Yangyang Shu | Low-supervised Learning | Best Researcher Award

Dr. Yangyang Shu | Low-supervised Learning | Best Researcher Award

Associate Lecturer at University of New South Wales, Australia

Yangyang Shu is a computer science researcher specializing in machine learning and artificial intelligence, with a current appointment as Associate Lecturer at the University of New South Wales (UNSW). His research spans self-supervised learning, domain adaptation, privileged information, and their applications in areas like fine-grained visual recognition and music understanding. With a strong academic background from institutions in China and Australia, he has published extensively in top-tier venues (CVPR, IJCAI, ECCV) and actively contributes to peer review for A* conferences and journals. Yangyang is also skilled in symbolic music generation and performance modeling, especially in the context of large language models.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D. in Engineering & Information Technology
    University of Technology Sydney (UTS), Australia
    Duration: July 2018 – November 2021
    Supervisor: Prof. Guandong Xu

  • M.Sc. in Computer Science
    University of Science and Technology of China (USTC)
    Duration: September 2015 – July 2018
    Supervisor: Prof. Shangfei Wang

  • B.Sc. in Computer Science
    Anhui University (AHU)
    Duration: September 2011 – July 2015

Professional Experience 💼

  • Associate Lecturer
    University of New South Wales (UNSW), School of Systems and Computing
    March 2025 – Present
    Supervisor: Prof. Roland Göcke

  • Research Fellow
    University of Adelaide, Australian Institute for Machine Learning (AIML)
    December 2021 – December 2024
    Supervisor: A/Prof. Lingqiao Liu

  • Academic Supervision
    Unofficially supervised 1 PhD student, 3 master’s students, and 2 undergraduates at USTC, UTS, Adelaide, and UNSW.

  • Teaching Experience

    • ZEIT 2103 – Data Structures and Representation, 2025 Semester 1

    • ZEIT 1112 – Introduction to Programming, 2025 Semester 2

Research Interests 🔬

  • Core Areas:

    • Machine Learning (Self/Semi-Supervised Learning)

    • Unsupervised Domain Adaptation

    • Learning with Privileged Information

    • Multi-task Learning and Fine-Grained Visual Recognition

    • Music Emotion Recognition and Aesthetic Assessment

  • Recent Research Focus:

    • Rationale-Guided Learning: Developing regularizations based on prediction rationale to improve generalization and data efficiency.

    • Large Language Models for Music: Enhancing training, generation control, and inference for symbolic music generation models.

Awards and Honors🏆✨

  • 🥇 National Scholarship, University of Science and Technology of China (14/150) – 2017

  • 🏅 Outstanding Graduate, USTC – 2018 (17/150)

  • 🎓 Excellent Graduate, Anhui Province – 2015 (2/391)

  • 🧮 Second Prize, National College Student Mathematics Competition – 2014

Conclusion🌟

Yangyang Shu is an emerging leader in artificial intelligence research, particularly in areas intersecting machine learning theory and creative applications like music AI. His consistent contributions to high-impact venues, combined with interdisciplinary research and teaching experience across major institutions in China and Australia, mark him as a promising figure in the AI and computer vision communities. His pursuit of rationale-aware and efficient learning systems shows a clear vision for the next generation of interpretable and human-aligned AI technologies.

Publications 📚

  1. 🎵 MuseBarControl
    Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss
    Yangyang Shu, Haiming Xu, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu
    📄 arXiv:2402.01157, 2024


  2. 🧠 Unlocking the Potential of Pre-trained Vision Transformers
    for Few-Shot Semantic Segmentation through Relationship Descriptors
    Ziqin Zhou, Haiming Xu, Yangyang Shu, Lingqiao Liu
    🎯 CVPR 2024 (⭐ Core Rank A*)


  3. ⚡ MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
    Wei Hua, Chenlin Zhou, Jibin Wu, Yansong Chua, Yangyang Shu
    🤖 IJCAI 2025 (⭐ Core Rank A*)


  4. 🧩 Learning Common Rationale to Improve Self-Supervised Representation
    for Fine-Grained Visual Recognition Problems
    Yangyang Shu, Anton van den Hengel, Lingqiao Liu
    🎯 CVPR 2023 (⭐ Core Rank A*)


  5. 🔄 Source-Free Unsupervised Domain Adaptation
    with Hypothesis Consolidation of Prediction Rationale
    Yangyang Shu, Xiaofeng Cao, Qi Chen, Bowen Zhang, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu
    📄 arXiv:2402.01157, 2024


  6. 📉 Improving Fine-Grained Visual Recognition in Low Data Regimes
    Via Self-Boosting Attention Mechanism
    Yangyang Shu, Lingqiao Liu, Baosheng Yu, Haiming Xu
    🖼️ ECCV 2022 (⭐ Core Rank A*)


  7. 🌇 Semi-Supervised Adversarial Learning
    for Attribute-Aware Photo Aesthetic Assessment
    Yangyang Shu, Qian Li, Lingqiao Liu, Guandong Xu
    📰 IEEE TMM 2021 (⭐ Core Rank A*)


  8. 🎨 Privileged Multi-Task Learning for Attribute-Aware Aesthetic Assessment
    Yangyang Shu, Qian Li, Guandong Xu
    📘 Pattern Recognition (PR) 2022 (⭐ Core Rank A*)


  9. ➕ V-SVR+: Support Vector Regression with Variational Privileged Information
    Yangyang Shu, Qian Li, Chang Xu, Shaowu Liu, Guandong Xu
    📰 IEEE TMM 2021 (⭐ Core Rank A*)


