Title: Machine Learning in Personalized Medicine and Genomics. Author: María Martínez García. Abstract: Personalized medicine, also known as precision medicine, aims to use an individual’s genetic profile to guide decisions on disease prevention, diagnosis, and treatment. Unlike traditional medicine’s one-fits-all approach, personalized medicine considers individual variations that can influence disease risk, severity, treatment response, and susceptibility to…

Title: Uncertainty propagation methods for collision avoidance in autonomous navigation Ref.: N629092412095 Principal Investigator: Javier López Santiago Starting Date: 01/08/2024 Ending Date: 31/07/2027 Budget: 315.198,57 € Background:

Title: THAI: Towards Humble and Discoverable AI Principal Investigator: Pablo Martínez Olmos Starting Date: 30/09/2024 Ending Date: 30/03/2026 Budget: 40.000,00 Abstract: This project addresses concerns about overconfidence and reliability in generative AI. As AI technologies continue to reshape various sectors, the project’s primary motivation lies in mitigating the risks associated with AI’s persuasive capabilities, particularly…

Title: Multisite Smartphone-based Ecological Momentary Intervention for suicide prevention Ref.: HR23-00421 Principal Investigator: Antonio Artés Rodríguez Starting Date: 01/03/2024 Ending Date: 28/02/2027 Budget: 999.955,00 € Background: Suicide is a major public health issue that represents a leading cause of years of life lost and entails substantial costs for health and welfare systems. Prevention of suicide…

Title: Advanced Inference and Representation Learning Methods in Variational Autoencoders. Author: Ignacio Peis Aznarte Abstract: Deep Generative Models have gained significant popularity in the Machine Learning research community since the early 2010s. These models allow to generate realistic data by leveraging the power of Deep Neural Networks. The field experienced a signficant breakthrough when Variational Autoencoders (VAEs)…

Title: How can humans leverage machine learning? From Medical Data Wrangling to Learning to Defer to Multiple Experts. Author: Daniel Barrejon Moreno. Abstract:The irruption of the smartphone into everyone’s life and the ease with which we digitise or record any data supposed an explosion of quantities of data. Smartphones, equipped with advanced cameras and sensors, have empowered…

Title Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation Authors Emese Sukei, Santiago de Leon Martinez, Pablo M. Olmos and Antonio Artés-Rodríguez Abstract Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or…

Title One-week suicide risk prediction using real-time smartphone monitoring Authors Maria Luisa Barrigon; Lorena Romero-Medrano; Pablo Moreno-Muñoz; Alejandro Porras-Segovia; Jorge Lopez-Castroman; Philippe Courtet; Antonio Artés-Rodríguez; Enrique Baca-Garcia Abstract Background: Suicide is a major global public health issue becoming increasingly common despite preventive efforts. Though current methods for predicting suicide risk are not sufficiently accurate, technological…

Antonio Artés Rodríguez and Pablo Martínez Olmos have recieved funding for two collaborative research projects within the call «Medicina Personalizada de Precisión» granted by Instituto de Salud Carlos III de Madrid. Antonio Artés Rodríguez Title: Integrating longitudinal patient-generated data and multi-omic profiling for comprehensive precision oncology in womens’ cancers Ref.: PMP22/00032 Principal Investigator: Quintela, Miguel…

Title: Deep Attentive Time Series Modelling for Quantitative Finance Author: Fernando Moreno Pino Supervisors:  Antonio Artés Rodríguez, Pablo M. Olmos Abstract: Time series modelling and forecasting is a persistent problem with extensive implications in scientific, business, industrial, and economic areas. This thesis’ contribution is twofold. Firstly, we propose a novel probabilistic time series forecasting methodology that introduces…