New M.Sc. Graduates in AI

Big graduation day in the ML family with 3 newly graduated students (two cum laude!) on topics ranging from neuro-probabilistic models for graphs, interpretability of graph neural networks for chemistry and cognitive architectures for creativity. Congratulations!

Daniele Arioli, CognAC: a cognitive architecture based on Information Dynamics of Thinking, co-supervised with V. Gervasi, M.Sc. in Computer Science & Artificial Intelligence

Valerio De Caro, Graph Relative Density Networks, M.Sc. in Computer Science & Artificial Intelligence

Danilo Numeroso, Explaining Deep Graph Networks By Structured Counterfactual Explanations, M.Sc. in Computer Science & Artificial Intelligence

Paper Accepted at COLING 2020

Congratulations to Daniele Castellana for having his paper accepted at COLING 2020. Check it out if you are interested in higher-order neural networks for parse trees using tensor decompositions (soon on the Arxiv!).

Daniele Castellana, Davide Bacciu: Learning from Non-Binary Constituency Trees via Tensor Decomposition. PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS (COLING 2020), 2020.

New JMLR paper

Couldn’t think of a better venue for my 99th research paper than the Journal of Machine Learning Research. Check out our work on deep and probabilistic learning for graphs. Terrific job by Federico Errica!

Davide Bacciu, Federico Errica, Alessio Micheli: Probabilistic Learning on Graphs via Contextual Architectures. In: Journal of Machine Learning Research, vol. 21, no. 134, pp. 1−39, 2020.

IEEE Service Award

Extremely honoured to have been nominated among the 2019 Outstanding Associate Editors of the IEEE Transactions on Neural Networks and Learning Systems.

Glad to see that the effort dedicated to reviewing and editorial services does not always go unnoticed!

Deep Learning for graph on Neural Networks

Very proud of the last effort from our group! Our tutorial paper on deep learning for graphs will be published as an invited paper on the Neural Networks journal!

Check out a preliminary version on the Arxiv!

Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda: A Gentle Introduction to Deep Learning for Graphs. In: Neural Networks, vol. 129, pp. 203-221, 2020.

COVID-19 Task Force

If you work in AI and you are willing to contribute to the worldwide fight against COVID-19 please consider joining the CLAIRE-COVID19 task force. I am coordinating the workgroup on omics, chemical and clinical data processing.

You can have a look at the first result of our pan-european collaboration which has been released here: it is a curated repository of protein-viral-drug-disease interactions for helping research in drug repurposing, bio-informatics, etc.

Graduation day

While classes are suspended, University is still open and today we had a great (epidemiologically-correct) graduation day with 5 of my students having discussed their B.Sc. and M.Sc. theses. Great works on graph neural networks applied to biochemistry and social-network data, tree transductions in image captioning, machine vision and genomic data processing. Congratulations to them all!

  • Alessio Gravina, Machine Learning prediction of compounds impact on Schizoprenia treatment, co-supervision by Corrado Priami and Kevin V. Grimes (Stanford University), M.Sc. in Computer Science & Artificial Intelligence
  • Davide Serramazza, Image captioning with structure generation, M.Sc. in Computer Science & Artificial Intelligence
  • Francesco Bachini, On the use of sequential learning models to estimate natural selection on Hiv from clinical samples, co-supervision by Matteo Fumagalli (Imperial College London), M.Sc. in Data Science and Business Informatics,
  • Gabriele Tenucci, A neural network-based intelligent filter for Youtube videos, B.Sc. in Computer Science
  • Lorenzo Gazzella, Analysing privacy-risks in social networks by deep learning for graphs, co-supervision with A. Monreale, B.Sc. in Computer Science
A snapshot of D. Serramazza Tree2Tree neural network for automated image captioning.