Quite intense weeks lately culminating in the graduation of several of my Ph.D. and M.Sc students. Quite interestingly this time they are all heading for positions in industry where I trust they will bring the appetite for science, technology and curiosity which I hope to have inspired in them.
Andrea Valenti completed his Ph.D. on learning representations for neural reasoners and he is now Machine Learning Engineer at Henesis
Alex Pasquali graduated in AI with honours and a thesis on hashtag#reinforcementlearning for virtual networks placement. He is now heading for an internship at Sauber: hope to spot him in the next F1 races!
Nicola Gugole graduated in AI with a thesis on identity preserving photo enhancement. Good luck for your adventure at Bending Spoons!
Sina Farhang Doust graduated in AI with a thesis bridging hashtag#nlp and hashtag#graph hashtag#neuralnetworks for legal text, and he is now bringing his skills to Aptus.AI.
Best of luck to all of you guys! Looking forward to collaborate again in the future.
Category Archives: Students
New Ph.D. graduate
Congratulations to Francesco Crecchi that just defended his Ph.D. thesis on “Deep Learning Safety under Non-Stationarity Assumptions”, jointly supervised by me and Battista Biggio. Francesco is my first Ph.D. student to graduate, so that doubles the celebrations on my side. Kudos!!
Reinforcement Learning course kickoff
The new edition of the Reinforcement Learning course will kickoff on Monday 29/03/2021 h. 16.00.
This is a course offered to M.Sc. Students of the AI Curriculum (recognition as 3 Free-choice CFU) and Ph.D. students. For furher information please check the official course Moodle.
Neurips 2020 WS papers
Excellent result by our group in the upcoming NeurIPS 2020 workshops with four accepted papers.
Congrats to Antonio Carta, Francesco Landolfi, Danilo Numeroso and Matteo Ronchetti!
Preprints coming up..
Generative Tomography Reconstruction. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Deep Learning and Inverse Problems, 2020.
K-plex Cover Pooling for Graph Neural Networks. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Learning Meets Combinatorial Algorithms, 2020.
Explaining Deep Graph Networks with Molecular Counterfactuals. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Machine Learning for Molecules - Accepted as Contributed Talk (Oral), 2020.
Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization . 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Workshop on Beyond BackPropagation: Novel Ideas for Training Neural Architectures, 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!
Probabilistic Learning on Graphs via Contextual Architectures. In: Journal of Machine Learning Research, vol. 21, no. 134, pp. 1−39, 2020.
Stanford-UNIPI thesis
Congratulations to Alessio Gravina, for his joint Stanford-UNIPI thesis on deep learning for graphs hitting the news:
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
Best PhD project Award
Many congratulations to Francesca Lizzi for winning the best Ph.D. project award at BIOSTEC 2020 with her thesis project on “Deep-learning based analysis of mammograms to improve the estimation of breast cancer risk”. Terrific job!
ISPR course kickoff
The 2019/20 edition of the ISPR course will start with the first lecture on
THURSDAY 20 FEBRUARY 2020 – h. 14-16
CLASSROOM L1 – POLO FIBONACCI (First Floor)
See you there!
Artificial Intelligence in Medicine paper
Check out our newly accepted journal paper on discovering and measuring the confounding effect of data attributes in biomedical tasks. Preprint available on the Arvix. Congratulations to Elisa!
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI). In: Artificial Intelligence in Medicine, vol. 103, 2020.