Songhua Wu Quant Researcher INNO Asset Management Ph.D. in Machine Learning Sydney AI Centre School of Computer Science The University of Sydney Email: songhua.wu [at] sydney.edu.au

Biography

I am a quant researcher at INNO asset management in Beijing.
I received the Ph.D. degree from the School of Computer Science at The University of Sydney (USYD), Sydney in 2023.
I received the B.E. degree from The University of Science and Technology of China (USTC), Hefei, China, in 2019.
My research interests include learning with noisy labels; weakly supervised learning; causal machine learning; fairness.

Education Background

• Ph.D., 2020.03 - 2023.12
  The University of Sydney, Australia, advised by Prof. Tongliang Liu
  Thesis: Learning with Noisy Labels Incorporating Fairness and Privacy Concerns [PDF]

• B.Eng., 2015.09 - 2019.06
  The University of Science and Technology of China, Hefei, China

Publications

• A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels. [PDF] [CODE]
  Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI 2024)
• Learning from Noisy Pairwise Similarity and Unlabeled Data. [PDF] [CODE]
  Songhua Wu, Tongliang Liu, Bo Han, Jun Yu, Gang Niu, Masashi Sugiyama
  Journal of Machine Learning Research. (JMLR 2022)
• Fair Classification with Instance-dependent Label Noise. [PDF] [CODE]
  Songhua Wu, Mingming Gong, Bo Han, Yang Liu, Tongliang Liu
  Conference on Causal Learning and Reasoning (CLeaR 2022)
• Bridging the Gap between Few-Shot Learning and Many-Shot Learning via Distribution Calibration. [PDF] [CODE]
  Shuo Yang, Songhua Wu, Tongliang Liu, Min Xu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI 2021)
• A Parametrical Model for Instance-Dependent Label Noise. [PDF] [CODE]
  Shuo Yang, Songhua Wu, Erkun Yang, Bo Han, Yang Liu, Min Xu, Gang Niu, Tongliang Liu
  IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI 2023)
• Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels. [PDF] [CODE]
  Songhua Wu*, Xiaobo Xia*, Tongliang Liu, Bo Han, Mingming Gong, Nannan Wang, Haifeng Liu, Gang Niu
  International Conference on Machine Learning (ICML 2021)

Research Experiences

• Research Intern, Peng Cheng Laboratory, Shenzhen, China, 09/2019 - 01/2020
  Federated learning, AutoML [Zhihu]

Teaching Experiences

• Teaching Assistant, USYD COMP5328 (Advanced Machine Learning), Semester 2 2021

• Tutor, USYD COMP5328 (Advanced Machine Learning), Semester 2 2020

Academic Services

• Conference Reviewer: NeurIPS, ICML, ICLR, UAI, etc.

• Journal Reviewer: Machine Learning, IEEE TNNLS, ACM Computing Surveys, etc.

Honors and Awards

• Faculty of Engineering Research Scholarship, The University of Sydney, 2020-2023
• Outstanding graduate of Anhui Province, 2019
• Outstanding graduate of University of Science and Technology of China, 2019
• Scholarship for outstanding student of University of Science and Technology of China, 2016/2017/2018
• Scholarship of Institute of Electronics, Chinese Academy of Sciences, 2016/2017

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© Songhua Wu | Last update: Feb. 2022