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Matteo Chinazzi
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2020 – today
- 2024
- [j6]Velma K. Lopez, Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson:
Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. 20(5): 1011200 (2024) - 2023
- [c4]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. ICML 2023: 37624-37634 - [c3]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Deep Bayesian Active Learning for Accelerating Stochastic Simulation. KDD 2023: 2559-2569 - [i7]Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yi-An Ma, Rose Yu:
Disentangled Multi-Fidelity Deep Bayesian Active Learning. CoRR abs/2305.04392 (2023) - 2022
- [j5]Nicolò Gozzi, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Marco Ajelli, Nicola Perra, Alessandro Vespignani:
Anatomy of the first six months of COVID-19 vaccination campaign in Italy. PLoS Comput. Biol. 18(5) (2022) - [c2]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. KDD 2022: 2029-2038 - [i6]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Multi-fidelity Hierarchical Neural Processes. CoRR abs/2206.04872 (2022) - 2021
- [j4]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:
Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nat. 600(7887): 127-132 (2021) - [j3]Fred S. Lu, André T. Nguyen, Nicholas B. Link, Mathieu Molina, Jessica T. Davis, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Marc Lipsitch, Mauricio Santillana:
Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches. PLoS Comput. Biol. 17(6) (2021) - [c1]Dongxia Wu, Liyao Gao, Matteo Chinazzi, Xinyue Xiong, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. KDD 2021: 1841-1851 - [i5]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
DeepGLEAM: a hybrid mechanistic and deep learning model for COVID-19 forecasting. CoRR abs/2102.06684 (2021) - [i4]Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Quantifying Uncertainty in Deep Spatiotemporal Forecasting. CoRR abs/2105.11982 (2021) - [i3]Dongxia Wu, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, Rose Yu:
Accelerating Stochastic Simulation with Interactive Neural Processes. CoRR abs/2106.02770 (2021) - 2020
- [i2]Dianbo Liu, Leonardo Clemente, Canelle Poirier, Xiyu Ding, Matteo Chinazzi, Jessica T. Davis, Alessandro Vespignani, Mauricio Santillana:
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. CoRR abs/2004.04019 (2020) - [i1]Chintan Shah, Nima Dehmamy, Nicola Perra, Matteo Chinazzi, Albert-László Barabási, Alessandro Vespignani, Rose Yu:
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks. CoRR abs/2006.11913 (2020)
2010 – 2019
- 2019
- [j2]Matteo Chinazzi, Bruno Gonçalves, Qian Zhang, Alessandro Vespignani:
Mapping the physics research space: a machine learning approach. EPJ Data Sci. 8(1): 33:1-33:18 (2019) - 2010
- [j1]Fabrizio Iozzi, Francesco Trusiano, Matteo Chinazzi, Francesco C. Billari, Emilio Zagheni, Stefano Merler, Marco Ajelli, Emanuele Del Fava, Piero Manfredi:
Little Italy: An Agent-Based Approach to the Estimation of Contact Patterns- Fitting Predicted Matrices to Serological Data. PLoS Comput. Biol. 6(12) (2010)
Coauthor Index
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last updated on 2024-12-04 20:16 CET by the dblp team
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