Yunchuan Liu

Email: yliu3 at govst dot edu

Office Room: D34150C

I am now an assistant professor at the Governors State University. My research interests are Machine Learning, Data Mining, Embedded System, and Power System.

I received my Ph.D. degree from University of Nevada, Reno and my major is Computer Science and Engineering. My advisors is Prof. Lei Yang. I received my Bachelor and Master degree from Shenzhen University.

News

Apr 2026: I have been selected as an Illinois Innovation Network(IIN) Fellow with topic of 'Conduct an analysis and recommendations for University preparedness, planning, and mitigation for energy and water impacts of data center projects across the IIN'.

Nov 2025: Our paper 'Automatic Labeling of Real-world PMU Data: A Weakly Supervised Learning Approach', has been accepted for publication in the journal Electronics and will be included in the special issue 'Machine Learning for Data Mining'.

Mar 2025: Our drone successfully completed its test flight and our internship work was covered by the media.

Oct 2024: Our proposal 'MediValAir: Innovative Drone Delivery System for Hospitals' is awarded by Illinois Innovation Voucher grant.

Jun 2024: Our paper 'Related Technological Density and Regional Industrial Upgrading from Perspective of Product Space Theory: Evidence from China' has been accepted by Applied Economics.

Feb 2023: Our paper 'Towards distributed learning of PMU data: A federated learning based event classification approach' has been accepted by IEEE PES General Meeting 2023.

Jan 2023: Our paper 'Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast' has been published in Future Internet.

Aug 2022: Yunchuan joined the Division of Science Mathematics and Technology at Governors State University as an Assistant Professor.

Jun 2022: Our paper 'Weakly supervised event classification using imperfect real-world PMU data with scarce labels' has been selected as one of the Best Conference Papers by IEEE PES General Meeting 2022.

May 2022: Our paper 'Robust event classification using imperfect real-world PMU data' has been accepted by IEEE Internet of Things Journal.

Feb 2022: Our paper 'Real-time event detection using rank signatures of real-world PMU data' has been accepted by IEEE PES General Meeting 2022.

Apr 2021: Our paper 'Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements' has been accepted by North American Power Symposium (NAPS) 2021.

Feb 2021: Our papers 'PMU-data-driven event classification in power transmission grids and Low-rank tensor completion for PMU data recovery' have been accepted by IEEE PES ISGT NA 2021.

Nov 2020: Our paper 'Seasonal self-evolving neural networks based short-term wind farm generation forecast' has been accepted by IEEE SmartGridComm 2020.

Oct 2020: Our paper 'A Regularized Tensor Completion Approach for PMU Data Recovery' has been accepted by IEEE Transactions on Smart Grid.

Teaching

University of Nevada, Reno

CS135 COMPUTER SCIENCE I

CS202 COMPUTER SCIENCE II

Governors State University

CPSC-3142 Intro to C++

CPSC-3310 Intro to Object-oriented Programming(Python)

CPSC-6548 Computer Programming: Java

CPSC-4792/6792 Language Model And Processing

CPSC-6780 Big Data Processing and Analytics

CPSC-6790 Data Mining and Business Intelligence

CPSC-8810 Formal Languages and Automata

CPSC-8845 Advanced Database Concepts

CPSC-8985 Grad Seminar

Publications

Journals

[J08] Liu, Y., Yang, L., & Zhang, J. (2025). Automatic Labeling of Real-World PMU Data: A Weakly Supervised Learning Approach. Electronics, 14(23), 4703.
[J07] Cheng, H., Song, M., & Liu, Y. (2024). Related technological density and regional industrial upgrading from perspective of product space theory: evidence from China. Applied Economics, 56(48), 5774-5788.
[J06] Liu, Y., Ghasemkhani, A., & Yang, L. (2023). Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast. Future Internet, 15(1), 17.
[J05] Liu, Y., Yang, L., Ghasemkhani, A., Livani, H., Centeno, V. A., Chen, P. Y., & Zhang, J. (2022). Robust Event Classification Using Imperfect Real-world PMU Data. IEEE Internet of Things Journal.
[J04] Ghasemkhani, A., Niazazari, I., Liu, Y., Livani, H., Centeno, V. A., & Yang, L. (2020). A regularized tensor completion approach for pmu data recovery. IEEE Transactions on Smart Grid, 12(2), 1519-1528.
[J03] Liu, Y., & Gong, X. (2013). Processing and Hardware Implementation of BT. 656 Digital Video Stream. Chinese Journal of Liquid Crystals and Displays, 28(2), 238-243.
[J02] Cheng,Z. , Liu, Y., & Gong, X. Design of USB Transmission System for Microprojection Video Signal. Chinese Journal of Liquid Crystals and Display. 2012, 27(1) 81-86.
[J01] Liu, Y., Gong, X.,& Wu, Q. SPI IP Core and Its Application in Microprojection System. Microcontroller and Embedded Systems. 2011,(2): 27-30.

Conferences (Full Paper Refereed)

[C09] Mohammadabadi, S. M. S., Liu, Y., Canafe, A., & Yang, L. (2023, July). Towards distributed learning of pmu data: A federated learning based event classification approach. In 2023 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1-5). IEEE..
[C08] Ghasemkhani, A., Liu, Y., & Yang, L. Real-time event detection using rank signatures of real-world PMU data. In 2022 IEEE Power & Energy Society (PES) General Meeting (pp. 1-5). IEEE.
[C07] Liu, Y.,& Yang, L. Weakly supervised event classification using imperfect real-world PMU data with scarce labels. In 2022 IEEE Power & Energy Society (PES) General Meeting (pp. 1-5). IEEE.(Best Paper Award)
[C06] Canafe, A., Liu, Y., Yang, L., & Livani, H. (2022, March). DCCA Enhanced Forced Oscillation Frequency Detection Using Real-world PMU Data. In 2022 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE.
[C05] Niazazari, I., Livani, H., Ghasemkhani, A., Liu, Y., & Yang, L. (2021, April). Event cause analysis in distribution networks using synchro waveform measurements. In 2020 52nd North American Power Symposium (NAPS) (pp. 1-5). IEEE.
[C04] Ghasemkhani, A., Liu, Y., & Yang, L. (2021, February). Low-rank Tensor Completion for PMU Data Recovery. In 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.
[C03] Niazazari, I., Livani, H., Ghasemkhani, A., Liu, Y., & Yang, L. (2021, April). Event cause analysis in distribution networks using synchro waveform measurements. In 2020 52nd North American Power Symposium (NAPS) (pp. 1-5). IEEE.
[C02] Liu, Y., Ghasemkhani, A., Yang, L., Zhao, J., Zhang, J., & Vittal, V. (2020, November). Seasonal self-evolving neural networks based short-term wind farm generation forecast.
In 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
[C01] Liu, Y., Huo, Y., and Niu, H. (2015, December). A method for reducing the sidelobes in superoscillation imaging. In MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis (Vol. 9811, pp. 76-81). SPIE.