For poster sessions: posters can be viewed here. If interested in a poster, you can click the corresponding Zoom link. The authors are presenting their poster in their Zoom.

Online Workshop, May 07 (Friday), 2021

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.

The workshop will feature speakers, panelists, and poster presenters from computer vision, natural language processing, computational biology, graph learning, etc., covering topics which include (but are not limited to):

  • Time-series analysis for forecasting the spread of pandemic
  • Computer vision for the screening and risk assessment of infected cases
  • Natural language processing for mining medical literature about pandemic
  • Graph learning for drug discovery and repurposing
  • Computational biology for vaccine development
  • Predicting and scheduling medical resources
  • Privacy-preserving contact tracing
  • Analyzing electronic medical records for treating infectious diseases
  • Social media analysis for detecting misinformation about pandemic
  • Measuring the social and economic impact of pandemic
  • Bias and society implications from the use of ML in pandemics
  • Clinical translation / implementation of algorithms in real life

In addition to invited talks by leading researchers from diverse backgrounds including CV, NLP, computational biology, graph learning, etc., the workshop will feature poster sessions and panel discussion to share perspectives on establishing foundational understanding of existing ML approaches on pandemics study and theoretically-principled ways of developing new methods. We accept short papers (up to 4 pages in ICLR format), which will be peer-reviewed by at least two reviewers. The accepted papers are allowed to be submitted to other conference venues.