From: 2025-03-27 10:30 to 12:00
Place: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH
Contact: kristian [dot] soltesz [at] control [dot] lth [dot] se

Date & Time: March 27, 10:30-12:00
Location: Seminar Room M 3170-73 at Dept. of Automatic Control, LTH
Speaker: Marzia Cescon
Title: Learning-enabled control methods for personalized health: the case of Type 1 Diabetes

Abstract:  The future of healthcare delivery will hinge on the personalization of therapies, moving progressively away from the one-size-fits-all approach to treatments tailored to the individual needs, medical history and genetic profile. When the therapeutic regime involves administering a drug, such personalization can be obtained with the use of data-enabled feedback control technology. One prime candidate for the effective development of personalized and adaptive treatment strategies by means of controls is Type 1 Diabetes (T1D). In this chronic disease, the auto-immune destruction of the beta-cells in the pancreas prevents the body from producing insulin, a hormone that is required for the glucose homeostasis feedback loop. As a consequence, patients diagnosed with T1D must rely on exogenous insulin for survival.

In our research group, we aim at artificially recreating the individual-specific glucose feedback loop using a combination of medical devices and learning-enabled control algorithms, to realize a fully automated insulin delivery system tailored to the patient. In this presentation, I will outline the challenges inherent to controlling physiological variables and will describe several learning-enabled control engineering algorithms that we are developing in-silico in our lab for closed-loop glucose control, to improve patients outcomes and quality of life.

Biography: Marzia Cescon is the David C. Zimmerman Assistant Professor of Mechanical Engineering at the University of Houston, TX. At UH she is also the founder and director of the Advanced Learning, Artificial Intelligence and Control laboratory, a multidisciplinary effort for learning-based decision making and control of complex and potentially unknown dynamical systems, and an affiliate of the UH Advanced Manufacturing Institute. The overarching theme of her research is the integration of learning and control with applications to personalized health and aerial, space and ground vehicle autonomy. Dr. Cescon earned a bachelor’s degree in information engineering and a master’s degree in Control Systems Engineering both from the University of Padua, Italy, and received the Ph.D. degree in Automatic Control from Lund University, Sweden. She has held several research positions including at the University of California, Santa Barbara, the Melbourne School of Engineering at the University of Melbourne, and the Harvard John A. Paulson School of Engineering and Applied Sciences at Harvard University. Dr. Cescon is the recipient of the NSF Career Award (2024).