Since the onset of the COVID-19 pandemic, there has been growing interest in epidemic modeling. While traditional mechanistic models effectively describe the mathematical dynamics of disease transmission, they often face challenges in handling complex, real-world scenarios. In contrast, Graph Neural Networks (GNNs) have emerged as a powerful alternative in epidemic research. This talk offers a comprehensive review of GNN applications in epidemic modeling, presenting a hierarchical taxonomy for both epidemiological tasks and modeling techniques. We categorize methods into Neural Models and Hybrid Models and introduce our Python toolkit, EpiLearn, which encompasses a wide range of these approaches. This talk will also cover the limitations of current models from multiple perspectives and propose future research directions. By providing a thorough exploration of existing GNN models, we aim to equip researchers with valuable insights into the current state and future possibilities of using GNNs in epidemiology.
Speaker Bio
Wei Jin is an Assistant Professor of Computer Science at Emory University. His research focuses on graph machine learning and time series analysis, with notable accomplishments such as INNS Doctoral Dissertation Runner-up Award, KAUST Rising Stars in AI, AAAI New Faculty Highlights, Most Influential Papers in KDD and WWW by Paper Digest, and top finishes in three NeurIPS competitions. He has led teams in building well-received open-source machine learning platforms including EpiLearn (https://github.com/Emory-Melody/EpiLearn), the first Python toolkit for machine learning in epidemic modeling. In addition, he has organized multiple tutorials and workshops at top conferences, and published in top-tier venues such as ICLR, KDD, ICML, and NeurIPS. He has served as (senior) program committee members at these conferences and received the WSDM Outstanding Program Committee Member award.
More Details
- When: Wed 30 Oct 2024, at 1:00 - 2:00 pm (GMT+10)
- Speaker: Prof Wei Jin (Emory University)
- Host: Dr Ruihong Qiu
- Venue: Online
- Zoom: https://uqz.zoom.us/j/89353235547 [Recording]
No.24-10 Recreating the Physical Natural World from Images