ISE Ph.D. student Citina Liang.
A Ph.D. student in the Daniel J. Epstein Department of Industrial and Systems Engineering has been recognized for her critical work developing a disease simulation model to capture how syphilis and HIV are transmitted across Los Angeles County. Citina Liang works in the lab of associate professor Sze-chuan Suen, where she developed the microsimulation model to capture real-world disease patterns. Her work was honored at the recent Society for Medical Decision Making annual conference in Ann Arbor, Michigan. Liang was one of the Lee B. Lusted student award recipients at the conference, where she received the 2025 Milt Weinstein Award for Outstanding Presentation in Applied Health Economics.
USC Viterbi spoke with Liang about her research in the fascinating field of infectious disease modeling.
Tell us about your research in modeling syphilis and HIV interventions in LA County.
We collaborated with the Los Angeles County Department of Public Health and UCLA to develop a microsimulation model that captures real-world patterns of syphilis and HIV transmission. The model simulates individual-level disease trajectories and allows us to evaluate the impact of different public health interventions, especially emerging strategies like DoxyPEP, a promising new preventive medication for syphilis.
Our findings help quantify the trade-offs between program costs and health benefits, identifying which strategies avert the most infections per dollar spent. This enables decision-makers to allocate limited resources more efficiently, generating the greatest societal benefit with the available resources.
What excites or inspires you most about your area of research?
I’m most inspired by the interdisciplinary nature of this work. Building a model like this requires input from epidemiologists, clinicians, policy makers, and engineers. I enjoy learning from diverse perspectives and translating them into a rigorous mathematical framework that informs real-world decision-making. It’s especially rewarding to see that our models are of interest to local stakeholders and could help guide public health decision-making.
Beyond simulation modeling, I also work on projects involving data analytics, machine learning, and behavioral modeling, which allow me to apply technical tools to understand and address different health and policy challenges.
How did it feel to be recognized at the Society for Medical Decision Making conference?
It was an incredible honor to receive the Lee B. Lusted Award in Applied Health Economics at SMDM. The conference brings together researchers, clinicians, and policy makers from a wide range of disciplines. I especially enjoyed learning about the most pressing public health issues today and seeing the diverse approaches people are taking to address them. It was also deeply validating to know that my work resonated with experts and decision-makers in the field.
How did you decide to embark on a research degree at USC Viterbi? What drew you to the area of infectious disease modeling?
I’ve always been drawn to solving problems through data and models. Since undergrad, I’ve worked on research projects using mathematical and machine learning tools to uncover hidden patterns and improve decision-making. Infectious disease modeling stood out to me because of its immediate societal relevance. It addresses public health at scale, where even small improvements in policy can lead to significant impact.
I was especially inspired by my advisor, Professor Sze-chuan Suen, whose research sits at the intersection of systems engineering and health policy. Her mentorship has been instrumental in shaping how I approach both technical challenges and broader societal questions.
Are there any challenges you’ve faced so far while pursuing your research, and if so, how did you overcome them?
Definitely! Building a simulation model from scratch is never straightforward. There were many long nights spent debugging, refining assumptions, and aligning the model with real-world data. One of the biggest challenges was working with messy, incomplete healthcare data. Cleaning, validating, and making it usable for analysis required persistence and creative problem-solving.
These experiences strengthened my ability to break down complex issues and work collaboratively with domain experts. Each time a challenge was solved, it felt like a breakthrough and that steady sense of progress is what keeps me going.
What’s next for you?
After completing my Ph.D., I hope to continue doing applied research that drives data-informed decisions and real-world impact. I’m especially interested in roles where I can work across teams and use modeling, machine learning, and optimization to solve complex problems.
My experience with large, messy datasets and simulation frameworks has taught me how to turn ambiguous questions into scalable, data-driven solutions. I look forward to bringing that mindset to new challenges in industry or research.
Do you have any advice for students looking to pursue a research degree?
Pick a topic that genuinely excites you because curiosity is your best fuel. Research can be challenging and nonlinear, but those difficulties have helped me grow both technically and personally. It’s deeply fulfilling to work on meaningful problems and contribute tools or insights that can help address real-world challenges. And I always remind myself that it’s okay not to have all the answers at the start; we learn how to find them along the way.
Published on July 28th, 2025
Last updated on July 28th, 2025
