Dr. Marzia Cescon
David C. Zimmerman Assistant Professor of Mechanical & Aerospace EngineeringOffice Location W206 Engineering Building 2
Phone 713-743-2037
Fax 713-743-4503
Email mcescon2 [at] uh.edu
Website https://cescon.me.uh.edu/
Education
2014, Ph.D. in Control Engineering, Lund University, Lund, Sweden
2011, Tech. Lic. in Control Engineering, Lund University, Lund, Sweden
2008, M.Sc. in Automation Engineering, University of Padova, Padova, Italy
2005, B.Sc. in Information Engineering, University of Padova, Padova, Italy.
Professional Experience
Aug 2019 – Present, David C. Zimmerman Assistant Professor, University of Houston, Houston, TX
Dec 2023 - January 2024, Cornelis Drebbel Faculty Fellow, Delft Center for Systems and Control (DCSC), Delft University of Technology, Delft, Holland
2018 – 2019, Postdoctoral Research Fellow, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
2018 – 2019, Adjunct Investigator, Sansum Diabetes Research Institute, Santa Barbara, CA
2015 – 2017, Research Fellow, The University of Melbourne, Melbourne, Australia
2014 – 2015, Research Specialist, University of California Santa Barbara, Santa Barbara, CA
Oct 2012 -Dec 2012 Visiting Lecturer in Automatic Control, Zhejiang University, Hangzhou (China)
Professional and Scientific Affiliations
Ongoing Reviewer: IEEE Transactions on Automatic Control, IEEE Transactions on Control System Technology, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Biomedical Engineering, Automatica, International Journal of Control, International Journal of Adaptive Control and Signal Processing, Biomedical Signal Processing and Control, Medical and Biological Engineering and Computing, Mathematical Biosciences, Journal of Applied Mathematics, Journal of Biomedical and Health Informatics, Journal of Computer Methods and Programs in Biomedicine; Various international conferences in systems, controls, and engineering in medicine and biology
Courses Taught
Mechanics II: Dynamics (MECE3336) – Undergraduate Course Topics: Kinematics of particles, kinetics of particles, work and energy, kinetics of systems of particles, plane kinematics of rigid bodies, plane kinetics of rigid bodies
Dynamics & Control of Mechanical Systems (MECE3338) – Undergraduate Course Topics: mechanical systems, transfer functions, 1st and 2nd order system response, time and frequency response specifications, introduction to electrical systems, vibration isolation, and feedback controller gain design to meet closed-loop response specifications.
Learning Meets Systems and Control (MECE6397) – Graduate Course Topics: Feedback fundamentals, robust control, adaptive control, learning dynamical systems, data-driven control, reinforcement learning
Mechanical Engineering Project Design (MECE6368) – Graduate
Research Interests
- Controls and Dynamical Systems
- Learning-Based Control
- Information and Decision Systems
- Machine Learning
- Modeling and System Identification
Grants
- National Science Foundation (FDT-Biotech), 2024-2027, $307,774 Lead PI (100%)
- National Aeronautics and Space Administration (MIRO), 2024-2029, $399,690 co-PI (8%)
- Mathworks Academic Support 2024 $13,309.00 PI (100%)
- National Science Foundation CAREER (CMMI DCSD), 2024-2029, $655,248 PI (100%)
- Diatech Diabetes Inc. / Juvenile Diabetes Research Foundation (IDDP), 2023-25, $120,000 PI (100%)
- U.S. Air Force Office for Scientific Research (AFRL ML-RCP), 2023-25 $200,000 PI (50%)
- U.S. Air Force Office for Scientific Research (AFRL ML-RCP), 2022-24 $249,990 PI (40%)
- University of Houston, Advanced Manufacturing Institute, 2022, $49,674 co-PI (33%)
- Diatech Diabetes Inc. / National Institutes of Health (SBIR Phase 1), 2021-2022, $52,170 PI (100%)
Current Research Projects
Data-Enabled Neural Multi-Step Predictive Control (DeMuSPc): a Learning- Based Predictive and Adaptive Control Approach for Complex Nonlinear Systems.
Learning Blood Glucose Predictors with Artificial Neural Networks for decision support systems and closed-loop glucose control.
Data-Driven Physiology-Based Predictive Digital Twin of Human Physiological Systems.
Decision Support for Insulin Dosage Optimization.
Personalized Insulin Infusion Failure Detection Algorithms.
Run time assurance (RTA) approaches for safe control of autonomous platforms in adverse and hazardous conditions.
Safe kinodynamic motion planning for autonomous platforms.
Development of novel algorithms for collaborative satellite autonomy.
Awards & Honors
2019 – 2024: David C. Zimmerman Faculty Fellowship, University of Houston.
2024: National Science Foundation Faculty Early Career Development Program (CAREER)
2024: Diabetes Technology Society Student Research Award, Bronze prize winner. (as advisor)
2023: Cornelis Drebbel Faculty Fellowship
2020: Diabetes Technology Society Student Research Award, Silver prize winner. (as advisor)
2018: Diabetes Technology Society Student Research Award, Gold prize winner (as advisor)
Selected Publications
- E.M. Aiello, M. Jaloli, and M. Cescon. Model Predictive Control (MPC) of an Artificial Pancreas with Data-Driven Learning of Multi-Step-Ahead Blood Glucose Predictors. Control Engineering Practice, Vol 144, 105810 (2024), arXiv preprint arXiv:2307.12015.
- M. Jaloli, and M. Cescon. Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology. Control Engineering Practice,Vol. 142, 2024, pp. 105762, DOI: 10.1016/j.conengprac.2023.105762.
- D. Cai, W. Wu, M. Cescon, W. Liu, L. Ji, D. Shi (2023). Data-enabled learning and control algorithms for intelligent glucose management: The state of the art. Annual Reviews in Control, 2023, Vol. 56, pp. 100897. DOI: 10.1016/j.arcontrol.2023.100897.
- M. Cescon, D. Choudhary, J.E. Pinsker, V. Dadlani, M. M. Church, Y.C. Kudva, F. J. III Doyle, and E. Dassau. Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions. Computers in Biology and Medicine, 135, 104633, Aug 2021
- M. Cescon, S. Deshpande, R. Nimri, F. J. Doyle III, E. Dassau. Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People with Type 1 Diabetes, in IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 482-491, Feb. 2021, doi: 10.1109/TBME.2020.3005622.
- T. Tanaka, H. Malki and M. Cescon. Linear Quadratic Tracking with Reinforcement Learning Based Reference Trajectory Optimization for the Lunar Hopper in Simulated Environment. IEEE Access 2021, vol. 9, pp. 162973-162983. https://doi.org/10.1109/ACCESS.2021.3134592.
Patents
- Iterative Learning Control with Sparse Measurements for Long Acting Insulin Injections in People with Type 1 Diabetes Inventors: Marzia Cescon, E. Dassau, F. J. III Doyle
U.S. Provisional Application No.: 62/872,020
Date: July 2019 - Activity Detection and Categorization in Diabetic Patients
Inventors: Marzia Cescon, E. Dassau, F. J. III Doyle
U.S. Provisional Application No.: 62/758,283
Date: November 2018