About me

I am a Ph.D. student at the Graph Machine Learning Group, within the Swiss AI lab IDSIA, at Università della Svizzera italiana (USI), under the supervision of Prof. Cesare Alippi. My research focuses on problems regarding irregular spatiotemporal data, like prediction, imputation, and control on sensor networks using geometric deep learning.

I obtained BSc (2017) and MSc (2020) degrees in Computer Science and Engineering at Politecnico di Milano. My master thesis project has been supervised by Prof. Nicola Gatti.


Ph.D. Student in Informatics

2020 — ongoing

Currently, I am a Ph.D. Student at the Swiss AI Lab IDSIA at USI Università della Svizzera Italiana, under the supervision of prof. Cesare Alippi.

MSc in Computer Science and Engineering

2017 — 2020

Master’s degree with honors (110/110L), defending a thesis on machine learning. During the two years of studies, I mostly attended AI-oriented courses.


Sep 2018 — Jan 2019

During the semester spent abroad (in Valencia) within the Erasmus program, I attended Spanish and English courses on programming, robotics and artificial intelligence.

BSc in Engineering of Computing Systems

2014 — 2017

The course program covered general topics of engineering and computer science.

High School in Mathematics

2009 — 2014
Liceo C. Caminiti

High school diploma with a specific focus in mathematics and science at Liceo Scientifico Caminiti in Santa Teresa di Riva (Sicily).

Academic activities


  • Advanced Topics in Machine Learning, MSc at USI
    Sep 2022 — Jan 2023

    Students tutoring for projects on reproducibility.

  • Graph Deep Learning, MSc at USI
    Feb 2022 — Jun 2022

    I gave a lecture on Spatiotemporal Graph Neural Networks and tutored students on projects.

  • Machine Learning, BSc at USI
    Feb 2021 — Jun 2021

    Lab sessions on practical aspects and show how to design machine learning solutions to real-world problems.

Supervised students

  • Marco Latella, MSc at USI

    Graph Representation Learning for Multi-site Photovoltaic Energy Production


  • Spotlight presentation at TGL Workshop

    The Temporal Graph Learning Workshop at NeurIPS 2022 (New Orleans).

  • Poster presentation at NeurIPS

    The 36th Conference on Neural Information Processing Systems (New Orleans).

  • Invited talk at Baker Hughes

    Invited to give a webinar on time series imputation (Virtual).

  • Poster presentation at ICLR

    The 10th International Conference on Learning Representations (Virtual).

  • Abstract presentation at MLDM

    The 10th Italian Workshop on Machine Learning and Data Mining (Virtual).

Awards & Scholarships

  • Best Paper AwardTemporal Graph Learning Workshop @ NeurIPS

    For the paper "Scalable Spatiotemporal Graph Neural Networks".

  • Travel Award — NeurIPS

    Travel award to attend the NeurIPS conference in New Orleans (US).

  • Scholarship — SAPAR

    Scholarship awarded to the 4 best STEM students.

  • Scholarship — Politecnico di Milano

    Reduced tuition for high merits.


I believe in worldwide accessibility of science. As such, I make the software I develop for my research publicly available (whenever possible) through my GitHub page. You can also find the code related to my publications on the GitHub page of Graph Machine Learning Group.

Torch Spatiotemporal

Torch Spatiotemporal (TSL) is a library built upon PyTorch and PyG for neural spatiotemporal data processing, with a focus on Graph Neural Networks.

GitHub Documentation

Other projects

  • GraPV
    Sep 2020 — ongoing

    Developing of graph-based methods for multi-site photovoltaic power forecasting, to improve production accuracy prediction. Funded by Innosuisse.


  • Taming Local Effects in Graph-based Spatiotemporal Forecasting
  • Andrea Cini* , Ivan Marisca* , Daniele Zambon , Cesare Alippi
  • Scalable Spatiotemporal Graph Neural Networks
  • Andrea Cini* , Ivan Marisca* , Filippo Maria Bianchi , Cesare Alippi
    AAAI 2023
  • Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
  • Ivan Marisca* , Andrea Cini* , Cesare Alippi
    NeurIPS 2022
  • Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
  • Andrea Cini* , Ivan Marisca* , Cesare Alippi
    ICLR 2022

    *Equal contribution.