See how machine learning and big data can recreate complex natural systems in this SCAI Summer Seminar Series hosted by Yezhou “YZ” Yang, an associate professor of computer science and engineering.
Learn to Reconstruct Marine Productivity Through Machine Learning Algorithms and Big Data
Presented by Zuchuan Li, Duke Kunshan University
Thursday, June 8, 2023
11 a.m.–noon
Brickyard (BYENG) 209, Tempe campus [map] and Zoom
For a recording, contact YZ Yang at yz.yang@asu.edu.
Abstract
The ocean biological carbon pump regulates atmospheric carbon dioxide levels and thus climate. Our understanding of the biological carbon pump until now was limited by scarce data. However, nowadays, our ability to acquire data has outstripped our ability to analyze it. For example, satellite remote sensing can cover the word’s oceans within a few days. The autonomous platforms are gathering an exponentially growing number of environmental observations. The overarching goal of Zuchuan Li’s research is to reconstruct the biological carbon pump through machine learning algorithms trained with satellite remote sensing data, prior knowledge and heterogeneous data from various platforms such as the global biogeochemical Argo float array. He will also discuss some environmental applications for deep learning, which may facilitate the combination of heterogeneous big data and prior knowledge represented as ordinal/partial differential equations.
About the speaker
Zuchuan Li is an assistant professor of data and environmental sciences at the Division of Natural and Applied Sciences at Duke Kunshan University. He is also affiliated with the Data Science Research Center at Duke Kunshan University. Li is interested in machine learning, Bayesian inference, and their applications to environmental science. Li has a doctorate from Duke University, where he has been supported by the NASA Earth and Space Science Fellowship from 2013 to 2016. He was awarded the WHOI postdoctoral scholarship to conduct his postdoctoral research at the Woods Hole Oceanographic Institution in 2017. He was a research scientist of statistics and machine learning at the University of Phoenix from 2018 to 2022.
