Postdoctoral Fellow, Medical College of Wisconsin, United States
Dr. Dayeong An is a Postdoctoral Fellow in the Department of Radiology at Northwestern University. With a multidisciplinary academic background spanning Biomedical Engineering, computational sciences, and statistics, she specializes in applying machine learning and probabilistic modeling to multimodal biomedical data. Dr. An’s research bridges clinical needs and computational innovation, focusing on neurovascular outcomes, stroke mechanisms, translational AI, and advanced image processing. Her career spans multiple institutions including the Medical College of Wisconsin, Marquette University, and Minnesota State University. She has developed sophisticated deep learning frameworks for MRI and DSA image enhancement, and predictive models for stroke recurrence and cardiovascular risks. Dr. An has received numerous awards, including poster competition wins and international travel scholarships. Her publications reflect a strong focus on early cardiotoxicity detection using advanced imaging and machine learning techniques. She is a forward-thinking researcher committed to precision medicine and AI-driven clinical advancements.
Professional Profile
ORCID Profile
🎓 Education
Dr. Dayeong An holds a Ph.D. in Biomedical Engineering from the Medical College of Wisconsin, completed in February 2024. She earned her M.S. in Computational Sciences from Marquette University in July 2018 and an M.S. in Mathematics and Statistics from Minnesota State University, Mankato in July 2014. She also completed a B.S. in Mathematics with a minor in Economics at Minnesota State University in July 2012. Her education demonstrates a progressive trajectory toward integrating mathematical modeling, statistics, and machine learning for biomedical applications. This interdisciplinary foundation has enabled her to contribute significantly to biomedical image analysis, computational modeling, and clinical decision support systems. Through her academic journey, Dr. An developed expertise in machine learning, data integration, and quantitative image analysis, laying a strong groundwork for her current and future research in Biomedical Engineering and translational medicine.
💼 Experience
Dr. An is currently a Postdoctoral Researcher at Northwestern University (March 2024–Present), where she develops advanced machine learning frameworks for perfusion MRI and conducts stroke outcome analysis using national datasets. She previously served as a Research Assistant at the Medical College of Wisconsin (2019–2024), working on deep learning-based myocardial strain analysis and MR image enhancement. Earlier roles include Teaching and Research Assistant at Marquette University (2015–2020), where she supported courses in statistics, calculus, and computational labs. She also held adjunct teaching roles at Globe University and South Central College in Minnesota and taught international students at Minnesota State University. Across these roles, she combined research and teaching in quantitative analysis, medical imaging, and neural networks, culminating in a robust professional background in both academia and applied biomedical research.
🏆 Awards and Honors
Dr. An has received several prestigious awards recognizing her excellence in research and academic achievement. In 2023, she was awarded travel grants by the Graduate Student Association and Kayoko Ishizuka Award from the Medical College of Wisconsin. She also received research travel support from Marquette University and won poster competitions for her innovative work on myocardial strain analysis using deep learning. In 2022, she was a winner at the Annual Research Day at the Medical College of Wisconsin for her work on myocardial displacement fields using image-to-image translation networks. Internationally, she earned scholarships for the Global Cardio Oncology Summit (Madrid, 2023) and the ISMRM Annual Meeting (Toronto, 2023). Her early recognitions include the Grad Cohort Workshop for Women scholarship (2018). These accolades reflect her impactful contributions to the biomedical engineering field, especially in AI applications for clinical imaging and precision medicine.
🔍 Research Focus
Dr. An’s research centers on leveraging machine learning and probabilistic modeling to interpret and integrate multimodal biomedical data. Her work focuses on neurovascular outcome prediction, stroke mechanism classification, and cardiovascular risk assessment using perfusion MRI, 4D flow imaging, and computational fluid dynamics. She actively develops transformer-based and GAN-enhanced models to refine imaging quality and interpretability, particularly in digital subtraction angiography (DSA) and cardiac MRI. A major thrust of her research lies in creating translational AI tools that enable precision medicine, supported by real-world clinical datasets like the NVQI-QOD. Dr. An also contributes to meta-analyses of stroke mechanisms and predictive modeling for recurrent ischemic events. Her cross-disciplinary approach combines statistical learning, biomedical engineering, and clinical collaboration to enhance patient-specific diagnostics and treatment planning. She is particularly driven by the potential of AI to bridge gaps between medical imaging, big data, and individualized care.
Publication Top Notes
1. Radiation-Induced Cardiotoxicity in Hypertensive Salt-Sensitive Rats: A Feasibility Study
📅 Life, 2025-05-27
🔗 DOI: 10.3390/life15060862
Authors: Dayeong An, Alison Kriegel, Suresh Kumar, Heather Himburg, Brian Fish, Slade Klawikowski, Daniel Rowe, Marek Lenarczyk, John Baker, El-Sayed Ibrahim
Summary: This study explores the feasibility of detecting early radiation-induced cardiotoxicity in hypertensive, salt-sensitive rats. The research integrates cardiac MRI with biological data to identify early imaging biomarkers, demonstrating the viability of using preclinical genetic models for cardiotoxicity prediction.
2. Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI
📅 Journal of Imaging, 2024-12-01
🔗 DOI: 10.3390/jimaging10120308
Authors: Dayeong An, El-Sayed Ibrahim
Summary: This paper presents a novel machine learning framework integrating cardiac MRI to identify early markers of radiation-induced cardiotoxicity in preclinical models. The study highlights the promise of AI in enhancing the diagnostic sensitivity of cardiac imaging, offering a path forward for precision cardiology.
Conclusion
Dr. Dayeong An is a strong candidate for a Best Researcher Award, especially in the fields of AI-driven biomedical imaging, precision medicine, and translational neuroscience. Her innovative contributions, interdisciplinary expertise, and recognition through multiple awards distinguish her as a rising star in clinical AI research. With more publications and leadership in funded projects, she is poised to become a leading figure in the field.