معرفی
Sara Algeri is an Associate Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. Her research bridges statistical theory and applications in astrophysics and medical sciences, supported by active funding from the National Science Foundation.
Her research expertise lies in developing statistically rigorous and computationally efficient methods for hypothesis testing, particularly in the presence of nuisance parameters and unknown backgrounds. Key areas include goodness-of-fit tests, sequential testing, and inference in high-dimensional or uncertain settings. Her work enables discoveries in physics, such as searches for dark matter and axions, while also contributing to public health through improved screening risk assessments.
The trends in her recent publications reveal a strong focus on solving inverse problems in high-energy astrophysics and particle physics with innovative statistical frameworks. She develops methods applicable to data from instruments like HAWC and axion haloscopes, often under high background noise. Simultaneously, she applies statistical rigor to clinical questions, such as surgical outcomes and screening test risks, demonstrating interdisciplinary impact.
Dr. Algeri is actively involved in collaborative research, as evidenced by her role as Co-Investigator on the NSF-funded project Computationally Tractable Inference for Multi-Messenger Astrophysics (2022–2025), which aims to develop scalable statistical tools for next-generation astrophysical data. While no formal students are listed, her collaborative publications suggest mentoring and advising activities. She has not received any explicitly mentioned scientific awards in the provided text.
Her work contributes to broader scientific goals, including the UN Sustainable Development Goals, by advancing methodological tools for scientific discovery and health improvement. She collaborates with researchers across institutions and disciplines, particularly in astrophysics and medical statistics.
