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Computer Science

D-Index
30
Citations
6795
World Ranking
13876
National Ranking
272

Overview

Ricard Gavaldà is affiliated with the Universitat Politècnica de Catalunya in Spain. Their research spans the fields of Medicine and Computer Science, with a focus on Artificial Intelligence, Nephrology, Physiology, Endocrine and Autonomic Systems, and Radiology, Nuclear Medicine and Imaging.

Their work covers several key topics, including:

  • COVID-19 diagnosis using AI
  • Organ Transplantation Techniques and Outcomes
  • Liver Disease Diagnosis and Treatment
  • Liver Disease and Transplantation
  • Natural Language Processing Techniques
  • Topic Modeling
  • Biomedical Text Mining and Ontologies

Ricard Gavaldà has contributed to publications in various academic journals and venues. Frequent publication venues include:

  • Journal of Clinical Medicine
  • Computers in Biology and Medicine
  • Data Mining and Knowledge Discovery
  • Anaesthesia Critical Care & Pain Medicine
  • ERJ Open Research

Selected recent papers authored or co-authored by Ricard Gavaldà include:

  • Interpretable prediction of mortality in liver transplant recipients based on machine learning, 2022, Computers in Biology and Medicine
  • Prediabetes Is Associated with Increased Prevalence of Sleep-Disordered Breathing, 2022, Journal of Clinical Medicine
  • Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19, 2020, Anaesthesia Critical Care & Pain Medicine
  • A case study of improving a non-technical losses detection system through explainability, 2023, Data Mining and Knowledge Discovery
  • Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients, 2021, Journal of Clinical Medicine

They frequently collaborate with other researchers, including Jaume Baixeries, Enric Sánchez, Gerard Torres, Ariadna Sauret, and Marcelino Bermúdez-López.

In addition to journal publications, Ricard Gavaldà has contributed to book publications with Springer Science+Business Media, including the work titled "ECML PKDD 2020 Workshops" published in 2020.

Best Publications

  • Learning from Time-Changing Data with Adaptive Windowing

    Albert Bifet;Ricard Gavaldà

  • New ensemble methods for evolving data streams

    Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Richard Kirkby

  • Adaptive Learning from Evolving Data Streams

    Albert Bifet;Ricard Gavaldà

  • Towards energy-aware scheduling in data centers using machine learning

    Josep Ll. Berral;Íñigo Goiri;Ramón Nou;Ferran Julià

  • Oracles and Queries That Are Sufficient for Exact Learning

    Nader H. Bshouty;Richard Cleve;Ricard Gavaldà;Sampath Kannan

  • Machine Learning for Data Streams: With Practical Examples in Moa

    Albert Bifet;Ricard Gavaldà;Geoff Holmes;Bernhard Pfahringer

  • Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms

    Carlos Domingo;Ricard Gavaldà;Osamu Watanabe

  • MACHINE LEARNING FOR DATA STREAMS

    Albert Bifet;Ricard Gavaldà;Geoff Holmes;Bernhard Pfahringer

  • Energy-efficient and multifaceted resource management for profit-driven virtualized data centers

    íñigo Goiri;Josep Ll. Berral;J. Oriol Fitó;Ferran Julií

  • Algorithms for learning finite automata from queries: a unified view

    José L. Balcázar;Josep Díaz;Ricard Gavaldà;Osamu Watanabe

  • Kalman filters and adaptive windows for learning in data streams

    Albert Bifet;Ricard Gavaldà

  • Adaptive on-line software aging prediction based on machine learning

    Javier Alonso;Jordi Torres;Josep Ll. Berral;Ricard Gavalda

  • Mining frequent closed graphs on evolving data streams

    Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Ricard Gavaldà

  • Online techniques for dealing with concept drift in process mining

    Josep Carmona;Ricard Gavaldà

  • Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning

    Josep Ll. Berral;Ricard Gavalda;Jordi Torres

  • Computational power of neural networks: a characterization in terms of Kolmogorov complexity

    J.L. Balcazar;R. Gavalda;H.T. Siegelmann

  • Improving Adaptive Bagging Methods for Evolving Data Streams

    Albert Bifet;Geoff Holmes;Bernhard Pfahringer;Ricard Gavaldà

  • Reducing wasted resources to help achieve green data centers

    J. Torres;D. Carrera;K. Hogan;R. Gavalda

  • Fraud Detection in Energy Consumption: A Supervised Approach

    Bernat Coma-Puig;Josep Carmona;Ricard Gavalda;Santiago Alcoverro

  • Detecting Sentiment Change in Twitter Streaming Data

    Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Ricard Gavaldà

  • Non-automatizability of bounded-depth frege proofs

    Maria Luisa Bonet;Carlos Domingo;Ricard Gavaldà;Alexis Maciel

  • Proceedings of the 20th international conference on Algorithmic learning theory

    Ricard Gavaldà;Gábor Lugosi;Thomas Zeugmann;Sandra Zilles

Frequent Co-Authors

Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Jordi Torres
Jordi Torres Universitat Politècnica de Catalunya
Albert Bifet
Albert Bifet University of Waikato
Osamu Watanabe
Osamu Watanabe Tokyo Institute of Technology
Francis Bach
Francis Bach École Normale Supérieure
Eduard Ayguadé
Eduard Ayguadé Barcelona Supercomputing Center
Hava T. Siegelmann
Hava T. Siegelmann University of Massachusetts Amherst
Gábor Lugosi
Gábor Lugosi Pompeu Fabra University
Doina Precup
Doina Precup McGill University
Dino Pedreschi
Dino Pedreschi University of Pisa

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