Publications

Journals

  1. A.  Chebbi, M. Franchek, K. Grigoriadis and M. Cescon. Substrate Temperature Estimation and Control in Advanced MOCVD Process for Superconductor Manufacturing. The International Journal of Advanced Manufacturing Technology, 2024. DOI: 10.1007/s00170-024-13699-1
  2. 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.
  3. 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.
  4. M. Jaloli, and M. Cescon (2023). Reinforcement Learning for Multiple Daily Injection (MDI) Therapy in Type 1 Diabetes (T1D). BioMedInformatics 2023, 3, pp. 422–433. DOI: 10.3390/biomedinformatics3020028
  5. 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.
  6. M. Jaloli, W. Lipscomb, and M. Cescon (2022). Incorporating the Effect of Behavioral States in Multi-Step Ahead Deep Learning Based Multivariate Predictors for Blood Glucose Forecasting in Type 1 Diabetes. BioMedInformatics, 2(4), pp.715-726. DOI: 10.3390/biomedinformatics2040048
  7. P. Colmegna, C. Toffanin, M. Cescon, R. Visentin (2022). Editorial: Recent Advances in Computer Simulation for Diabetes Treatment and Care. Front. Endocrinol. 13:914657. DOI: 10.3389/fendo.2022.914657
  8. M. Jaloli and M. Cescon. Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using CNN-LSTM-Based Deep Neural Network. Journal of Diabetes Science and Technology, Online first April 25, 2022. https://doi.org/10.1177/19322968221092785
  9. 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.
  10. T. Kaaya, S. Wang, M. Cescon, et al. Physics-lumped parameter based control oriented model of dielectric tubular actuator. Int J Intell Robot Appl (Oct 2021). https://doi.org/10.1007/s41315-021-00211-1
  11. 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
  12. A. Keow, A. Mayhall, M. Cescon, and Z. Chen. Active disturbance rejection control of metal hydride hydrogen storage. Int. J. of Hydrogen Energy, 46(1), 1 January 2021, Pages 837-851.
  13. 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.

Peer-Reviewed Conference Proceedings

  1. S. Panza, Y. Wi, D. Invernizzi, M. Cescon, and M. Lovera. Experiential learning in automatic control using quadrotor UAVs. 2024 IFAC Workshop on Aerospace Control Education (WACE2024). Accepted for oral presentation and publication in the conference proceedings.
  2. Y. Wi and M. Cescon. System Identification and Automatic Flight Control for a Quadrotor Drone. Proc. Modeling, Estimation and Control Conference (MECC2024). Accepted for oral presentation and publication in the conference proceedings.
  3. Y. Wi  and M. Cescon.  Modeling and Identification of Quadrotor Dynamics affected by Wind Stress. Proc. Modeling, Estimation and Control Conference (MECC2024). Accepted for oral presentation and publication in the conference proceedings.
  4. M. Ismail Ali, T. Tavoulareas, Z. Chen, and M. Cescon. Project-Based Learning Using a Quadrotor Testbed. Proc. Conference on Control Technology and Applications (CCTA2024). Accepted for oral presentation and publication in the conference proceedings.
  5. Y. Wi and M. Cescon. Design and implementation of an experiential learning intervention for a graduate course in system identification. Proc. Conference on Control Technology and Applications (CCTA2024). Accepted for oral presentation and publication in the conference proceedings.
  6. M. F. Arevalo-Castiblanco, Y. Wi, M. Cescon and C. Uribe.  An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks. Proc. Conference on Control Technology and Applications (CCTA2024). Accepted for oral presentation and publication in the conference proceedings.
  7. Y. Wi and M. Cescon. Data-driven control-oriented identification of quadrotor dynamics: a tutorial. IFAC Symposium on System Identification (SYSID2024). Accepted for oral presentation and publication in the conference proceedings.
  8. A. Crespo-Santiago and M. Cescon. Physiology-Informed Deep Learning Modeling of Type 1 Diabetes Dynamics: Mapping Data to Virtual Subjects. IFAC Symposium on System Identification (SYSID2024). Accepted for oral presentation and publication in the conference proceedings.
  9. De la Barcena, A., Rhodes, C., McCarroll, J., Cescon, M. and Hobbs, K.L. Testing Spacecraft Formation Flying with Crazyflie Drones as Satellite Surrogates. 2024 IEEE Aerospace Conference, Big Sky, Montana. Accepted for oral presentation.
  10. M. Jaloli and M. Cescon. Modeling Physical Activity Impact on Glucose Dynamics in People with Type 1 Diabetes for a Fully Automated Artificial Pancreas. 2023 IEEE Conference on Control Technology and Applications (CCTA), Bridgetown, Barbados, 2023, pp. 546-551, doi: 10.1109/CCTA54093.2023.10253054.

