Yulin Hswen
Associate Professor
yhswen@umd.eduBiomolecular Building Office 3120B
Yulin Hswen, ScD, MPH, is the Distinguished AI Associate Professor for the School of Public Health within the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM aim.umd.edu). She holds appointments in the Department of Epidemiology and Biostatistics and in the College of Computer, Mathematical, and Natural Sciences at UMD. Her research advances social listening and digital intelligence to surface unheard voices and hard to measure the cold and warm realities by analyzing internet based data such as search queries, online forums, and social media. She develops multimodal AI methods to study human beliefs and behavior and to inform interventions and policy that can shift perspectives and norms to improve population health. (yulinhswen.com ; hswenlab.com)
Dr. Hswen leads the Hswen Lab (yulinhswen.com; hswenlab.com).
Department Information
- Department of Epidemiology and Biostatistics
Dr. Hswen earned a Doctoral Degree in social and computational epidemiology from Harvard University, where their work focused on integrating computational methods with population health research.
Dr. Hswen’s research centers on social and computational epidemiology, with an emphasis on identifying authentic attitudes, feelings, and beliefs that influence behaviors and shape population health. By collecting unconventional and “underground” data from online social networks, Dr. Hswen captures unfiltered conversations to uncover the complex connections between social experiences and health outcomes.
Dr. Hswen is also the Founder and Principal Investigator of Covidseeker.com, an innovative platform that captures geospatial-temporal data from smartphones to better understand the relationship between human mobility and infectious disease risk. Through this work, Dr. Hswen advances new methods for leveraging real-world digital data to inform public health interventions and policy.
Artificial intelligence and machine learning; Digital health and computational epidemiology; social media and social listening; Big data and epidemiological surveillance; Environmental health and population level monitoring; Behavioral economics and health behavior; Mathematics and computer science methods; Ethics and responsible AI in public health