I am Xiongye Xiao, a PhD candidate in Electrical and Computer Engineering (ECE) at the University of Southern California (USC). My research focuses on AI for Science, Network Science, and Machine Learning, with a goal of developing advanced physical, mathematical, and computational tools to study multiscale structures and dynamics and map them to functions. My work primarily involves complex network analysis, neural operators, multimodal learning, and generative modeling, while also exploring their applications in various scientific fileds including neuroscience and materials science.
Spanning network gels from nanoparticles and graph theoretical analysis of their structure and properties
Advanced Materials , 2022
Drew A. Vecchio, Mark D. Hammig, Xiongye Xiao, Anwesha Saha, Paul Bogdan, and Nicholas A. Kotov.
Unifying Structural Descriptors for Biological and Bioinspired Nanoscale Complexe
Nature Computational Science , 2022
Minjeong Cha, Emine Turali-Emre, Xiongye Xiao, Ji-Young Kim, Paul Bogdan, Scott VanEpps, Angela Violi, Nicholas A. Kotov.
Deciphering the generating rules and functionalities of complex networks
Nature Scientific Reports , 2021
Xiongye Xiao, Hanlong Chen, and Paul Bogdan
Generator based approach to analyze mutations in genomic datasets
Nature Scientific Reports , 2021
Siddharth Jain†, Xiongye Xiao†, Paul Bogdan, and Jehoshua Bruck
A COVID-19 Rumor Dataset
Frontiers in Psychology , 2021
Mingxi Cheng, Songli Wang, Xiaofeng Yan, Tianqi Yang, Wenshuo Wang, Zehao Huang, Xiongye Xiao, Shahin Nazarian, and Paul Bogdan
Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data
Nature Scientific Reports , 2020
Chenzhong Yin†, Xiongye Xiao†, Valeriu Balaban, Mikhail E. Kandel, Young Jae Lee, Gabriel Popescu, and Paul Bogdan
Biomorphic structural batteries for robotics
Science Robotics , 2020
Mingqiang Wang, Drew Vecchio, Chunyan Wang, Ahmet Emre, Xiongye Xiao, Zaixing Jiang, Paul Bogdan, Yudong Huang, and Nicholas A. Kotov
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
International Conference on Learning Representations (ICLR), 2023
Xiongye Xiao, Defu Cao, Ruochen Yang, Gaurav Gupta, Gengshuo Liu, Chenzhong Yin, Radu Balan, Paul Bogdan
Non-Linear Operator Approximations for Initial Value Problems
International Conference on Learning Representations (ICLR), 2022
Gaurav Gupta, Xiongye Xiao, Radu Balan, and Paul Bogda
Multiwavelet-based operator learning for differential equations
dvances in Neural Information Processing Systems (NeurIPS), 2021 [Spotlight, top 2%]
Gaurav Gupta, Xiongye Xiao, Radu Balan, and Paul Bogda
AI and the Clinical Immunology/Immunoinformatics for COVID-19
Artificial Intelligence in Covid-19
Zikun Yang, Xiongye Xiao, and Paul Bogdan