Stefano Teso’s personal page

Post-doc researcher in Machine Learning
Department of Computer Science and Information Engineering
University of Trento, Italy

mail: teso _at_ disi _dot_ unitn _dot_ it
www: http://disi.unitn.it/~teso

Who am I?

So, it turns out that I am a post-doc researcher. I’m mostly concerned with the problem of predicting structured objects, in its many variants. Logical, semantic, and relational data are what I’m into.

I’ve been working on different application domains, ranging from the fully biological (genomics, proteomics, interactomics), to the more sociological, to the completely abstract (!). Feel free to take a peek at my publications for the details.

Short Bio / Breve Biografia

I got a bachelor’s degree in CS from the University of Venice, and a master’s degree in bioinformatics plus a Ph.D. degree in Information Engineering and Computer Science (with a biological flavor) from the University of Trento.

After that I spent some time at the Fondazione Bruno Kessler (FBK), within the Data & Knowledge Management unit, under the supervision of prof. Luciano Serafini, working on statistical-relational learning for knowledge bases.

Right now I’m working at the Deep and Structured Machine Learning Group at the University of Trento, under the supervision of prof. Andrea Passerini.

Projects / Progetti

The Trentino Knowledge Base project, financed by the CARITRO Foundation. Check out the project page for more!

Software

pylmt : an implementation of Learning Modulo Theories for learning in hybrid relational domains.

setmargin : implementation of constructive preference elicitation via set-wise max-margin learning.

Teaching / Didattica

Publications / Pubblicazioni

  • Stefano Teso, Paolo Dragone, Andrea Passerini – Coactive Critiquing: Elicitation of Preferences and Features, AAAI 2017. (To appear)
  • Stefano Teso, Paolo Dragone, Andrea Passerini – Structured Feedback for Preference Elicitation in Complex Domains, BeyondLabeler workshop at IJCAI 2016. link
  • Stefano Teso, Andrea Passerini, Paolo Viappiani – Constructive Preference Elicitation by Setwise Max-margin Learning, In IJCAI 2016. link
  • Stefano Teso, Andrea Passerini – Inducing Sparse Programs for Learning Modulo Theories, In NIPS Workshop on Constructive Machine Learning 2015. link
  • Stefano Teso, Roberto Sebastiani, Andrea Passerini – Constructive Learning Modulo Theories, In NIPS Workshop on Constructive Machine Learning, 2015.
  • Stefano Teso, Roberto Sebastiani, Andrea Passerini – Structured Learning Modulo Theories, Artificial Intelligence Journal, 2015. link
  • Claudio Sacca’, Stefano Teso, Michelangelo Diligenti, Andrea Passerini – Improved Multi-level Protein–Protein Interaction Prediction with Semantic-based Regularization, BMC Bioinformatics 2014, 15:103. link
  • Stefano Teso, Andrea Passerini – Joint Probabilistic-Logical Refinement of Multiple Protein Feature Predictors, BMC Bioinformatics 2014, 15:16. link
  • Stefano Teso, Roberto Sebastiani, Andrea Passerini – Hybrid SRL with Optimization Modulo Theories, In NIPS Workshop on Constructive Machine Learning, 2013. link
  • Stefano Teso – Statistical Relational Learning for Proteomics: Function, Interactions, Evolution, Ph.D. Thesis, University of Trento, 2013. link
  • Stefano Teso, Jacopo Staiano, Bruno Lepri, Andrea Passerini, Fabio Pianesi – Ego-Centric Graphlets for Personality and Affective States Recognition, ASE/IEEE International Conference on Social Computing, 2013.
  • Stefano Teso, Jacopo Staiano, Bruno Lepri, Andrea Passerini, Fabio Pianesi – Ego-Centric Graphlets for Personality and Affective States Recognition, Workshop on Information in Networks, 2013.
  • Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini – Predicting virus mutations through relational learning, In ECCB Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2011).
  • Stefano Teso, Cristina Di Risio, Andrea Passerini, Roberto Battiti – An on/off lattice approach to protein structure prediction from contact maps, 2010, In PRIB‘10 Proceedings of the 5th IAPR.
  • Carlo Nicolini, Bruno Lepri, Stefano Teso, Andrea Passerini – From on-going to complete activity recognition exploiting related activities, 2010, In ICPR 2010 Workshop on Human Behaviour Understanding (HBU 2010).
  • Stefano Teso, Paola Lecca – Notes on stochastic simulation of chemical kinetics with cycle-leaping, 2008, Microsoft Research–University of Trento Centre for Computational and Systems Biology (COSBI). link