Dr. Yang is an Associate Professor of Quantitative Health Psychology in the Department of Psychology at the University of Rhode Island and co-director of the Behavioral Science Ph. D. program. Her research interests focus on the development and adaptation of quantitative methods and analysis tools (1) to handle missing data, especially for longitudinal studies and clinical trials, and (2) to analyze longitudinal data that assess changes and relationships in individual behaviors and experiences over time (including both panel data and intensive longitudinal data, e.g., ecological momentary assessments).
In addition, she conducts research to evaluate the psychometric properties of patient-reported outcome measures (e.g., for assessing fatigue and pain). Her methodological interests are largely motivated by empirical data analytic problems and measurement issues in health psychology. She is collaborating with scholars in intervention/prevention science, substance use, emotion regulation, and health behaviors. Dr. Yang is currently funded on a Mentored Research Scientist Development Award (K01DA058715) from the National Institutes of Health to improve statistical methods for handling missing data in substance use studies that use ecological momentary assessment.
QUESTIONS & ANSWERS
- Could you tell us about the focus of your research and what scientific questions you’re addressing?
My research centers on advancing quantitative and statistical methods to tackle key challenges in psychological, behavioral, and health-related studies. I focus particularly on developing and evaluating techniques for handling missing data—especially in longitudinal studies and clinical trials—and for analyzing longitudinal data to better understand how individual behaviors and experiences evolve over time. Substantively, I’m passionate about topics like intervention and prevention science, substance use, emotion regulation, and health behaviors, where rigorous methods can drive meaningful insights and impact. - How has access to URI’s research computing resources and team impacted your ability to pursue this work?
Access to URI’s research computing resources and expert support team has been instrumental in advancing my work. It enabled me to carry out large-scale, computationally intensive simulation studies that were essential for evaluating both established methods and the new approaches developed by my research team. Their infrastructure and guidance have significantly expanded the scope and rigor of what we’re able to accomplish. - Can you share a specific project or breakthrough where HPC, AI, quantum or data resources played a critical role?
HPC has been essential to several of my ongoing research projects. A standout example is my NIH-funded K01 award, where I’m developing a new Bayesian method for handling missing data in substance use research using ecological momentary assessments. The Bayesian methods, combined with intensively collected longitudinal data, demand significant computational power. Thanks to HPC, I can run large-scale simulation studies by distributing tasks across parallel jobs—dramatically accelerating results compared to what’s possible on a stand-alone local machine. - What kinds of challenges (computational, data-related, or otherwise) have you faced, and how has our team helped you overcome them?
When my research team began using HPC, we faced several technical hurdles—like compiling Bayesian analysis software on the cluster and integrating it with R, as well as optimizing SLURM parameters to run simulations more efficiently. The support team, especially Kevin Bryan, was incredibly helpful and responsive throughout the process. Kevin ensured a smooth and successful migration for us from the old cluster system, Andromeda, to the new system, Unity, and continues to provide prompt, day-to-day support whenever technical issues arise. - Have you or your team participated in any of our training programs, workshops, or consultations? If so, how did they contribute to your research?
The CAREERS CYBERTEAMS project assisted students within my lab with code optimization in R using Unity/HPC on an ongoing project. Collaboration with different mentors allowed students to develop robust coding and enhance research projects. - In what ways have collaboration opportunities through computing enhanced your group’s productivity or broadened your research opportunities?
All the graduate students in my lab are engaged in methodological research that relies heavily on simulation studies. HPC has significantly enhanced our ability to collaborate—making it easier to share data, exchange code, compare results, and support one another through debugging and troubleshooting. It has created a more connected and productive research environment, where collaboration is seamless and efficient. - Looking ahead, how do you see your research evolving, and what role do you anticipate research computing will play in that future?
Looking ahead, I’m excited to explore machine learning approaches to uncover the mechanisms behind psychological interventions and address persistent challenges like missing data. As my research continues to evolve, I anticipate that research computing will remain essential — powering more sophisticated analyses, accelerating simulations, and unlocking deeper insights. - What advice would you give to other faculty or students considering leveraging URI’s research computing resources?
I strongly encourage faculty and students to take advantage of URI’s research computing resources early in their projects. Whether you’re running large-scale simulations, processing complex data, or just beginning to explore computational methods, the available infrastructure and support can dramatically expand what’s possible. It’s common to encounter challenges at the start — but you’re not alone. URI’s research computing team is helpful and responsive, and they’ll work with you to get up and running. These resources are a true asset for boosting research productivity and fostering collaboration.
Written by Elizabeth Pauley
Ph.D. student in Psychology