Faculty Research Profiles

I am leading a diverse, interdisciplinary team who likes to tackle important questions in systems medicine by developing and using novel machine learning algorithms. Our ultimate goal is to identify risk factors and mechanisms affecting aging and contributing to the onset and progression of chronic diseases and cancer. We also develop predictive methods and tools that can directly improve health. We use probabilistic graphical models and other machine learning methods to integrate and mine high-dimensional, multi-modal biomedical data and to investigate biological processes pertinent to health and disease.

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Dr. Jinling Liu is an Assistant Professor in the Department of Epidemiology housed in both College of Public Health and Health Professions and College of Medicine at the University of Florida (UF). Before joining UF, she was a faculty member in the Department of Engineering Management and Systems Engineering (EMSE) and Department of Biological Sciences at Missouri University of Science and Technology (Missouri S&T). Dr. Liu holds a Ph.D degree in Biology from the Pennsylvania State University as well as an MS degree in Biomedical Informatics from the University of Pittsburgh. Dr. Liu also completed a National Library of Medicine Postdoctoral Fellowship in Biomedical Informatics. She has extensive experience in big data analytics, systems biology, probabilistic graphical models, causal inference and machine learning.

Dr. Liu’s research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a variety of statistical and machine learning techniques – one of her projects along these lines has been recently funded by a prestigious NIH Career Development Award for young investigators. One main theme of her research is to infer from the multi-omics data the activation states of signaling pathways and utilize such information in precision medicine. She is committed to advancing precision medicine and improving health equity through her work. She is also collaborating with researchers in other domains for data analytics and decision making.

Liu lab has access to rich high-dimensional multi-omics data, including WGS, WES, DNA methylation, RNA-seq, proteomics, lipidomics and metabolomics in large-scale human populations. The lab is interested in integrating and mining the different types of data to understand the genomic causes and other biomarkers as well as the underlying signaling network for a variety of diseases including aging, cardiovascular diseases, brain diseases, cancer, et al., through novel statistical or deep learning methods development and application. If you are interested in data analytics, artificial intelligence, genetic epidemiology or precision medicine, please see the above Liu Lab website on how to apply. Feel free to direct any questions you may have to Dr. Liu through jinling.liu@ufl.edu.

Dr. Lopez-Quintero’s research has focused on disentangling the role that factors at different levels of influence (e.g., biological, behavioral, socio-cultural, or political) play on the transitions from the early stages of drug use involvement to the development of drug use disorders and other behavioral and mental health outcomes. In her current research she aims to develop a comprehensive and interdisciplinary insight of the mechanisms that generate and sustain disparities in drug use transitions and trajectories by examining the complex interactions between drug use and neuropsychological processes, socio-cultural factors, and systemic level factors. Through her research Dr. Lopez-Quintero aims to contribute to inform the design of effective and developmentally appropriate drug use prevention and treatment interventions, as well as public health policy.

My research program is focused on exploring the interactions between diet and gut microbiota and their contributions to various aspects of human health. We are utilizing state of the art sequencing and bioinformatics approaches to establish potential utility and develop microbiota targeting interventions.

Dr. Malin’s research program focuses on examining impacts of environmental toxicants and nutrition on health outcomes. She is particularly interested in potential effects of fluoride and aluminum exposure on neurobehavioral and neuroendocrinological functioning. She has published a breath of studies on impacts of fluoride exposure on Attention-Deficit/Hyperactivity Disorders (ADHD) symptoms and diagnosis, sleep behaviors, thyroid gland activity, kidney and liver function, and women’s reproductive health. She currently has an active R00 research grant from NIH/NIEHS to examine early life fluoride exposure in relation to neurodevelopment and sleep patterns among children in the United States and Canada respectively. Lastly, Dr. Malin is also interested in health disparities in relation to environmental toxicant exposures.

Learn more about Dr. Malin’s lab: https://fabblab.phhp.ufl.edu

Computational epidemiology Bio-health informatics Artificial intelligence Viral – bacterial diseases

As a psychiatric epidemiologist, Dr. Striley conducts community-engaged research that aims to increase community members’ recognition of need for health screening, mental health and behavioral health services and to decrease barriers to those services. She has extensive training and experience in community engaged research, including recruitment and retention of diverse community members, and in drug use, abuse and misuse epidemiology. Dr. Striley is one of the few researchers who conducts research in caffeine and energy drink misuse. She is also researching the epidemiology of movement disorders including Tourette Disorder, Dystonia and Essential Tremor.

