Guangchi Liu 刘光迟, Ph.D.
Professor, Ph.D. Advisor 教授，博士生导师
School of Computer Science and Engineering
Southeast University, Nanjing, China
Office: 160, Computer Building, Jiulonghu Campus
My name is Guangchi (Luke) Liu and I am currently a professor of computer science at Southeast Univesity. I received my B.S. and M.S. degree from Southeast Univesity and Ph.D. in in Compuer Science from Montana State University, U.S.A under the supervision of Dr. Qing Yang. My research interests lie in the areas of Social Computing and Security, Trust Network, Machine Learning and Natural Language Processing, etc.
I used to be an industry research lead in the U.S. for 5 years. Under my supervision, my team prototyped and productionized numerious social media and opinion analysis systems, which have been used by more than 100 companies around the world. More details can be found at my personal webiste https://gc-liu.github.io/ .
Social Computing and Security, Trust Network, Natrual Language Processing, Machine Learning and Internet of Things.
I'm actively looking for strongly self-motivated undergraduate, master, and Ph.D. researchers. Please send me your CV if you are interested in joining us! All of the team members I have mentored are currently working at well-known tech/financial companies including Google, Meta, Amazon, Microsoft, Anasys, JP Morgan, Deloitte, etc. or pursuing Ph.D. degree at colleges including North Carolina State Univ., Fudan Univ., etc.
 Guangchi Liu, Qing Yang, Honggang Wang and Alex X. Liu, “Three-Valued Subjective Logic: A Model for Trust Assessment in Online Social Networks”. IEEE Transactions on Dependable and Secure Computing (TDSC) (2021).
 Guangchi Liu, Chenyu Li and Qing Yang, “NeuralWalk: Assessing Trust in Online Social Networks with Neural Network” in IEEE International Conference on Computer Communications (INFOCOM) 2019.
 Guangchi Liu, Qi Chen, Qing Yang, Binhai Zhu, Honggang Wang and Wei Wang. “OpinionWalk: An Efficient Algorithm for Massive Trust Assessment in Online Social Networks” in IEEE International Conference on Computer Communications (INFOCOM) 2017.
 Guangchi Liu, Qing Yang, Honggang Wang, Xiaodong Lin, and Mike P. Wittie. “Assessment of Multi-Hop Interpersonal Trust in Social Networks by Three-Valued Subjective Logic” in IEEE International Conference on Computer Communications (INFOCOM) 2014.
 Guangchi Liu, Qing Yang, Honggang Wang, Shaoen Wu, and Mike P. Wittie. “Uncovering the Mystery of Trust in An Online Social Network ” in IEEE Conference on Communications and Network Security (CNS) 2015.
 Tong Cheng, Guangchi Liu and Qing Yang, “Trust Assessment in Vehicular Social Network based on Three- Valued Subjective Logic”. IEEE Transactions on Multimedia (TMM) (2019).
 Xiaofei Niu, Guangchi Liu, and Qing Yang. “Trustworthy Website Detection Based on Social Hyperlink Network Analysis.” IEEE Transactions on Network Science and Engineering (TNSE) (2018).
 Xiaofei Niu, Guangchi Liu and Qing Yang, “OpinionRank: Trustworthy Website Detection using Three Valued Subjective Logic”. IEEE Transactions on Big Data (TBD) (2020).
 Mehdi Assefi, Ehsun Behravesh, Guangchi Liu and Ahmad Pahlavan Tafti. “Big Data Machine Learning using Apache Spark MLlib” in IEEE BigData (2017).
 Qi Chen, Ye Liu, Guangchi Liu, Qing Yang, Xianming Shi, Hongwei Gao, Lu Su and Quanlong Li. “Harvest Energy from the Water: A Self-Sustained Wireless Water Quality Sensing System” in ACM Transactions on Embedded Computing Systems (TECS) (2017).
 Ye Liu, Qi Chen, Guangchi Liu and Qing Yang. “EcoSense: A Hardware Approach to On-Demand Sensing in the Internet of Things” in IEEE Communication Magazine (2016).
Project I: Dissemination of Multimodal Information in Directed Graph
Keywords: Graph Machine Learning, Graph Signal Processing, White-box Neural Network
Based upon the latest graph machine learning and interpretable machine learning approaches, this research aims to design a data-driven yet interpretable framework towards modeling multimodal information dissemination in network scenarios. Applications of this research include opinion dissemination modeling, trust network data verification, etc.