Dr. Xiaowei Jia
Abstract:
Data
science and machine learning models, which have found tremendous success in
several commercial applications where large-scale data is available, e.g.,
computer vision and natural language processing, has 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. Because the hypothesis space of scientific applications is
often complex and exponentially large, an uninformed data-driven search can
easily select a highly complex model that is neither generalizable nor
physically interpretable, resulting in the discovery of spurious relationships,
predictors, and patterns. This problem becomes worse when there is a scarcity
of labeled samples, which is quite common in science and engineering domains.
The work aims to build the foundations of physics-guided machine learning by
exploring several ways of bringing scientific knowledge and machine learning
models together. In particular, we will show examples of using Physics-Guided
Machine Learning techniques in improving the modeling of aquatic systems. My
work has the potential to greatly advance the pace of discovery in a number of
scientific and engineering disciplines where physics-based models are used,
e.g., hydrology, agriculture, climate science, materials science, power
engineering and biomedicine.
Biography:
Xiaowei
Jia is an Assistant Professor in the Department of Computer Science at the
University of Pittsburgh. He received his Ph.D. degree from the University of
Minnesota in 2020 under the supervision of Prof. Vipin Kumar. Prior to that, he
got his M.S. degree from State University of New York at Buffalo in 2015 and
his B.S. degree from University of Science and Technology of China in 2012. His
research interests include physics-guided data science, spatio-temporal data
mining, deep learning, and remote sensing. His work has been published in major
data mining and AI journals (e.g., TKDE) and conferences (e.g., SIGKDD, ICDM, SDM,
and CIKM), as well top-tier journals in hydrology (e.g., WRR) and agronomy
(e.g., Agricultural Economics). Xiaowei was the recipient of UMN Doctoral
Dissertation Fellowship (2019), the Best Conference Paper Award in ASONAM 16,
and the Best Student Paper Award in BIBE 14.