  10. 🧪 Perf-AL: Performance Prediction for Configurable Software through Adversarial Learning
    Yangyang Shu, Yulei Sui, Hongyu Zhang, Guandong Xu
    🛠️ ESEM 2020, pp. 1–11 (⭐ Core Rank A)


  11. 🖼️ Learning with Privileged Information for Photo Aesthetic Assessment
    Yangyang Shu, Qian Li, Shaowu Liu, Guandong Xu
    🧮 Neurocomputing 2020, Vol. 404, pp. 304–316 (⭐ Core Rank B)


  12. 🎻 Emotion Recognition from Music Enhanced by Domain Knowledge
    Yangyang Shu, Guandong Xu
    🌊 PRICAI 2019, Fiji, pp. 121–134 (⭐ Core Rank B)


  13. 🧠 Emotion Recognition through Integrating EEG and Peripheral Signals
    Yangyang Shu, Shangfei Wang
    🔊 ICASSP 2017, USA, pp. 2871–2875 (⭐ Core Rank B)


 

 

 

 

Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Prof. Dr. Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Professor at Srm Institute Of Science And Technology Tiruchirappalli, India

Dr. S. Kanaga Suba Raja is a dedicated computer science educator and researcher with a passion for innovation and technology. With a rich history of academic leadership and groundbreaking research, he continues to inspire the next generation of engineers. 🌍💡

Publication Profile : 

Scopus

Orcid

Google Scholar

 

🎓 Educational Background :

Dr. Kanaga Suba Raja completed his Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University in 2013. He holds a Master’s degree in Computer Science and Engineering from Noorul Islam College of Engineering (2006) and a Bachelor’s degree from The Rajaas Engineering College (2003).

💼 Professional Experience :

With over 19 years of experience in academia, Dr. Kanaga Suba Raja has held several prominent positions, including Professor and Head of the Department of Computer Science and Engineering at SRM Institute of Science and Technology, and Associate Dean at the School of Computing. His career spans roles as Associate Professor and Lecturer at various institutions under Anna University, where he contributed significantly to curriculum development and academic administration.

📚 Research Interests : 

Dr. Kanaga Suba Raja specializes in artificial intelligence, cloud computing, and biomedical engineering. He has published over 100 research papers, received numerous citations, and holds patents related to cloud computing and medical technologies.

📝 Publication Top Notes :

  1. Priya, J., Kanaga Suba Raja, S., & Usha Kiruthika, S. (2024). State-of-art technologies, challenges, and emerging trends of computer vision in dental images. Computers in Biology and Medicine, 178. https://doi.org/10.1016/j.compbiomed.2024.108800
  2. Priya, J., Kanaga Suba Raja, S., & Sudha, S. (2024). An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing (SIViP). https://doi.org/10.1007/s11760-024-03227-9
  3. Chandra, & Kanaga Suba Raja, S. (2024). HHECC-AES: A novel hybrid cryptography scheme for developing the secured wireless body area network using heuristic-aided blockchain model. Ad Hoc & Sensor Wireless Networks, 59, 141–179. https://doi.org/10.32908/ahswn.v59.10477
  4. Sandhiya, B., Kanaga Suba Raja, S., Shruthi, K., & Praveena Rachel Kamala, S. (2024). Brain tumour segmentation and classification with reconstructed MRI using DCGAN. Biomedical Signal Processing and Control, 92. https://doi.org/10.1016/j.bspc.2024.106005
  5. Sandhiya, B., & Kanaga Suba Raja, S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89(1). https://doi.org/10.1016/j.bspc.2023.105778
  6. Kausalya, K., & Kanaga Suba Raja, S. (2024). OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video. International Journal of Wavelets, Multiresolution and Information Processing, 22(1). https://doi.org/10.1142/S0219691323500340
  7. Chandra, B., & Kanaga Suba Raja, S. (2023). Security in wireless body area network (WBAN) using blockchain. IETE Journal of Research. https://doi.org/10.1080/03772063.2023.2233472
  8. Hema, M., & Kanaga Suba Raja, S. (2023). A quantitative approach to minimize energy consumption in cloud data centres using VM consolidation algorithm. KSII Transactions on Internet and Information Systems, 17(2), 312-334. https://doi.org/10.3837/tiis.2023.02.002
  9. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Enhanced ECC based authentication protocol in wireless sensor network with DoS mitigation. Cybernetics and Systems, 53(2). https://doi.org/10.1080/01969722.2022.2055403
  10. Hema, M., & Kanaga Suba Raja, S. (2022). An efficient framework for utilizing underloaded servers in compute cloud. Computer Systems Science and Engineering, 43(5). https://doi.org/10.32604/csse.2023.024895
  11. Vivekanandan, M., & Kanaga Suba Raja, S. (2022). Virtex-II Pro FPGA-based smart agricultural system. Wireless Personal Communications, 125(1), 119–141. https://doi.org/10.1007/s11277-022-09544-x
  12. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Elliptic curve cryptography-based authentication protocol enabled with optimized neural network-based DoS mitigation. Wireless Personal Communications, 124(27). https://doi.org/10.1007/s11277-021-08902-5
  13. Balaji, V., & Kanaga Suba Raja, S. (2021). Recommendation learning system model for children with autism. Intelligent Automation & Soft Computing, 31(2). https://doi.org/10.32604/iasc.2022.020287
  14. Valarmathi, K., & Kanaga Suba Raja, S. (2021). Resource utilization prediction technique in cloud using knowledge-based ensemble random forest with LSTM model. Concurrent Engineering: Research and Applications. https://doi.org/10.1177/1063293X211032622
  15. Kanaga Suba Raja, S., & Virgin Louis, B. A. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369–3388. https://doi.org/10.1007/s11277-021-08618-6