Ricard Gavaldà
Professor, CS Department, Universitat Politècnica de Catalunya
My research is on Machine Learning and Data Mining, covering both their algorithmic foundations and their applications. Before moving into machine learning, and for some time in parallel, I worked in Computational Complexity Theory.
Here are my Google Scholar profile and DBLP publication list.
Topics in Machine Learning:
- Stream mining: In this scenario, the data to be analyzed is not available at once, but arrives as a sequence of items, possibly in high volume and at high speed, so models need to adapt to new instances and possibly forget the effect of old ones. I have worked on prediction and frequent pattern mining tasks involving data streams, with particular emphasis on streams whose distribution evolves over time. Coauthors include Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Massimo Quadrana.
- Inference of latent-variable models: Models in which data is assumed to be generated as a (probabilistic) consequence of some unobserved variables. Coauthors include Matteo Ruffini and Marta Casanellas.
- Inference of finite-state machines: A special case of the above in which the models are finite-state machines that generate sequences of symbols. Coauthors include Jorge Castro, Borja Balle, Joelle Pineau, and Doina Precup.
- Causality: Most machine learning methods identify correlations in the data, rather than causal relationships, which are often what we ultimately want to understand. Causal graphs and do-calculus are two ways of representing and reasoning about causality from data. Coauthors include Gilles Blondel and Marta Arias.
Concerning applications, here is a list of the industrial projects I worked on while at UPC, with topics including datacenter management, healthcare and life sciences, social network analysis, sports analytics and others.