"What we fail to observe is not always absent—it may simply exist where we have not yet learned to look."
The Hswen Lab
If a tree falls in the forest does it make a sound?
Beyond traditional data, beyond traditional discovery.
Finding the evidence others never collected.
Because what isn't measured doesn't exist.
Boom...Crash...!
In further detail
"Discovery begins where conventional evidence ends." Yulin Hswen
Mission.
The Hswen Lab uncovers the health stories that traditional research overlooks. We develop artificial intelligence and computational methods to discover hidden patterns across clinical records, social media, medical imaging, and other emerging data sources—revealing populations, symptoms, and behaviors that remain invisible to conventional research. We believe that the absence of evidence is often the result of where science has looked, not whether something exists.
"The limits of science are often the limits of where we choose to look." Yulin Hswen
Research.
Scientific knowledge is shaped not only by what we study, but by what we choose to measure, fund, and collect. The Hswen Lab develops AI-driven approaches to identify health signals that fall outside traditional research systems, combining digital data, clinical records, imaging, and population health to reveal what has remained hidden. We believe innovation comes from looking where others have not.
About
Yulin Hswen, ScD, MPH, BS
"The absence of evidence often reflects the limits of data we analzye, not the limits of what exists."
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Dr. Yulin Hswen is an Associate Professor at the University of Maryland in the Department of Epidemiology and Biostatistics, the College of Computer, Mathematical, and Natural Sciences and the Artificial Intelligence Interdisciplinary Institute. She earned her doctorate in Computational Epidemiology from Harvard University.
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Dr. Hswen's research is driven by a central question: What are we missing? She believes that scientific knowledge is shaped not only by what we observe, but by where we choose to look. Traditional research relies on data that are routinely collected, funded, and measured, yet many important aspects of human health exist beyond these conventional sources. Her work develops artificial intelligence and computational methods to uncover overlooked signals across clinical records, medical imaging, online communities, social media, and other emerging forms of data, expanding the evidence available to understand population health.
Her research examines how information, narratives, and social influence spread through digital ecosystems and how these processes shape health behaviors, decision making, and public discourse. She studies both the opportunities and risks of AI-enabled information environments, including algorithmic amplification, social engineering, and the growing reliance on intelligent systems in everyday life. By integrating epidemiology, artificial intelligence, and behavioral science, her work seeks to understand not only how information spreads, but how it changes what people believe, feel, and ultimately do.
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Methodologically, Dr. Hswen develops and applies large-scale artificial intelligence, multimodal machine learning, natural language processing, computer vision, and generative AI to analyze language, images, behavior, and human interaction across diverse data sources. Her research spans public health surveillance, clinical decision support, digital epidemiology, and responsible AI, with applications ranging from emerging infectious diseases and chronic disease to online health communities, illicit markets, and health information.
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Across these diverse domains, her research is unified by a single principle: the absence of evidence should never be mistaken for evidence of absence. By expanding where science looks for evidence, Dr. Hswen's work reveals hidden patterns, amplifies underrepresented experiences, and advances more responsible, equitable, and human-centered artificial intelligence for medicine and public health.
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Dr. Hswen is also an Associate Editor at JAMA and JAMA+ AI, where she curates and oversees scholarship at the intersection of artificial intelligence, medicine, and population health. She also hosts JAMA+ AI Conversations, translating emerging AI research for clinicians, researchers, and the public.
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Personal interests: Collectionneur de montres, 18th-19th century French and Italian Art, and Crystal.
Activities: Strategic surprises, extraordinary excursions, finessing forums, wandering walks, drawn-out dinners, and lingering laydowns.
Where to find her: In solitude. Walking. Cast iron bathtub. Eating blue steak.
Previous life: Actress - Commercial, Movies, Television https://www.imdb.com/name/nm1840526/
Languages: English (Written: British), French (Fluent), Punjabi
