Machine Learning for Preventing and Combating Pandemics

Pengtao Xie · Xiaodan Liang · Jure Leskovec · Judy Wawira · Jeremy Weiss · Manuel Gomez Rodriguez · Madalina Fiterau · Yueyu Jiang · Leo Celi · Eric P Xing

Abstract Workshop Website
Fri 7 May, 8:45 a.m. PDT


Pandemics are major disasters in human history. The recent COVID-19 pandemic has caused about 0.52 million deaths and infected about 11 million people all over the world as of July 3. In the past two decades, several pandemics/ epidemics including Zika, SARS, Ebola, H1N1 Flu, etc. have killed a large number of people. Medical experts predict that future pandemics will periodically occur and may be even worse than past ones. Since the outbreak of COVID-19, AI researchers have been developing methods to combat this pandemic, including building forecasting models to predict the spread of coronavirus, developing computer vision methods to analyze CT scans and chest X-rays for screening and risk assessment of infected cases, leveraging computational biology methods for vaccine development, etc. These efforts have shown high utility in controlling the spread of COVID-19 and pave a promising way for preventing future pandemics. To further promote research on AI-based control of pandemics, we aim to organize a workshop which brings together researchers in machine learning, healthcare, medicine, public health, etc. and facilitates discussions and collaborations in developing machine learning and AI methods to diagnose and treat infectious diseases and prevent and contain pandemics. Different from previous healthcare-related workshops, our workshop focuses on infectious diseases and health problems related to pandemic.

Chat is not available.
Timezone: America/Los_Angeles »