Jesper Sundell

Jesper Sundell is a Research Fellow at the Department of Automatic Control, Lund University, and a member of the ELLIIT initiative and the LTH Profile Area: Engineering Health. His work focuses on advancing pharmacometric modeling techniques through interdisciplinary approaches combining machine learning and computational methods. He holds a postdoctoral position with a research emphasis on automated covariate modeling and drug safety evaluation.

His research interests include developing innovative methods for pharmacokinetic modeling, symbolic regression applications in biomedical contexts, and integrating artificial intelligence into clinical pharmacology. He contributes to projects like the 'Learning pharmacometric model structures from data,' emphasizing data-driven model creation.

Jesper Sundell's recent studies address critical areas such as QT interval analysis for drug safety, mineralocorticoid receptor modulation effects, and the application of neural networks in covariate modeling frameworks. His work bridges computational methods with clinical applications, aiming to enhance drug development processes through advanced modeling techniques.

He is affiliated with key institutions including the Department of Automatic Control and collaborates within ELLIIT, focusing on IT and mobile communication advancements in health-related research. His research outputs reflect a commitment to both theoretical and applied aspects of pharmacometrics and systems biology.