Haidong Lu, PhD
Assistant Professor of Medicine (General Medicine) and Epidemiology (Chronic Diseases)
Dr. Haidong Lu is an epidemiologist and methodologist who is passionate about bridging the disciplines of epidemiology, statistics, data science, population health, and clinical research. His research focuses on leveraging appropriate health data science techniques to draw causal inferences from observational data (e.g., electronic health records). Through this approach, he strives to generate high-quality, impactful real-world evidence that informs clinical and regulatory decision-making. In addition to observational studies, he is dedicated to enhancing methodologies for analyzing randomized controlled trials, particularly in addressing challenges related to non-compliance and generalizability. Dr. Lu is also interested in artificial intelligence in learning health systems. Dr. Lu’s current research lies at the intersection of pharmacoepidemiology, data science, and substance use, supported by a K99/R00 Pathway to Independence Award from the National Institute on Drug Abuse (NIDA). He co-leads the Yale Pharmacoepidemiology Working Group, and serves as an Associate Editor for Epidemiologic Methods and AJE Advances: Research in Epidemiology, and as a Statistical Editor for the Journal of Addiction Medicine.
What do you do with Data Science?
I am an epidemiologist and methodologist working at the intersection of epidemiology, statistics, and data science. My research focuses on leveraging advanced data science techniques to draw causal inferences from observational data, such as electronic health records, in order to generate high-quality real-world evidence that informs clinical and regulatory decision-making. I also develop and apply innovative methods for randomized controlled trials, particularly to address challenges related to non-compliance and generalizability. Currently, my work is supported by a K99/R00 Pathway to Independence Award from the National Institute on Drug Abuse, titled "Evaluating and Optimizing Care for Opioid Use Disorder using a Structured Data-Science Approach". Looking ahead, I am particularly interested in integrating artificial intelligence into learning health systems to improve population health. I have published in journals such as Epidemiology, the American Journal of Epidemiology, and the International Journal of Epidemiology, and I also serve as Associate Editor for Epidemiologic Methods and AJE Advances: Research in Epidemiology, and as Statistical Editor for the Journal of Addiction Medicine.