Early Prognosis of COVID-19 Infections via Machine Learning
Objective:The 2020 COVID-19 outbreak has revealed infections that result in particularly distinct outcomes: certain patients remain asymptomatic during the infection, others experience moderate symptoms for a few weeks, while still others suffer acute or even critical complications. This array of outcomes poses a key challenge for COVID-19 containment, since the most effective countermeasures when infections are detected are markedly different for each type of patient. To address this challenge, Dr. Santiago Mazuelas, an AXA Research Fund grantee at BCAM, will develop machine learning techniques for the early prognosis of COVID-19 infections that predict the future severity of infections using health data obtained at the time the infection is detected. Such predictions could help medical staff and public health stakeholders make timely decisions that could result in favorable outcomes. For instance, an infected patient with a negative (or positive) early prognosis could be directly transferred to semi-intensive care (or a regular hospital ward) before he/she experiences notable symptoms. In addition, the prediction algorithms developed in the project could be used to closely monitor individuals who are not infected but who have a high probability of being asymptomatic or suffering complications if they do contract COVID-19.
Measuring ideals in a singularity
This proposal concerns singularities arising in the solution spaces of systems of polynomial equations.
MATH4SPORTS - Modelización matemática para la industria deportiva: salud y rendimiento
MATH4SPORTS seeks to transfer applied mathematics as a driving technology to the field of the sports industry, with a high potential for technology transfer to start-ups, professional clubs, researchers and other agents in the innovative environment of Bizkaia.
Chemistry informed machine learning in emulsion polymerization processes and products
Spectral theory and PDE: Real and Fourier Analysis