GEOG Seminar 4/6: Xiaowei Jia, "Knowledge Guided Machine Learning: Challenges and Opportunities"

Join us for our weekly seminar this Thursday, April 6 from 3:45 to 5 p.m. in River Road or via Zoom! Dr. Xiaowei Jia at the University of Pittsburgh will discuss his work in data science and machine learning.

Abstract:

Data science and machine learning (ML) models, which have found tremendous success in several commercial applications where large-scale data is available, e.g., computer vision and natural language processing, have met with limited success in scientific domains. Traditionally, physics-based models of dynamical systems are often used to study engineering and environmental systems. Despite their extensive use, these models have several well-known limitations due to incomplete or inaccurate representations of the physical processes being modeled. Given rapid data growth due to advances in sensor technologies, there is a tremendous opportunity to systematically advance modeling in these domains by using machine learning methods. However, capturing this opportunity is contingent on a paradigm shift in data-intensive scientific discovery since the “black box” use of ML often leads to serious false discoveries in scientific applications. 

My work aims to build the foundations of knowledge-guided machine learning by exploring several ways of bringing scientific knowledge and machine learning models together. In particular, we show the effectiveness of the proposed methods in multiple applications of great societal and scientific relevance. My work also has the potential to greatly advance the pace of discovery in a number of other scientific and engineering disciplines where physics-based models are used, e.g., hydrology, agriculture, climate science, materials science, power engineering and biomedicine.

Speaker Bio:

Dr. Xiaowei Jia is an Assistant Professor in Computer Science at the University of Pittsburgh. He has extensive expertise on physics guided machine learning, with applications in hydrology, crop monitoring, carbon emission, etc. He received the Best Dissertation Award from the University of Minnesota. Dr. Jia's research has been published in top data mining and artificial intelligence venues (e.g., AAAI, IJCAI) and he has a recent comprehensive survey on physics guided machine learning in ACM Computing Surveys. He also has applied research published in domain science journals such as Water Resources Research. His research is supported by NSF, USGS, NASA, etc., and he has various ongoing collaborations with our department.

Zoom Info: Please reach out to lizhili@umd.edu for Zoom meeting information.