Frank Doyle is the John A. Paulson Dean of the Paulson School of Engineering and Applied Sciences at Harvard University, where he also is the John A. & Elizabeth S. Armstrong Professor. Prior to that he was the Mellichamp Professor at UC Santa Barbara, where he was the Chair of the Department of Chemical Engineering, the Director of the UCSB/MIT/Caltech Institute for Collaborative Biotechnologies, and the Associate Dean for Research in the College of Engineering. He received a B.S.E. degree from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. He has been recognized as a Fellow of multiple professional organizations including: IEEE, IFAC, AIMBE, AIChE and the AAAS. He was the President for the IEEE Control Systems Society in 2015 and was the Vice President and Chair of the Technical Board for the International Federation of Automatic Control from 2014 to 2017. In 2015 he received the Control Engineering Practice Award from AACC and in 2020 received the Industrial Achievement Award from IFAC; both for the development of the artificial pancreas. He is an elected member of the National Academy of Medicine (2016), the National Academy of Inventors (2020), and the National Academy of Engineering (2021). His research interests are in systems biology, network science, modeling and analysis of circadian rhythms, and drug delivery for diabetes. In his spare time, he enjoys hiking with his family, genealogical research, and is a certified soccer referee at the adult and collegiate level.
Translating Control Technology for Personalized Medicine
The future of healthcare will involve personalized medical therapies for individuals. In applications involving the delivery of a drug (for example, insulin), such personalization can be achieved through the use of tailored feedback control strategies. For close to 30 years, our research group has collaborated with medical experts on the design of algorithms for safe and effective insulin delivery for individuals with Type 1 diabetes mellitus (T1DM). T1DM is a chronic autoimmune disease affecting approximately 35 million individuals world-wide, with associated annual healthcare costs in the US estimated to be approximately $15 billion. Over the years, there have been remarkable innovations in glucose measurement technology, insulin pump design, and personalized control algorithms. Over the last 5 years, multiple commercial closed-loop devices have entered the market, thus delivering the so-called “artificial pancreas” to individuals with T1DM. In this talk, I will outline the difficulties inherent in controlling physiological variables, the challenges with regulatory approval of such devices, and will describe several control systems engineering algorithms we have tested in clinical and outpatient settings for the artificial pancreas. I will describe our work in creating an embedded version of our MPC algorithm to enable a portable implementation in a medical IoT framework and will highlight some of the open challenges for automated insulin delivery. I’ll close by sharing other medical examples where feedback algorithms could provide transformational advances in personalized medicine, including chronotherapy.
