ALFA – Machine Learning, Functional Programming, and Algorithms

Scientific Activity

To cover all these research areas, the INIT is divided into 10 sections

ALFA – Machine Learning, Functional Programming, and Algorithms

Our main focus is the design of algorithms. These algorithms can be used in many problems but we are mostly interested in those applications related to machine learning. In addition, we are interested in programming with functional languages and the adaptation of algorithms to functional programming.

RESEARCHERS

Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020 Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020
MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES
NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

Statistical analysis of events in space-time on networks and trajectories. (SpTNet) December 2016 – December 2020

New technologies have opened the door to the era of big data and with it the emergence of data science. In a massive world of information, the need for statistical treatment of events that evolve in space-time on a network system is increasingly common. When these events are monitored over time they give information on the displacement and provide trajectories over networks. The theory of stochastic space- time processes on networks provide the theoretical and methodological support for this type of data.

This project aims to address the statistical modeling of this type of event in these new scenarios. We will begin with the parametric analysis of first-order
characteristics, which are necessary to arrive at those of second order and higher orders. We intend to characterize all the orders (as far as possible) of a specific process on networks to determine the underlying structure of space-time interaction existing on the network.

MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES

MODELOS ESTOCÁSTICOS E INFERENCIA PARA PROCESOS MARCADOS ESPACIO-TEMPORALES SOBRE REDES

UJI-B2021-37

NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

NEURO-ML: MACHINE LEARNING THROUGH BIO-INSPIRED MODELS (AND VICE VERSA)

UJI-A2022-12

 

Esta web utiliza cookies propias y de terceros para facilitar la navegación a los usuarios y ofrecerles una mejor experiencia y servicio. Si continua navegando, entendemos que acepta la política de cookies de la Universitat Jaume I.

ACEPTAR
Aviso de cookies