• 강사 : 서유진 박사 (Division of Applied Mathematics, Brown University, USA)
• 일시 : 2021. 11. 25 (목) 17:00~18:30

Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. In this talk, I will present how ML techniques can be applied to ecological problems. In the first part of the talk, I will show problem formulation for plant-pollinator interaction analysis and the use of latent factor models to understand the underlying mechanisms that determine species interactions. The second part of the talk addresses imperfect detection, which is a pervasive challenge of ecological data. I will present a new link prediction framework customized for ecological networks to handle the imperfect detection problem in modeling multiple species interactions. In the third part, I will introduce a computational framework called StatEcoNet, which integrates neural networks with a graphical generative model, to handle model complexity as well as the imperfect detection problem in species distribution modeling. Lastly, I will give an overview of the Global Ecosystem Dynamics Investigation (GEDI) project, a NASA mission to measure forest vertical structure and understand its relationship to Earth's carbon cycle.