Research Interests

My research interests fall into the broad area of data science, with special emphasis on data mining, machine learning, and databases.
My work is centered on real-world problems that requires large-scale data processing, and I tackle such problems mainly from a combinatorial-optimization and algorithmic perspectives.
Specifically, my research activity is mainly focused on the following topics:

I. Graph analytics, graph querying, graph mining
  • Graph clustering
  • Dense subgraph discovery
  • Managing/mining/querying uncertain graphs
  • Reachability/distance queries on graphs
  • Querying graph databases
  • Graph pattern mining
II. Managing and mining data on the (social) Web
  • (Social) web mining
  • (Social) recommender systems
  • Information propagation in complex networks
  • Community search/detection
  • Personalization of online services
  • Ranking and centrality

III. Natural Language Processing (NLP)
  • Entity recognition and disambiguation
  • Distributional semantic embeddings
IV. Clustering high-dimensional and multifaceted data
  • Projective/subspace clustering
  • Clustering ensembles
  • Projective clustering ensembles
  • Document clustering leveraging multiple views/classifications
V. Clustering probabilistic/uncertain data

VI. Managing and mining semistructured data

VII. Managing and mining biological data
  • Querying and mining biological networks
  • Modeling, mining, and analyzing proteomic data
  • Modeling, mining, and analyzing gene expression data
VIII. Spatio-temporal data management