Seminars course 2021-22
Hristo Inouzhe: A fair clustering approach through diversity.
Paula Gordaliza: Fair machine learning: from repairing data to algorithmic fairness advice.
Juan Carlos Hernández: Algorithmic fairness. The proposal of the European Regulation on Artificial Intelligence.
Simulations and machine learning for dark search subject : classification methods, optimal filtering and parameter extraction in electrical signals.
Data scientist and PhD in physics. Part of the central marketing measurement team at Amazon USA. He has developed analysis tools, machine learning, simulations and data engineering for subject dark experiments: for LuxZeplin Experiment as researcher associate at Stanford/SLAC National Accelerator Laboratory, and for SuperCDMS Experiment during his doctoral stay at Texas A&M University. He has also served as consultant science for data at Propagator, a company he founded in Mexico.
Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework
Rubén Armañanzas is a scientist with twenty years of experience in artificial intelligence, machine learning, bio and neuro informatics, and biomedical applications. He obtained his doctoral degree in 2009 at the University of the Basque Country (San Sebastián, Spain). He moved to Madrid to work at the Technical University of Madrid within the Cajal Blue Brain Project until fall 2013. He held a faculty position in the Bioengineering Department and an affiliate faculty of the Interdisciplinary Program in Neuroscience at George Mason University (Fairfax, US) until late 2018. Rubén moved to industry in early 2019 as the Manager of the Algorithm Development Team at BrainScope Company Inc. (Bethesda, MD) where he managed project planning and execution of the Research & Development team. In late 2020, he joined the Basque Center for Applied Mathematics as a Senior Classification Engineer. During his career Rubén has led several healthcare projects aimed at providing personalized medicine to predict disease diagnoses. The methodological toolbox includes, among others, probabilistic graphical models and several classification algorithms from machine learning, and bio-inspired heuristic algorithms within the optimization field.
Business innovation through data science and mathematical research
Dae-Jin Lee is researcher of BCAM - Basque Centre for Applied Mathematics - and leader of the research line of group of Applied Statistics (part of area of research of data Science and Artificial Intelligence of BCAM) created in 2014. He obtained his doctorate in Statistics from Universidad Carlos III de Madrid in June 2010. Prior to his position at BCAM, he was researcher Postdoctoral Fellow in the Mathematics, Informatics and Statistics division of the CSIRO (former CMIS) now CSIRO Data61 in Melbourne Australia (from February 2011 to March 2014). He also heads the data science area of the Centre's Transfer Unit at knowledge .
Differential replication as a tool for machine learning accountability in practice
Irene Unceta obtained a Bachelor's Degree in Physics from the University of Barcelona, Spain, in 2013, a MSc in Computational Science from the University of Amsterdam, The Netherlands, in 2017, and an Industrial PhD in Mathematics and Computer Science from the University of Barcelona, in 2021.
"Bayesian spatial conditional conditional overdispersion models for count data: Applications to infant mortality and to COVID-19 incidence".
Mabel Morales Otero is doing the doctorate in Economics under the supervision of Professor Vicente Núñez Antón, within the department of Quantitative Methods of the School of Economic and Business Sciences, University of the Basque Country (UPV/EHU).
Semi-outdoor space as an adaptation strategy to the risk of overheating in a tropical context of high urban density - Social and environmental benefits.
Juan Carlos Gamero Salinas researcher is a pre-doctoral fellow at School of Architecture (ETSAUN) of the University of Navarra (UNAV).