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Mr Yangyang Shu

Mr Yangyang Shu

Associate Lecturer

Ph.D. in Computer Science, University of Technology Sydney

91色情片 Canberra
School of Systems & Computing

Dr Yangyang Shu is an associate lecturer in the School of Systems and Computing at the University of New South Wales, Canberra.

Before joining 91色情片, he was a research fellow at the Australian Institute for Machine Learning (AIML), the University of Adelaide and worked on the project - The Centre for Augmented Reasoning (CAR).听

His research focuses on building practical and reliable AI systems that are trustworthy, efficient, and adaptive. He develops machine learning methods that improve how AI systems reason and align with human intentions, enhance the controllability and efficiency of foundation models, and enable models to learn and generalise beyond large-scale supervision. His research spans machine learning, generative AI, computer vision, and multimodal learning, with a particular interest in reliable and data-efficient foundation models. More information about his research can be found on his homepage:

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E-mail
yangyang.shu@unsw.edu.au
Location
Building 15, Room 209
  • Book Chapters | 2026
    Shen Y; Shu Y; Paik HY; Sui Y, 2026, 'MetaLogic: Robustness Evaluation of听Text-to-Image Models via听Logically Equivalent Prompts', in , pp. 152 - 171,
  • Journal articles | 2026
    Hua W; Chua Y; Wang M; Zhang Q; Wang K; Shu Y, 2026, 'SPASRNN: boosting spiking recurrent neural networks using sparse gradient decent for semantic representation learning', Neural Computing and Applications, 38,
    Journal articles | 2026
    Li S; Cai Q; Zhang F; Shu Y; Li G; Dong J; Liu L; Zhang D, 2026, 'LearnMat: Semantic-Aware Self-Supervision Fine-Grained Visual Recognition', IEEE Transactions on Image Processing, 35, pp. 2555 - 2569,
    Journal articles | 2025
    Ge J; Zhang B; Liu A; Phan VMH; Chen Q; Shu Y; Zhao Y, 2025, 'CIT: Rethinking class-incremental semantic segmentation with a Class Independent Transformation', Pattern Recognition, 167,
    Journal articles | 2025
    Shu Y; Liu Y; Cao X; Chen Q; Zhang B; Zhou Z; van den Hengel A; Liu L, 2025, 'Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale', Transactions on Machine Learning Research, 2025-June
    Journal articles | 2024
    Shu Y; Li Q; Liu L; Xu G, 2024, 'Semi-Supervised Adversarial Learning for Attribute-Aware Photo Aesthetic Assessment', IEEE Transactions on Multimedia, 26, pp. 4086 - 4096,
    Journal articles | 2022
    Shu Y; Li Q; Liu L; Xu G, 2022, 'Privileged multi-task learning for attribute-aware aesthetic assessment', Pattern Recognition, 132,
    Journal articles | 2022
    Shu Y; Li Q; Xu C; Liu S; Xu G, 2022, 'V-SVR+: Support Vector Regression With Variational Privileged Information', IEEE Transactions on Multimedia, 24, pp. 876 - 889,
    Journal articles | 2020
    Shu Y; Li Q; Liu S; Xu G, 2020, 'Learning with privileged information for photo aesthetic assessment', Neurocomputing, 404, pp. 304 - 316,
    Journal articles | 2019
    Wang S; Wang C; Chen T; Wang Y; Shu Y; Ji Q, 2019, 'Video Affective Content Analysis by Exploring Domain Knowledge', IEEE Transactions on Affective Computing, 12, pp. 1002 - 1017,
  • Conference Papers | 2025
    Hua W; Zhou C; Wu J; Chua Y; Shu Y, 2025, 'MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion', in Ijcai International Joint Conference on Artificial Intelligence, pp. 5399 - 5407,
    Conference Papers | 2024
    Zhou Z; Xu H-M; Shu Y; Liu L, 2024, 'Unlocking the Potential of Pre-Trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors', Institute of Electrical and Electronics Engineers (IEEE), pp. 3817 - 3827, presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
    Conference Papers | 2023
    Shu Y; Van den Hengel A; Liu L, 2023, 'Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems', Institute of Electrical and Electronics Engineers (IEEE), pp. 11392 - 11401, presented at 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
    Conference Papers | 2022
    Shu Y; Yu B; Xu H; Liu L, 2022, 'Improving Fine-Grained Visual Recognition in听Low Data Regimes via听Self-boosting Attention Mechanism', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 449 - 465,
    Conference Papers | 2020
    Shu Y; Sui Y; Zhang H; Xu G, 2020, 'Perf-AL: Performance prediction for configurable software through adversarial learning', in International Symposium on Empirical Software Engineering and Measurement,
    Conference Papers | 2019
    Shu Y; Xu G, 2019, 'Emotion recognition from music enhanced by domain knowledge', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 121 - 134,
    Conference Papers | 2019
    Zhao M; Shu Y; Liu S; Xu G, 2019, 'Electricity Price Forecast using Meteorology data: A study in Australian Energy Market', in Besc 2019 6th International Conference on Behavioral Economic and Socio Cultural Computing Proceedings,
    Conference Papers | 2017
    Shu Y; Wang S, 2017, 'Emotion Recognition Through Integrating EEG and Peripheral Signals', Institute of Electrical and Electronics Engineers (IEEE), pp. 2871 - 2875, presented at 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),

My current research program is organised around three interconnected themes:

Learning to Reason and Align . I investigate how AI systems can reason more reliably and better align with human intentions. My work explores rationale-guided learning, counterfactual reasoning, and robust multimodal learning to improve model generalisation, reliability, and alignment.

Efficient and Controllable Foundation Models . I develop methods to make foundation models more controllable and computationally efficient, particularly autoregressive and multimodal generative models. My research explores counterfactual learning, fine-grained generation control, efficient adaptation, and accelerated inference.

Learning Beyond Data . I study how AI systems can learn and generalise effectively under limited supervision, scarce data, and changing environments. This research includes low-supervision learning, domain adaptation, and robust representation learning.

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My Teaching

ZEIT 2103, Data Structures and Representation, 2025, S1

ZEIT 1102, Introduction to Programming, 2025, S2

ZEIT 3101, IT Project2, 2025, S2

ZEIT 2102, Computer Technology, 2025, S2

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