I am an Assistant Professor of Computer Science & Engineering at Mississippi State University, focusing on dynamics over graphs (see details of my research interests). I obtained my Ph.D. at Virginia Tech in 2020, under the supervision of Dr. Chang-Tien Lu. For students interested in me, check ways to work with me. If interested in my courses, please check the course syllabi.
Graph Theory & LLM starting in Spring 2025. Fill this form to apply.- [Tutorial] 2/25: Will give our tutorial on MT11 Unifying Spectral and Spatial Graph Neural Networks at SIAM Data Mining 25 at D.C.
- [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.
[Papers]7/24: 1 paper accepted at IJCAI 24 aboutTrajectory Mining on Graph, 2 papers accepted at EMBC 2024 aboutAntimicrobial Resistance Prediction with Genetic GraphandGeneration of Synthetic Microbiomes, 1 paper accepted at PAKDD onAppliances Graph for Energy Disaggregation, 1 paper accepted at ICTAI onNeural Tangent Bayesian Optimization for Graph Dynamics.- [Grant] 6/24: We will start working on the spatial epidemiology of animal disease with USDA scientists under USDA-ARS funded project.
- [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
Research Interest: My research focuses on graph dynamics, leveraging methodologies such as spectral graph theory, uncertainty quantification, higher-order analysis, and physics-inspired approaches. Currently, I am particularly interested in the dynamics of coupled and interdependent networks. My collaborations span diverse applications, including Social network 1, Transportation 2, Spatial epidemiology, ecological network 3, Brain network 4, Genetics 5, Circuit 6. Also, I have been working on spectral method 7, Uncertainty Quantification 8, LLM/transformers, AI education 9
Working with MS State faculty with crimiology expertise in cyberbully, hate speech in NSF projects: Advancing Speech Detection: A Hybrid Approach Using Large Language Models and Graph Neural Networks, and Synergistic Graph Flow Analytics: An Integrated Infrastructure for Bridging Complexity, Fragmentation, and Interdisciplinary Gaps ↩︎
traffic modeling, Graph Convolutional Networks with Kalman Filtering for Traffic Prediction, redistricting Geospatial clustering for balanced and proximal schools ↩︎
with USDA ecologists in funded projects. ↩︎
with MS State faculty and Univ. of Mississippi Medical Center (UMMC) on brain signals of ADHD & sleep ↩︎
with MS State College of Veterinary Medicine, EMBC paper ↩︎
UC Davis & GMU, DATE paper ↩︎
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks ↩︎
AAAI 24: Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization, SDM 23: Understanding Influence Maximization via Higher-Order Decomposition ↩︎
NSF project: Learning to create Intelligent Solutions with Machine Learning and Computer Vision: A Pathway to AI Careers for Diverse High School Students ↩︎
Grants, Awards & Honors
Selected
Contact Me
- zchen@cse.msstate.edu OR chen.zhiqian.work@gmail.com
- 304 Butler Hall, 665 Perry Street, MS State, MS 39762
