Ricard Gavaldà

Ricard Gavaldà

Ricard Gavaldà
PhD in Computer Sciencefrom Universitat Politècnica de Catalunya (UPC)
Professor at UPC

Personal webpage

email: gavalda@cs.upc.edu

Biosketch

Full professor at the Department of Computer Science of Universitat Politècnica de Catalunya (UPC), in Barcelona,since 2008. He is an associate professor. He has authored over 80 papers in journals and conferences, and supervised or co-supervised 7 Ph.D. students at UPC. His original research field was computational complexity theory, and is stillgenerally interested in applications of logic and algebra in computer science.His current interests are algorithmic aspects of machine learning and data mining, with emphasis on efficient and scalable algorithms. In recent years he has started working on applications of these techniques to several domains, such as autonomic and green computing, healthcare, mobility and social network analysis, including active pursuit of industrial collaborations.

 

Research Interests

  • Machine Learning
  • Data Mining
  • Algorithms, with emphasis on scalability
  • Application to specific domains including Healthcare, Sustainability, Mobility, and Social Network Analysis
  • Theory of computation

 

Selected publications

  • Borja Balle, Jorge Castro, Ricard Gavaldà: Adaptively Learning Probabilistic Deterministic Automata from Data Streams. Machine Learning 96(1-2): 99-127 (2014)
  • Borja Balle, Jorge Castro, Ricard Gavaldà: Learning Probabilistic Automata: A Study in State Distinguishability. Theoretical Computer Science 473: 46-60 (2013)
  • Josep Lluis Berral, Ricard Gavaldà, Jordi Torres: Power-Aware Multi-Datacenter Management Using Machine Learning. 42nd Intl. Conf. on Parallel Processing (ICPP 2013): 858-867
  • Albert Bifet, Ricard Gavaldà: Mining Adaptively Frequent Closed Unlabeled Rooted Trees in Data Streams. 17th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2008): 34-42
  • Ricard Gavaldà, Pascal Tesson, Denis Thérien: Learning Expressions and Programs over Monoids. Information and Computation 204(2): 177-209 (2006)