Wenchong He

Ph.D. Candidate in Computer Science

University of Florida

Biography

I am a Ph.D. candidate in Department of Computer & Information Science & Engineering at the University of Florida. My broad research areas are data science, machine learning and artificial intelligence. Specifically my research focuses on spatiotemporal data mining, knowledge-informed machine learning, trustworthy AI as well as interdisciplinary scientific applications in climate science, environmental monitoring and physics simulation.

Interests

Machine Learning and Deep Learning
Data Science
Uncertainty Quantification
Deep Generative AI

Education

PhD in Computer Science, 2024

University of Florida
M.S. in Computer Science, 2021

University of Alabama (Transfered to UF)
M.S. in Computational Mathematics, 2019

College of William Mary
BSc in Physics, 2017

University of Science and Technology of China (USTC)

Featured Publications

Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen, Ronald Fick, Miles Medina, Christine Angelini

September, 2023 In NeurIPS23

A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space

We propose a novel hierarchical transformer architecture for massive point sample obervations in continuous space.

Selected Publications

Wenchong He, Zhe Jiang, Tingsong Xiao, Zelin Xu, Shigang Chen, Ronald Fick, Miles Medina (2023). A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space. In NeurIPS23.

PDF Cite Code Dataset Project Poster Slides Video Source Document

Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang (2023). Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery. In ACM SIGSPATIAL 2023 [Best Paper Award].

PDF Cite Code Dataset Poster Slides

Wenchong He, Minh N. Vu, Zhe Jiang, My Thai (2022). An Explainer for Temporal Graph Neural Networks. In GLOBECOM 2022.

PDF Cite Code Dataset Poster Slides

Wenchong He, Zhe Jiang, Marcus Kriby, Yiqun Xie, Xiaowei Jia, Da Yan, Yang Zhou (2022). Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery. In KDD 2022.

PDF Cite Code Dataset Poster Slides