When AI meets Spatio-Temporal Data: Concepts, Methodologies, and Applications

Speaker: Dr. Yuxuan LIANG
         National University of Singapore

Title:  "When AI meets Spatio-Temporal Data: Concepts, Methodologies,
         and Applications"

Date:   Wednesday, 1 February 2023

Time:   10:00am - 11:00am HKT


Zoom link:
https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09

Meeting ID: 465 698 645
Passcode: 20222023


Abstract:

With the rapid advances in new-generation information technologies such as
the Internet of Things, 5G, and mobile Internet, Spatio-Temporal (ST) data
are growing explosively. In contrast to image, text, and voice data, ST
data often present unique spatio-temporal characteristics, including
spatial distance and hierarchy, as well as temporal closeness,
periodicity, and trend. Spatio-Temporal AI is a proprietary AI technology
for ST data, where AI meets conventional city-related fields, like
transportation, civil engineering, environment, and economy, in the
context of urban spaces. This talk first introduces the concept of
Spatio-Temporal AI, discussing its general framework and key challenges
from the perspective of computer science. Secondly, we classify the
applications of spatio-temporal AI into four categories, consisting of
modeling ST trajectories, ST grid data, ST graphs, and ST series. We also
present representative scenarios in each category. Thirdly, we delineate
our recent progress in the methodologies of the above four categories.
Finally, we outlook on the future of spatio-temporal AI, suggesting a few
research topics that are somehow missing in the community.


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Biography:

Yuxuan Liang is a Research Fellow at School of Computing, National
University of Singapore (NUS). He is currently working on the research,
development, and innovation of spatio-temporal data mining and AI, with a
broad range of applications in smart cities. Prior to that, he completed
his PhD study at NUS. He published over 30 peer-reviewed papers in
refereed journals and conferences, such as KDD, WWW, NeurIPS, and TKDE.
Those papers have been cited over 1,800 times (Google Scholar H-Index:
20). He was recognized as 1 out of 10 most innovative and impactful PhD
students focusing on data science in Singapore by Singapore Data Science
Consortium (SDSC).