Huilin Tang, PhD
Huilin Tang, PhD
Huilin Tang, PhD, is a postdoctoral researcher in the Department of Biostatistics, Epidemiology, and Informatics whose work focuses on advancing precision medicine for diabetes and neurodegenerative diseases. He integrates pharmacoepidemiology and data science, leveraging large-scale electronic health record and claims data to emulate clinical trials and apply causal inference and machine learning methods to evaluate the effectiveness and safety of glucose-lowering therapies. A central goal of his research is to identify optimal diabetes management strategies for older adults, particularly those living with Alzheimer’s disease and related dementias.
In this Q&A, Huilin shares how his early experiences as a clinical pharmacist shaped his research path, how his work addresses gaps between clinical trials and real-world care, what he values most about DBEI’s collaborative environment, and the projects and passions that inspire him both inside and outside of academia.
Can you tell us about your current research and what inspired you to pursue this area?
My research focuses on advancing precision medicine for diabetes and neurodegenerative diseases through the integration of pharmacoepidemiology and data science. At the University of Pennsylvania, I leverage large-scale electronic health record and claims data to emulate clinical trials and apply causal inference and machine learning methods to evaluate the effectiveness, safety, and heterogeneity of treatment effects of glucose-lowering therapies. A major goal of my current work is to identify optimal diabetes management strategies for older adults, particularly those living with Alzheimer’s disease and related dementias.
My inspiration for this research comes from my early experiences as a clinical pharmacist, where I frequently encountered patients struggling with complex treatment regimens and limited evidence to guide individualized care. These experiences motivated me to bridge clinical practice with data-driven research, to generate actionable, real-world evidence that can directly improve patient outcomes. By integrating clinical insight with advanced analytical methods, I aim to develop predictive tools and evidence that empower clinicians to deliver safer, more effective, and personalized diabetes care.
How does your research intersect with real-world challenges?
My research directly addresses the gap between clinical trial evidence and the realities of patient care. While randomized controlled trials provide critical insights, they often exclude older adults, patients with multiple chronic conditions, or those with cognitive impairment—groups that make up a large portion of the real-world diabetes population. By leveraging large-scale electronic health records and claims data, I emulate clinical trials in real-world settings to evaluate how diabetes treatments perform across diverse patient populations.
A key area of my work focuses on people living with both diabetes and Alzheimer’s disease or related dementias. These individuals face unique treatment challenges—such as higher risks of severe hypoglycemia, poor medication adherence, and fragmented care—that are rarely studied in traditional research. My projects use causal inference and machine learning methods to estimate treatment effects and develop predictive models that can identify those most at risk of adverse outcomes. Ultimately, my goal is to translate complex data into actionable insights that guide safer, more effective, and more personalized diabetes care in everyday clinical practice.
What do you find most rewarding about working in the Department of Biostatistics, Epidemiology, and Informatics?
What I find most rewarding about working in DBEI is the highly collaborative and intellectually stimulating environment that bridges data science, clinical medicine, and population health. The department brings together experts in biostatistics, epidemiology, informatics, and artificial intelligence, which creates an ideal setting for interdisciplinary research. I particularly value the opportunity to work with leading methodologists and clinicians who are passionate about translating data into meaningful health insights.
Being part of DBEI allows me to refine my quantitative skills while applying them to pressing clinical and public health problems—such as improving diabetes management in aging populations. The open exchange of ideas and the emphasis on methodological rigor continually inspire me to think creatively about how to use real-world data to inform precision medicine and improve patient outcomes.
Can you share a significant recent project, publication, or professional recognition that you are particularly proud of contributing to or achieving?
One recent project I am particularly proud of examined the association between newer glucose-lowering drugs, specifically GLP-1 receptor agonists and SGLT2 inhibitors, and the risk of Alzheimer’s disease and related dementias in people with type 2 diabetes. Using a target trial emulation approach with large-scale electronic health record data, our study provided real-world evidence supporting the potential neuroprotective effects of these therapies. The findings were published in JAMA Neurology in 2025 and have contributed to the growing discussion around drug repurposing for neurodegenerative disease prevention.
This work is meaningful to me because it bridges multiple disciplines—pharmacoepidemiology, causal inference, and neuroscience—and has important implications for improving the care of older adults with diabetes. It also represents the type of translational, data-driven research that I aspire to continue generating evidence that informs clinical practice and supports precision medicine.
What advice would you give to students or early-career professionals in your field?
My advice is to stay curious, collaborative, and persistent. The fields of pharmacoepidemiology and data science are evolving rapidly, and success often comes from being open to learning new methods and disciplines—whether it’s statistics, informatics, or clinical science. Don’t be afraid to step outside your comfort zone; some of the most rewarding research happens at the intersections between fields.
I would also emphasize the importance of mentorship and teamwork. Many of my most meaningful projects were the result of learning from mentors and collaborating across disciplines. Finally, remember that impactful research takes time and resilience. Stay focused on the bigger picture—how your work can ultimately improve patient care and public health.
What is an interest or pastime that you enjoy outside of academia?
Outside of research, I really enjoy outdoor sports like badminton and tennis. Staying active helps me clear my mind and often gives me space to think creatively about new ideas. And when I just want to unwind, I enjoy watching TV shows or documentaries—sometimes something lighthearted, other times something more thought-provoking, depending on my mood.