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Guangzhi Qu, Ph.D.

School of Engineering and Computer Science

Engineering Center, Room 301
115 Library Drive
Rochester , MI 48309-447
(location map)
Dean's Office (248) 370-2217
Academic Advising (248) 370-2201

Guangzhi Qu Headshot

Computer Science and Engineering Department
phone (248) 370-2690
[email protected]

Ph.D., University of Arizona


  • Operating Systems
  • Artificial Intellingence
  • Machine Learning


  • Artificial Intelligence
  • Applied Machine Learning
  • Software Analysis
  • Embedded Systems
  • Multicore Computing
  • Discrete Event Simulation

Selected Publications

  1. Feng Zhang, Erkang Xue, Ruixin Guo, Guangzhi Qu, Gansen Zhao, Alber Y. Zomaya, “DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization”, IEEE Transactions on Parallel and Distributed Systems pp: 1289-1302, vol.33, issue: 6, 2022.
  2. Zijun Han, Guangzhi Qu, Bo Liu, Feng Zhang, “Exploit Data Level Parallelism and Schedule Dependent Tasks on Multi-core Processors”, Information Sciences, vol. 585 pp 382-394, 2022
  3. Xi He, Bo Liu, Jianqing Li, Guangzhi Qu, Jianlei Lang, Rentao Gu, “A Method for Mining Granger Causality Relationship on Atmospheric Visibility”, ACM Transactions on Knowledge Discovery from Data. Vol. 15 (5), page 1-16, 2021
  4. Xinya Lei, Ruixin Guo, Feng Zhang, Lizhe Wang, Rui Xu, Guangzhi Qu, "Optimizing FHEW with Heterogeneous High Performance Computing", IEEE Transactions on Industrial Informatics, Vol.16(8), page 5335-5344, 2019.
  5. Bo Liu, Shuo Yan, Jianqiang Li, Guangzhi Qu, Yong Li, Jianlei Lang, Rentao Gu, "A Sequence-to-Sequence Air Quality Predictor Based on the n-step Recurrent Prediction", IEEE Access, Vol.7, page 43331-43345, 2019.
  6. Ruixin Guo, Erkang Xue, Feng Zhang, Gansen Zhao, Guangzhi Qu, “Optimizing the Confidence Bound of Count-min Sketches to Estimate the Streaming Big Data Query Results More Precisely”, Computing, 2019. 10.1007/s00607-018-00695-z
  7. Paula Lauren, Guangzhi Qu, Jucheng Yang, Paul Watta, Guang-bin Huang, Amaury Lendasse, “Generating Word Embeddings from an Extreme Learning Machine for Sentiment Analysis and Sequence Labeling Tasks”, Cognitive Computation, 2018.
  8. Paula Lauren, Guangzhi Qu, Feng Zhang, Amaury Lendasse, “Discriminant document embeddings with an extreme learning machine for classifying clinical narratives”, Neurocomputing, 2017
  9. Feng Zhang, Ti Gong, Victor E. Lee, Gansen Zhao, Chunming Rong, Guangzhi Qu, “Fast algorithms to evaluate collaborative filtering recommender systems”, Knowledge-Based Systems, vol 96, March 15th, 2016.
  10. “Local Analgesia Adverse Effects Prediction using Multi-label Classification,” Neurocomputing, vol. 92, pp. 18-27, 2012.
  11. “Complex Networks Properties Analysis for Mobile Ad hoc Networks,IET Communications, vol. 6, Issue 4, pp.370-380, 2012.
  12. “Bucket Learning: Improving Model Quality through Enhancing Local Patterns, Knowledge-based System,” Available online 2 October, 2011, ISSN 0950-7051, 10.1016/j.knosys.2011.09.013.
  13. “A Weighted-Graph-Based Approach for Diversifying Search Results,” International Journal on Knowledge and Web Intelligence, 2011 - Vol. 2, No.1 pp. 15-35.
  14. “Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine,” IEEE the 9th International Conference on Machine Learning and Applications, 2010.