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Wang-Zhou DAI

Can be pronounced as "won joe dye"

Ph.D.
Research Associate
Department of Computing
Imperial College London

About

After received my Ph.D. degree in Computer Science from Nanjing University in 2019 (supervisor Prof. Zhi-Hua Zhou), I then joined Prof. Stephen Muggleton’s group in the Department of Computing at Imperial College London as a Research Associate from 2019.

My research interest is in machine learning, a sub-field of artificial intelligence. Currently, I am interested in combining sub-symbolic machine learning and logic-based symbolic machine learning. (CV)

Research

Leveraging the power of logic reasoning in machine learning is my main focus right now. Popular machine learning techniques, such as Deep Neural Network and Statistical Learning, are good at mapping noisy sub-symbolic data (e.g. images) into symbols (e.g., labels, clusters, etc.); While symbolic machine learning techniques, such as Inductive Logic Programming and Statistical Relational Learning, are good at modelling complex (e.g., recursive) relationships in symbolic data. The two sub-areas in AI have been developed separately throughout the most of the history, resulting a huge gap between machine perception and reasoning. I am trying in various aspects to bridge the two islands, aiming at building ultra-strong machine learning systems that are human understandable, sample-efficient and applicaple to physical-world tasks.

Publications Codes

News

Sep. 29 2021. A paper about how to accelerate the abduction reasoning in abductive learning has been accepted by NeurIPS’21.
Apr. 30 2021. Two papers on combining Inductive Logic Programming (ILP) and neural networks have been accepted by IJCAI’21. paper
Sep. 23 2019. Checkout my Prolog—Julia interface Jurassic.pl, and make fast scientific computation / statistical learning / deep learning in Prolog by calling them as predicates!
Sep. 03 2019. Our paper Bridging machine learning and logical reasoning by abductive learning has been accepted by NeurIPS’19.

Academic Service

Conference PC Member:
- 2021: IJCAI’21 (SPC), KDD’21, ICDM’21, PAKDD’21, NeurIPS’21, ICLR’21;
- 2020: ICML’20, IJCAI’20, AAAI’20, NeurIPS’20, ICLR’20, ECAI’20 (SPC), PAKDD’20, ICPR’20, CIKM’20;
- 2019: ICML’19, IJCAI’19, AAAI’19, KDD’19, PAKDD’19;
- 2018: IJCAI’18, NeurIPS’18.
Journal Reviewer: IEEE TKDE, JMLR, ACM TKDD.

Talks

Abductive Learning: Connecting Machine Learning and Logical Reasoning, @Dagstuhl Seminar “Approaches and Applications of Inductive Programming”, May 11 2021.
Combining Machine Learning and Logical Reasoning, @ Hisilicon, Huawei Shanghai Research Center, Dec 28 2020. (slides)
Abductive Learning: Connecting Machine Learning and Logical Reasoning, @ Huawei Autonomous Driving Network Forum, Nov 27 2020. (Slides)
Introduction to Abductive Learning, @ Nanjing University of Science and Technology, Nov 24 2020.
Abductive Knowledge Induction from Raw Data, @ Samsung AI Research Cambridge, Nov 16 2020.
Bridging Machine Learning and Logical Reasoning by Abductive Learning. Imperial @ NeurIPS 2019, Data Science Institute, Imperial College London. Nov 28th 2019.
Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning, @ London Machine Learning Meetup, Aug 28 2019. (Slides)
- Machine Learning seminar @ City, University of London, May 17 2019.
Research on Integrating First-Order Logical Domain Knowledge with Machine Learning, @ School of Mechanical Engineering, Xiangtan University, Apr 8 2019.