Bridging physics-based and data-driven modeling for COVID-19 forecasting

Rui Wang, Danielle Robinson, Christos Faloutsos, Yuyang Wang, and Rose Yu, AutoODE: Bridging physics-based and data-driven modeling for COVID-19 forecasting, NeurIPS 2020 Workshop on Machine Learning in Public Health. (best paper award at the NeurIPS Machine Learning in Public Health Workshop)

As COVID-19 continues to spread, accurately forecasting the number of newly infected, removed and death cases has become a crucial task in public health. While mechanics compartment models are widely-used in epidemic modeling, data-driven models are emerging for disease forecasting. In this work, we investigate these two types of methods for COVID-19 forecasting. Through a comprehensive study, we find that data-driven models outperform physics-based models on the number of death cases prediction. Meanwhile, physics-based models have superior performances in predicting the number of infected and removed cases. In addition, we present an hybrid approach, AutoODE, that obtains a 57.4% reduction in mean absolute errors of the 7-day ahead COVID-19 trajectories prediction compared with the best deep learning competitor.

2021 Franz Edelman Award

2021 Edelman Competition

Koen Peters, Sérgio Silva, Tim Sergio Wolter, Luis Anjos, Nina van Ettekoven, Éric Combette, Anna Melchiori, Hein Fleuren, Dick den Hertog, Özlem Ergun (2022) UN World Food Programme: Toward Zero Hunger with Analytics. INFORMS Journal on Applied Analytics 52(1):8-26. https://doi.org/10.1287/inte.2021.1097 (2021 Franz Edelman Award, WFP received the Nobel Peace Prize in 2020.)

10 Breakthrough Technologies 2023

 David Rotman, 10 Breakthrough Technologies 2023, MIT Technology Review, January 9, 2023.

Our annual look at 10 Breakthrough Technologies—including CRISPR for high cholesterol, battery recycling, AI that makes images, and the James Webb Space Telescope—that will have a profound effect on our lives. Plus care robots, 3-D printing pioneers, and chasing bugs on the blockchain.

Feature selection repository scikit-feature in Python

J. Li, K. Cheng, S. Wang, F. Morstatter, R.P. Trevino, J. Tang, and H. Liu, “Feature Selection: A Data Perspective,” ACM Computing Surveys, vol. 50, no. 6, pp. 1–45, December 2017. https://doi.org/10.1145/3136625 (Python at GitHub)

scikit-feature is an open-source feature selection repository in Python developed by Data Mining and Machine Learning Lab at Arizona State University. It is built upon one widely used machine learning package scikit-learn and two scientific computing packages Numpy and Scipy. scikit-feature contains around 40 popular feature selection algorithms, including traditional feature selection algorithms and some structural and streaming feature selection algorithms.

It serves as a platform for facilitating feature selection application, research and comparative study. It is designed to share widely used feature selection algorithms developed in the feature selection research, and offer convenience for researchers and practitioners to perform empirical evaluation in developing new feature selection algorithms.