  11. T. Kaaya, S Wang, M. Cescon, and Z. Chen. Physics-based and control-oriented modeling of dielectric elastomer tubular actuator. In Proc. American Control Conference 2021 (ACC2021), pp. 4472-4477, doi:10.23919/ACC50511.2021.9483321.

  12. M. Jaloli, D Choudhary, and M. Cescon. Neurological status classification using convolutional neural network. In Proc. IFAC Conf. on Cyberphysical and Human Systems (CPHS2020), IFAC-PapersOnLine, 53(5), 2020, Pages 409-414,
    https://doi.org/10.1016/j.ifacol.2021.04.193. [video]

  13. D Choudhary and M. Cescon. EDA-sense: Dynamic feedback control of sympathetic arousal. In Proc. IFAC Conf. on Cyberphysical and Human Systems (CPHS2020), IFAC-PapersOnLine, 53(5), 2020, Pages 238-243,
    https://doi.org/10.1016/j.ifacol.2021.04.103. [video]

Peer-Reviewed Abstracts

  1. A. Crespo-Santiago and M. Cescon. Physiology-Informed Generative Adversarial Networks in Type 1 Diabetes: Mapping Data to Virtual Subjects. 17th Int. Conf. on Advanced Technologies and Treatments for Diabetes (ATTD2024), Florence, Italy. Accepted for presentation.
  2. M. Jaloli and M. Cescon.  Investigating the effect of different treatments in exercise-induced hypoglycemia in Type 1 Diabetes. Selected for oral presentation, 16th Int. Conf. on Advanced Technologies and Treatments for Diabetes (ATTD2023), Berlin, Germany. To appear in: Diabetes Technology and Therapeutics.
  3. Blanco, L.E., Gray, J.C., Wilcox, J.H. and Cescon, M.. A novel infusion failure detection paradigm based on fluid pressure and supervised learning. 21st Diabetes Technology Meeting (DTM2022), To appear in: Journal of Diabetes Science and Technology
  4. M. Jaloli and M. Cescon. Demonstrating the effect of daily stress on blood glucose level variation in type 1 diabetes. Diabetes Technology and Therapeutics, 24, A79-A79.
  5. M. Jaloli  and M. Cescon. Demonstrating the effect of daily physical activities on blood glucose level variation in type 1 diabetes. Selected for oral presentation, 15th Int. Conf. on Advanced Technologies and Treatments for Diabetes (ATTD2022), Barcelona, Spain. Diabetes Technology and Therapeutics, 24, A234-A234.

  6. M. Jaloli and M. Cescon. Predicting blood glucose levels using CNN-LSTM neural networks. The 20th Diabetes Technology Meeting (DTM2020), Bethesda, MD, Diabetes Technology Society Student Research Award Silver Prize Winner and Selected for oral presentation. J. Diabetes Sci. & Tech., 15 (2), A36 p. A432, March 2021 [video]

  7. D. Choudhary and M. Cescon. Characterising the Effect of Physical Activity for Blood Glucose Management in People with Type 1 Diabetes (T1D), The 20th Diabetes Technology Meeting (DTM2020), Bethesda, MD, J. Diabetes Sci. & Tech., 15 (2), A15 p. A411, March 2021

  8. D. Choudhary, M. Jaloli, and Marzia Cescon. Characterising sympathetic response with power spectral density analysis. In Proc. 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EBMC2020), Montreal, Canada, 2020 [video]