陈枳扦 Chen, ZhiQian

Assistant Prof.

Mississippi State Univ.

I am an Assistant Professor of Computer Science and Engineering at Mississippi State University, currently focusing on dynamic behaviors over graphs and networks (see my research interests). For students interested in my research, please explore the opportunities to work with me. If you are interested in my courses, please preview the course syllabi.

Join us at SIAM MDS 24’ and CIKM 24’ for our tutorial on Unifying Spectral and Spatial Graph Neural Network

I am seeking to hire 1 PhD student to work on Graph Theory and Large Language Models (LLMs) in the context of English and Spanish social networks, with the position starting in Spring 2025.

Assistantship applicants should complete this Form to apply. Student worker applicants use this Form.

[Grant] 8/24: Received a NSF CNS/MSI fund (co-PI) to detect hate speech in English and Spanish w/ graph learning & LLMs: Advancing Speech Detection: A Hybrid Approach Using Large Language Models and Graph Neural Networks.
[Grant] 7/24: Received a NSF CNS/CIRC fund (PI) to develop grand theory for graph dynamics: Synergistic Graph Flow Analytics: An Integrated Infrastructure for Bridging Complexity, Fragmentation, and Interdisciplinary Gaps.
[Grant] 6/24: We will start working on the spatial epidemiology of animal disease with USDA scientists under USDA-ARS funded project.
[Tutorial] 6/24: Check our CVPR 24’ tutorial on Unifying Spectral and Spatial Graph Neural Network. See the tutorial website CVPR 24’ Tutorial.
[Grant] 4/24: Received a NSF EDU/ITEST fund (Co-PI) to conduct AI education research: Learning to create Intelligent Solutions with Machine Learning and Computer Vision: A Pathway to AI Careers for Diverse High School Students. See media report.
[Honor] 4/24: Received Excellent Reviewer of the IEEE Transactions on Network Science and Engineering Journal.
[Grant] 1/24: Received NSF IIS/III REU supp to fund (PI) undergraduate student research.
[Paper] 12/23: 2 papers are accepted by AAAI 24’ proposing Graph Bayesian Optimization to conduct information propagation efficiently: Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization, and Graph Bayesian Optimization for Multiplex Influence Maximization
[Paper] 12/23: Our theoretical framework for spectral GNN accepted by ACM Computing Survey, see paper, related work and slides.
[Grant] 12/23: Received a Seed Grant from the International Institute to develop collaboration with the U. of Auckland in New Zealand.
[Grant] 12/23: Launch a working group on Graph AI doing cross-disciplinary study. Thanks to BCoE.
[Tool] 8/23: XFlow is released, which targets modeling generalized graph flows
[Grant] 8/23: Received support from USDA-ARS funded project (Co-PI) on disease genetics. Special thanks to CVM@MSState. See our storymap.
[Paper] 12/22: One paper is accepted by SIAM Data Mining (SDM) 23’ Understanding Influence Maximization via Higher-Order Decomposition: how seeds interact in higher-order perspective
[Textbook] 7/22: Our textbook published by Springer Nature provides numerous code examples, Springer Nature, Amazon
[Grant] 4/22: Received NSF’s IIS/III fund (PI): CRII: Interpretable Influence Propagating and Blocking on Graphs

More News

[Paper] 12/21: One paper about Graph Wavelet is accepted by SIAM Data Mining (SDM) 22’ with SIAM Early Career Travel Award
[Tutorial] 5/21: See our tutorial Spreading Model for Epidemics (GNN, RNN, SIR and PDE) in SDM 21’

[Paper & Award] 9/20: Papers on Kalman filtering and school redistricting are accepted by ACM SIGSPATIAL 20’ with (Best Paper Award)
[Paper] 12/19: Unsupervised learning for material discovery is published in Nature Communications.
- Selected as 22nd of Top 50 Chemistry and Materials Sciences Articles Top 50 Collection