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X-ORIGINAL-URL:https://cmc.deusto.eus/
X-WR-CALNAME:cmc.deusto.eus
X-WR-CALDESC:DeustoCCM - Chair of Computational Mathematics at University of Deusto
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UID:MEC-5ef7f68d74e8de904b773669eff12e1d@cmc.deusto.eus
DTSTART:20250709T093000Z
DTEND:20250709T110000Z
DTSTAMP:20250702T112700Z
CREATED:20250702
LAST-MODIFIED:20250707
PRIORITY:5
TRANSP:OPAQUE
SUMMARY:DeustoCCM Seminar by R. Orive and A. Alvarez
DESCRIPTION:Date: Wed. July 9, 2025 (11:30H)\nEvent: DeustoCCM Seminar\n11:30H. Session 01\nTitle: Robustness with neural ordinary differential equations\nSpeaker: Prof. Rafael Orive Illera\nAffiliation: UAM, Autonomous University of Madrid\nAbstract. The robustness of a machine learning refers to how the model responds to data disturbances, which can be random, associated with measurement errors, or due to targeted and malicious disturbances. Neural differential equations have emerged as a suitable tool both for developing learning models and for interpreting the behavior of neural networks within them. Using optimal differential equation control techniques, we study the problem of robustness and new learning models.\n \n12:15H. Session 02\nTitle: New deep learning models and perspectives for continuous-time glucose monitoring\nSpeaker: Antonio Álvarez López\nAffiliation: UAM, Autonomous University of Madrid\nAbstract. In this talk, I will present recent progress from my doctoral thesis on modeling the dynamics of probability distributions using time series data. This problem appears in various areas, notably digital health, where understanding how biomarkers like glucose evolve over time is key to studying chronic diseases such as diabetes. Our approach uses a Gaussian mixture model that represents the evolution of a probability density. We combine Maximum Mean Discrepancy (MMD) for Gaussian parameter estimation at each time, with a neural ODE governing the evolution of mixture weights over time. The resulting model is interpretable, computationally efficient, and sensitive to subtle distributional shifts. Simulations show our method achieves accuracy comparable to current state-of-the-art methods. We illustrate its effectiveness with data from a digital clinical trial, demonstrating how interventions influence glucose distributions over time.\n \nWHERE\nUniversity of Deusto, DeustoTech.\nRoom: Logistar\nUnibertsitate Etorb., 24, 48007 Bilbao, Bizkaia\n
URL:https://cmc.deusto.eus/events-calendar/deustoccm-seminar-09jul2025/
ORGANIZER;CN=DeustoCCM - Chair of Computational Mathematics:MAILTO:
CATEGORIES:Events Calendar,Events Calendar Past
LOCATION:Unibertsitate Etorb., 24, 48007 Bilbao, Bizkaia
ATTACH;FMTTYPE=image/png:https://cmc.deusto.eus/wp-content/uploads/DeustoCCMseminar_rOrive_aAlvalrez_09jul2025_img.png
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