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: news

# Best paper award @AAAI 2023

Great news from overseas as our paper on “Non-dissipative propagation by anti-symmetric deep graph networks” has just received the Best Student Paper Award 🏆 🍾 at the Deep Learning for Graphs workshop of AAAI23.

The paper is a great piece of work by Alessio Gravina, with a bit of support by Claudio Gallicchio and myself.

An extended version of it will s.oon be presented at ICLR 2023

# New year updates

Quite a busy semester, hence long time no posts. Lets start the year with a bunch of good news.

July 2022 is going to be a busy month in Padova with the organization of IEEE WCCI 2022, including

- 1st Pervasive Artificial Intelligence Workshop co-chaired with Antonio Carta (Università di Pisa), Patrizio Dazzi (ISTI-CNR), Magdalini Eirinaki (San Jose State University), Iraklis Varlamis (Harokopio University of Athens) –
**Deadline early April 2022** - Deep learning for graphs special session, co-chaired with Shirui Pan (Monash University), Daniele Grattarola (IDSIA), Miao Zhang (Aalborg University), Nicolò Navarin (University of Padova), Feng Xia (Federation University Australia), Daniele Zambon (IDSIA) –
**Deadline 31st January 2022**

# 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!!

# IJCNN 2021 papers

Our group had 4 papers recently accepted to the upcoming International Joint Conference on Neural Networks! Much work on deep learning for graphs, including a novel edge-based model, an efficient graph generation approach and an explanation method for the chemical domain. Also a first proposal for an efficient federation of reservoir computing methods, part of our H2020 TEACHING efforts. Preprints soon on the Arxiv!

# 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..

# 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!).

# ICML 2020 Top Reviewer

Review effort does not always come unnoticed.. Happy to be listed in the top third of the ICML reviewers.

# 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!