陈枳扦

陈枳扦 Chen, ZhiQian

Assistant Prof.

Mississippi State Univ.

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.

Check our tutorials on Unifying Spectral and Spatial Graph Neural Network at SIAM MDS 24’ (slides), CIKM 24’ (slides), and CVPR 24’ (slides). This tutorial is based on our ACM Computing Survey paper, and see more related papers at Awesome Spectral GNN.
Hiring PhD student to work on 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 advance speech recognition in English and Spanish w/ graph learning & LLMs: NSF Link.
  • [Grant] 7/24: Received a NSF CNS/CIRC fund (PI) to develop research infrastructure for graph dynamics: NSF Link.
  • [Papers] 7/24: 1 paper accepted at IJCAI 24 about Trajectory Mining on Graph, 2 papers accepted at EMBC 2024 about Antimicrobial Resistance Prediction with Genetic Graph and Generation of Synthetic Microbiomes, 1 paper accepted at PAKDD on Appliances Graph for Energy Disaggregation, 1 paper accepted at ICTAI on Neural 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: NSF Link. 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) for interpretable graph dynamics: NSF Link
More News

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

research interests
research interests

Grants, Awards & Honors

NSF
NSF CNS
Co-PI - MSI: Detect hate speech in English and Spanish with graph learning + LLM.
See certificate
NSF
NSF EDU
Co-PI - ITEST: Develop AI education program for K-12 in Mississippi.
See certificate
NSF
NSF CNS
PI - CIRC: Develop Grand Theory for Graph Dynamics.
See certificate
NSF
NSF REU - Supplement
Sole-PI: Develop undergraduate research experience on graph dynamics.
See certificate
USDA
USDA-ARS
Co-PI - Developing Detection and Modeling Tools for the Geospatial and Environmental Epidemiology of Animal Disease.
NSF
NSF IIS
Sole PI - CRII: Interpretable Influence Propagating and Blocking on Graphs.
See certificate
USDA
USDA-ARS
Co-PI - Advancing Agricultural Research through High Performance Computing

Selected

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Higher-order Relation in Seeds
How remote seeds can implicitly ``interact’’ with each other in the influence maximization problem?
Higher-order Relation in Seeds
An Unified Framework for Graphs
Is there an unfying framework for all types of graphs, including spectral and spatial, also, directed, higher-order, and dynamic graphs?
An Unified Framework for Graphs
Graph Bayesian Optimization
How to conduct Bayesian optimization over graph problems so as to reduce data use?
Graph Bayesian Optimization
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
A textbook for practitioners and students in computer science and data analysis
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
Graph Learning with Kalman Filtering
Use Kalman filtering to handle uncertainty.
Graph Learning with Kalman Filtering
Graph Learning on Circuits
Ues graph neural network to model circuit, and predict its encryption attribute.
Graph Learning on Circuits
Graph Learning on Street Views
Use streetview to predict the crime statistics.
Graph Learning on Street Views

Contact Me

  • zchen@cse.msstate.edu OR chen.zhiqian.work@gmail.com
  • 304 Butler Hall, 665 Perry Street, MS State, MS 39762