological perspective. When we understand why the research matters and who it is meant to serve, we can make better decisions about study design, model selection, variable definitions, and interpretation. Even when we refer to previous publications with similar aims, we still need to adapt the method based on the specific data, research question, and population we are studying. Doing something differently from prior work is not necessarily wrong if it better serves the purpose of the project. 

This has become one of the most important lessons I learned at UK and one of the core ideas behind how I approach research. As a statistician, I may not always be expected to know every detail of the subject-matter background at the beginning of a project. However, I believe it is always valuable to learn about the people, context, and real-world problems behind the data. For me, doing research in the right way means not only applying statistical methods correctly, but also understanding why the work matters and how it can serve people. 

What projects have you worked on while with BH WELL? 

Dr. Wang: I started working with BH WELL in 2020, and over the years, I have been fortunate to be involved in many different projects. Each project taught me something new, not only about statistics and data analysis, but also about research, clinical context, teamwork, and the people behind the data. 

My first project was the Nicotine Metabolite Ratio project. I still remember asking many questions about how nicotine metabolism works and why people with different nicotine metabolism rates may respond differently to smoking cessation treatment. This was the first project that helped me understand the importance of learning the non-statistical background of a research question. It taught me that before applying a model or conducting an analysis, I needed to understand the scientific reasoning behind the project. 

Later, I was very fortunate to join the Long Acting Injectable (LAI) meta-analysis project. This project taught me how to conduct a literature review, screen manuscripts, extract information from published papers, and understand more about medications and mental illness. One of the most interesting things I learned was that even when studies begin with a similar research question or assumption, the results can be very different across patient groups, settings, and locations. Sometimes, the findings may even appear to be opposite. This taught me that presenting results is only one part of research; the more important part is understanding the possible reasons behind those differences. 

At the same time, I also worked on the LAI project involving medical record data entry for Eastern State Hospital. This was the first time I entered medical record data myself, and it was one of the most challenging projects for me at that time, especially because I did not have a clinical background in mental illness or psychiatric medications. As a non-native English speaker, remembering medication names and assigning them to the correct diagnosis categories was especially difficult. I also learned that medications do not always belong to only one category; some may serve multiple purposes, which required me to bring questions to clinical professionals. Although human error can still happen during manual data entry, this experience helped me understand the complexity of clinical data. 

As my coding skills improved, I was later able to use programming to categorize the data much more efficiently, so people no longer needed to manually separate everything by diagnosis. This saved a significant amount of time for later stages of the project. I also redesigned the data entry tables by removing variables that could be derived from other variables, which helped me learn how to design better data collection structures and avoid unnecessary redundancy. 

The comic book project was another special project for me. When I first saw the comics, I did not realize that research could be conducted in such a creative and accessible way. Later, we developed the idea of translating the comic into multiple languages so that people from different backgrounds could read and benefit from them. This project taught me about academic translation, how translation works in research, and how different languages can be used to express the same ideas while still preserving the meaning and tone of the original work. We also later worked on the comic survey project, which taught me interesting and practical skills, such as how to detect robot responses and distinguish them from real human responses. That was a unique and valuable experience for me. 

The Medicaid projects are the projects I spent the most time on, and they involved the largest dataset I had ever worked with. To make sure the project aims were achieved, we had frequent meetings with the Medicaid team. With their support and guidance, we identified many important findings and developed new research questions. I even learned SQL programming through working on these projects. There were so many ideas and potential aims that came from this work, and I hope I will still have opportunities in the future to explore some of the questions we were not able to complete because of the project timeline. 

There are many other projects I have not listed here, such as the children’s readmission project, the lung cancer project, and other survey projects. Each of them taught me something different and helped me grow as a researcher. I cannot fully express how grateful I am to have had the opportunity to work on these projects and to learn from the people involved in them. 

Which project was your favorite to work on? 

Dr. Wang: My favorite projects have been the Medicaid projects. These projects taught me how to communicate and collaborate with research team members both within and outside of BH WELL. They also gave me the opportunity to work with large-scale data and face many important challenges, including outcome derivation, algorithm development, statistical analysis with large samples, and how to present results using appropriate and audience-friendly figures and tables. Most importantly, these projects taught me to think carefully about why we choose a certain analytic approach and how that approach serves the research question. 

With the support of the BH WELL team, especially the mentorship of Dr. Okoli, I learned how valuable thoughtful discussion can be throughout the research process. Although we encountered many challenges, our meetings often became opportunities for brainstorming, problem-solving, and developing high-quality research ideas. I have always felt that if a meeting could last forever, Dr. Okoli and I would continue discussing the data, the research questions, and the new ideas that kept coming to mind. 

The Medicaid database is very large and has so much potential. During our discussions, we identified many questions and directions that we wanted to explore, but we did not always have the time to pursue them because of the project aims and the need to write results. These projects were challenging, but they were also extremely meaningful. They helped me grow as a researcher and strengthened my passion for using data to answer important real-world questions. 

What has being part of the BH WELL team meant to you? 

Dr. Wang: The BH WELL team means so much to me. They are not simply colleagues; they feel more like friends and family. 

During my Ph.D. program, I faced not only academic challenges, but also personal and family difficulties that made me feel helpless in ways I had never experienced before. I have never been very good at sharing my feelings with others. I did not want the people around me to become an emotional dumping ground for my struggles, and I was always taught not to bring negative energy to others. However, through my experience working on behavioral health projects, I learned the importance of seeking help when it is needed. That understanding, along with the fact that Heather and Dr. Okoli noticed the changes in me and offered thoughtful words, support, and help when I needed it most, helped me get through some of the hardest moments in my life. 

Over the years, working with the BH WELL team and being mentored by Dr. Okoli has taught me so much. Their guidance and support are also part of the reason I was able to grow professionally and receive many interview opportunities, even during some of the most difficult times. The support I have received from the BH WELL team is more than I can fully express in words. 

This team includes people from different races, genders, cultures, and backgrounds, but to me, they all feel like family. I am deeply grateful to have been part of this team. 

What’s next for you? 

Dr. Wang: I will be joining Nemours Children’s Health in Jacksonville, Florida, as a Biostatistician. My work will focus on designing epidemiological and basic research studies, writing statistical sections for research proposals, supporting and serving as a co-investigator on funding applications, and preparing statistical analysis results for study reports and manuscripts. These responsibilities will allow me to be involved in multiple stages of the research process, from developing study designs and research proposals to analyzing data and communicating findings. 

Beyond my direct role, Nemours has strong collaborations with many research institutions and health systems, including Mayo Clinic, Baptist Health, and the National Cancer Institute. These collaborations span multicenter trials and cross-institutional data studies, with the goal of accelerating discovery, deepening scientific understanding, and directly informing improvements in pediatric care. Being part of this research environment will give me many opportunities to collaborate with researchers and clinicians across a broad range of pediatric specialties and research areas. 

My long-term goal is to support medical professionals in developing and answering meaningful research questions, while also continuing to develop my own statistical methodology in computational pathology and Bayesian frameworks. I am especially interested in using histology images to support more accurate downstream analyses, such as survival prediction, molecular subtype classification, and gene enrichment analysis. In the long term, I hope this type of work can help create more accessible and informative tools for patients, including those facing financial burdens, by making better use of existing clinical and pathology data. 

__ 

The BH WELL team congratulates Dr. Wang on earning her PhD and thanks her for the many contributions she has made during her time on our team. We wish her all the best as she begins her career at Nemours Children’s Health and look forward to seeing the impact of her work in the years ahead.