Motto
Since ancient times all people of accomplishment and great learning must have experienced three kinds of stages:
The first state can be described by,
Last night the west breeze
blew withered leaves off trees.
I mount the tower high
and strain my longing eye.
The second state can be described by,
I won’t regret
even if the belt on my robe grows looser;
For you
it’s worth being wan and haggard.
The third state can be described by,
A thousand times
I search for her in the crowd,
and, suddenly turning my head,
discover her where the lantern lights are dim.
Last night the west breeze
blew withered leaves off trees.
I mount the tower high
and strain my longing eye.
The second state can be described by,
I won’t regret
even if the belt on my robe grows looser;
For you
it’s worth being wan and haggard.
The third state can be described by,
A thousand times
I search for her in the crowd,
and, suddenly turning my head,
discover her where the lantern lights are dim.
--王国维
About Me
I am Ying Pang (庞颖 in Chinese), a final-year PhD student
in Computer Science Department at
University of Otago. Currently, I am studying in Systems Research Group (SRG) and supervised by
Haibo Zhang, Jeremiah D. Deng, and Lizhi Peng (external).
My research primarily focuses on distributed optimization, federated learning, robust learning with label noise, and imbalanced learning. Currently, I am working on designing,
analyzing, and evaluating collaborative learning frameworks that address real-world constraints, including heterogeneity,
communication issues, privacy concerns, interpretability, and robustness to label noise.
Publications
Journal
2024
- Imbalanced Ensemble Learning Leveraging a Novel Data-Level Diversity Metric Ying Pang, Lizhi Peng*, Haibo Zhang, Zhenxiang Chen, and Bo Yang Pattern Recognition, 2024 [Rank A*, Top 10%, Q1, IF: 7.5]
2023
- Collaborative Learning with Heterogeneous Local Models: A Rule-based Knowledge Fusion Approach Ying Pang, Haibo Zhang*, Jeremiah D. Deng, Lizhi Peng, and Fei Teng IEEE Transactions on Knowledge Discovery and Engineering (IEEE TKDE), 2023 [Rank A*, Top 10%, Q1, IF: 8.9]
2021
- Similarity-Evaluation-Based Evolving of Flexible Neural Trees for Imbalanced Classification Min Qiu, Lizhi Peng*, Ying Pang, Bo Yang, and Panpan Li Applied Soft Computing, 2021 [Top 10%, Q1, IF: 7.2]
2019
- Imbalanced Learning Based on Adaptive Weighting and Gaussian Function Synthesizing with an Application on Android Malware Detection Ying Pang, Lizhi Peng*, Zhenxiang Chen, Bo Yang, and Hongli Zhang In (Elsevier) Information Sciences, 484(1), pp. 95-112, 2019. [Rank A, Top 10%, Q1, IF: 8.1]
Conference
2022
- Rule-Based Collaborative Learning with Heterogeneous Local Learning Models Ying Pang, Haibo Zhang*, Jeremiah D. Deng, Lizhi Peng, and Fei Teng The 26th pacific-asia conference on knowledge discovery and data mining (PAKDD), 2022 [Rank B, Acceptance rate: 19.3%]
2019
- A Signature-Based Assistant Random Oversampling Method for Malware Detection
Ying Pang, Zhenxiang Chen*, Lizhi Peng, and Bo Yang
The 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2019.
[Rank B]
2017
- Finding Android Malware Trace from Highly Imbalanced Network Traffic Ying Pang, Zhenxiang Chen*, Xiaomei Li, and Bo Yang The 20th IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)), 2017