Dr. Vaddiparti’s current research endeavors encompass various areas. He is primarily focused on real-time monitoring of sleep quality, mood, and posttraumatic stress disorder symptoms among individuals using medical marijuana in Florida. Additionally, he is conducting secondary data analysis, with funding from the International Center for Responsible Gaming, delving into the topics of early and adolescent risk and protective factors for problem gambling. Furthermore, Dr. Vaddiparti presently serves as a co-principal investigator in several noteworthy projects, including:

Collaboration on a research study commissioned by the Florida Department of Health, which explores HIV/AIDS-related stigma and its impact on clinical outcomes and quality of life.

An NIH-funded initiative, Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV). This project places a special emphasis on combining causal inference and artificial intelligence methods, in conjunction with real-world multilevel data to improve anti-retroviral treatment outcomes and reduce disparity in the context of HIV.

Participation in a NIDA-funded study, “Health outcomes and cognitive effects of marijuana use among individuals living with HIV/AIDS,” as part of the MAPLE Supplement Study.

Dr. Vaddiparti’s research portfolio reflects a commitment to advancing knowledge and addressing critical issues in the field of public health.

Dr. Wang’s research interests focus on leveraging advanced technologies and methods (e.g., wearable sensors, ecological momentary assessment/EMA) to improve the understanding of etiology and outcomes of substance use (e.g., alcohol, medical marijuana) among vulnerable populations (e.g., people with HIV, breast cancer patients, patients with chronic pain).

Alcohol use and HIV; wearable biosensors; ecological momentary assessment; medical cannabis

**Dr. Westmoreland will be accepting one PhD student to begin Fall 2025**

Dr. Westmoreland (she/hers) is an Assistant Professor in the Department of Epidemiology at the University of Florida. Broadly, her research interests are in behavioral and sexually transmitted infections (STI) epidemiology. Much of Dr. Westmoreland’s research has focused on sexual behavior associated with HIV/STI risk and prevention including the role that social and structural contexts play in influencing sexual behavior. Her current research centers the intersection of alcohol/substance use and sexual health behavior culminating in her NIAAA funded K01 Mentored Research Scientist Development Award investigating the impacts of alcohol use and misuse on daily PrEP (pill form) adherence. Additionally, Dr. Westmoreland has research interests and experience in the use of technology to conduct research and disseminate interventions to hard-to-reach populations, in LGBTQ+ health disparities, in applied social network analysis, and in survey research methodology.

Prior to joining the Department at UF, Dr. Westmoreland was a Research Assistant Professor in the City University of New York’s Institute for Implementation Science in Population Health where she also completed her post-doctoral training. Dr. Westmoreland completed her doctorate in Epidemiology at the UCLA Fielding School of Public Health, her MSPH at the University of South Carolina, and her BS in Public Health Sciences at Clemson University.

Dr. Yaghjyan’s research focuses on molecular epidemiology of breast cancer, benign breast disease, and mammographic breast density. Her areas of interest include tissue and circulating markers, contributions of gut microbiome to breast health, and the role of environmental factors in breast carcinogenesis.

Our research lies at the interface of machine learning, genomics, and precision medicine. Our long-term goal is to build machine learning systems to assist scientific discovery, clinical decision making, and personal health management. The focus of our ongoing research is the development of machine learning algorithms (e.g., deep learning and probabilistic graphical models) which exploit massive genetic, multiomic, and clinical data to uncover the genomic basis of complex human diseases. Specifically, our work follows a variant-gene-pathway principle where we start from deep learning modeling of biological sequences (e.g., DNA and RNA) to predict functional effects of variants in different cellular processes (i.e., in silico mutagenesis; NAR 2016, Cell Systems 2017, Bioinformatics 2017). We then move on to a global modeling of genotype-phenotype mapping where we identify candidate risk genes (Neuron 2022, Cell Systems 2022) and predict phenotypes from personal genomes (Cell 2018). By leveraging cutting-edge techniques (e.g., deep learning and single-cell genomics), we are particularly interested in modeling the complexity (e.g., nonlinearity and cell-type-specificity) of the underlying biological system (Cell 2019).