Matteo Chinazzi

According to our database, Matteo Chinazzi authored at least 13 papers between 2010 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2023

Deep Bayesian Active Learning for Accelerating Stochastic Simulation.

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Disentangled Multi-Fidelity Deep Bayesian Active Learning.

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

Proceedings of the International Conference on Machine Learning, 2023

2022

Anatomy of the first six months of COVID-19 vaccination campaign in Italy.

Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Marco Ajelli, Nicola Perra, Alessandro Vespignani

PLoS Comput. Biol., 2022

Multi-fidelity Hierarchical Neural Processes.

Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.

Fred S. Lu, André T. Nguyen, Nicholas B. Link, Mathieu Molina, Jessica T. Davis, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Marc Lipsitch, Mauricio Santillana

PLoS Comput. Biol., 2021

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave.

Jessica T. Davis, Matteo Chinazzi, Nicola Perra, Kunpeng Mu, Ana L. Pastore y Piontti, Marco Ajelli, Natalie E. Dean, Corrado Gioannini, Maria Litvinova, Stefano Merler, Luca Rossi, Kaiyuan Sun, Xinyue Xiong, Ira M. Longini Jr., M. Elizabeth Halloran, Cécile Viboud, Alessandro Vespignani

Nat., 2021

Accelerating Stochastic Simulation with Interactive Neural Processes.

Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu

CoRR, 2021

DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting.

Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yian Ma, Rose Yu

CoRR, 2021

Quantifying Uncertainty in Deep Spatiotemporal Forecasting.

Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020

Finding Patient Zero: Learning Contagion Source with Graph Neural Networks.

Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu

CoRR, 2020

A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models.

Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T. Davis, Alessandro Vespignani, Mauricio Santillana

CoRR, 2020

2019

Mapping the physics research space: a machine learning approach.

Matteo Chinazzi, Bruno Gonçalves, Qian Zhang, Alessandro Vespignani

EPJ Data Sci., 2019

2010

Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data.

Fabrizio Iozzi, Francesco Trusiano, Matteo Chinazzi, Francesco C. Billari, Emilio Zagheni, Stefano Merler, Marco Ajelli, Emanuele Del Fava, Piero Manfredi

PLoS Comput. Biol., 2010