🧑‍🎓 About Me

• Hi there!
• This is Zhidi Lin (林志地). I am a Ph.D. student in Computer and Information Engineering at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), under the supervision of Prof. Feng Yin (main-supervisor) and Prof. Shuguang Cui (co-supervisor). Prior to my Ph.D. studies, I received my M.Sc. degree from Xiamen University in 2019, under the supervision of Prof. Qi Yang and Prof. Xuemin Hong. From Aug. 2017 to Mar. 2019, I was a research intern at Shenzhen Research Institute of Big Data (SRIBD), working with Prof. Dongliang Duan and Prof. Liuqing Yang.
• I am affiliated with the Bayesian Learning for Signal Processing (BLSP) Group of CUHK-Shenzhen, where my research mainly focuses on data-driven modeling, Approximate Bayesian Inference, Gaussian processes, and related applications.

Miscellanies

• Languages – Mandarin (native), English (fluent), Hokkien (native)
• Sports – Badminton 🏸, Swimming 🏊‍♂️, …
• Leadership/Community activities
  • Residential Tutor, Diligentia College, CUHK-Shenzhen, China.

🔥 News

• 2023.02: Paper entitled “Output-Dependent Gaussian Process State-Space Model” has been accepted by IEEE ICASSP 2023.
• 2022.05: Paper entitled “Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data” has been accepted by IEEE FUSION 2022.
• 2022.01.05: I pass the Ph.D. Qualifying Examination and become a Ph.D. candidate.
• 2021.02: Paper entitled “Graph Neural Network for Large-Scale Network Localization” has been accepted by IEEE ICASSP 2021.
• 2020.11.06: Review paper (29-page) entitled “FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing” has been published in IEEE Open Journal of Signal Processing.
• 2020.05: Paper entitled “An Interpretable and Sample Efficient Deep Kernel for Gaussian Process” has been accepted by UAI 2020.

💻 Projects

• coming soon

📚 Publications

Journal

• Fedloc: Federated learning framework for data-driven cooperative localization and location data processing
  Feng Yin, Zhidi Lin, Qinglei Kong, Yue Xu, Deshi Li, Sergios Theodoridis and Shuguang Cui. IEEE Open Journal of Signal Processing, vol. 1, pp. 187-215, November 2020.
  

• One-Class classifier based fault detection in distribution systems with varying penetration levels of distributed energy resources
  Zhidi Lin, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang and Shuguang Cui. IEEE Access, vol. 8, pp. 130023-130035, July 2020.
  

• Data-driven fault localization in distribution systems with distributed energy resources
  Zhidi Lin, Dongliang Duan, Qi Yang, Xuemin Hong, Xiang Cheng, Liuqing Yang and Shuguang Cui. Energies, vol. 13, no. 1, pp. 275, January 2020.
  

Conference

• Output-dependent Gaussian process state-space model [code]
  Zhidi Lin, Lei Cheng, Feng Yin, Lexi Xu and Shuguang Cui. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
  

• Gaussian process regression with grid spectral mixture kernel: Distributed learning for multidimensional data [code]
  Richard Cornelius Suwandi*, Zhidi Lin*, Yiyong Sun, Zhiguo Wang, Lei Cheng, and Feng Yin. The 25th International Conference on Information Fusion (FUSION), 2022.
  

• Graph neural network for large-scale network localization [code]
  Wenzhong Yan, Di Jin, Zhidi Lin, Feng Yin. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
  

• An interpretable and sample efficient deep kernel for Gaussian process
  Yijue Dai, Tianjian Zhang, Zhidi Lin, Feng Yin, Sergios Theodoridis, Shuguang Cui. Conference on Uncertainty in Artificial Intelligence (UAI), 2020.
  

• A flexible approach for human activity recognition based on broad learning system
  Zhidi Lin, Haipeng Chen, Qi Yang, Xuemin Hong. International Conference on Machine Learning and Computing (ICMLC), 2019.
  

• Data-driven fault localization in distribution systems with distributed energy resources
  Zhidi Lin, Dongliang Duan, Qi Yang, Xiang Cheng, Liuqing Yang, Shuguang Cui. IEEE Sustainable Power and Energy Conference, (iSPEC), 2019.
  

• One-class classifier based fault detection in distribution systems with distributed energy resources
  Zhidi Lin, Dongliang Duan, Qi Yang, Xiang Cheng, Liuqing Yang, Shuguang Cui. IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018.
  

• Online MEM based binary classification algorithm for China Mobile imbalanced dataset
  Shuqing Lin, Feng Yin, Zhidi Lin, Yanbin Lin, Shuguang Cui, Teng Li, Fengli Yu, Wei Yu, Xuemin Hong, Jianghong Shi, Zhi-Quan Luo. IEEE/CIC International Conference on Communications in China (ICCC), 2018.
  

(* indicates equal contributions).

🏫 Services

• Journal Reviewer of Signal Processing, Elsevier
• Conference Reviwer of ICML, NeurIPS, UAI, ICASSP, FUSION

👨‍🏫 Teaching

• Spring 2023: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, MAT2040 Linear Algebra. Instructor: Prof. Dongdong He

• Fall 2022: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, MAT2040 Linear Algebra. Instructor: Prof. Feng Yin

• Summer 2022: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, MAT2040 Linear Algebra. Instructor: Prof. Chuan Huang

• Spring 2022: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, STA3010 Regression Analysis. Instructor: Prof. Feng Yin

• Fall 2021: Teaching Assistant, CUHK-Shenzhen. Graduate Course, CIE6133/MCE5919 Gaussian Process for Machine Learning and Signal Processing. Instructor: Prof. Feng Yin

• Spring 2021: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, STA3010 Regression Analysis. Instructor: Prof. Feng Yin

• Fall 2020: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, MAT3280 Probability Theory. Instructor: Prof. Kenneth Shum

• Spring 2020: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, STA3010 Regression Analysis. Instructor: Prof. Feng Yin

• Fall 2019: Teaching Assistant, CUHK-Shenzhen. Undegrad Course, MAT3280 Probability Theory. Instructor: Prof. Kenneth Shum

• Spring 2017: Teaching Assistant, Xiamen University. Undegrad Course, Algorithm Design & Analysis. Instructor: Prof. Defu Zhang