Yangyang Shu | Low-supervised Learning | Best Researcher Award

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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)

 

 

 

 

Chao Zhang | Machine Learning | Best Researcher Award

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Prof. Chao Zhang | Machine Learning | Best Researcher Award

Professor at Shanghai University, China

Professor Chao Zhang is a distinguished academic and researcher specializing in mechanical engineering, particularly in tribology and engine component wear. With an extensive career spanning multiple prestigious institutions, including Shanghai University and Northwestern University, he has significantly contributed to the field through research, publications, and technical committee roles. His expertise integrates classical tribology with modern computational techniques such as machine learning and quantum chemical molecular dynamics.

Publication Profile 

Scopus

Educational Background 🎓

  • Bachelor’s Degree: Mechanical Engineering, Shanghai Railway University (1983)

  • Master’s Degree: Mechanical Engineering, Shanghai Internal Combustion Engine Research Institute (1989)

  • Ph.D.: Mechanical Engineering, Shanghai University (1997)

Professional Experience 💼

  • Senior Research Associate (1997–2002): Northwestern University, USA (Worked with Profs. H.S. Cheng and Qian Wang)

  • Professor:

    • Tongji University, China

    • Shanghai University, China

    • Kunming University of Science and Technology, China

  • Technical Committee Member: Engines and Powertrains, International Federation for the Promotion of Mechanism and Machine Science (IFToMM)

Research Interests 🔬

  • Tribology and lubrication in engine components

  • Scuffing behavior and wear modeling of piston components

  • Multi-phase and multi-scale engine wear modeling using quantum chemical molecular dynamics and machine learning

  • Digital twin modeling for tribocorrosion

  • Application of artificial intelligence and big data in engine tribology

  • Mechano-chemical kinetic models for boundary lubrication

Awards and Honors🏆✨

  • Technical committee member of IFToMM (Engines and Powertrains)

  • Contributor to Springer’s Mechanisms and Machine Science Series

  • Numerous high-impact journal publications in Tribology Transactions, ASME Journal of Tribology, Tribology International, and Wear

Conclusion🌟

Professor Chao Zhang is an accomplished mechanical engineering expert with a focus on tribology, engine wear, and computational modeling. His interdisciplinary research integrates classical tribology with advanced computational methods, positioning him as a leading figure in his field. His contributions to academia, industry collaborations, and publications underscore his commitment to advancing mechanical engineering and tribology.

Publications 📚

1️⃣ Zhang, C. (2025). Multi-phase and multi-scale engine wear modeling via quantum chemical molecular dynamics and machine learning: A theoretical framework. 🔬🛠️ Wear, xxx(xxx)xxx. [🔗 DOI: 10.1016/j.wear.2025.205771]

2️⃣ Zhang, C. (2023). Lubricant-Chemistry Kinetic Model of Antiwear Film Formation by Oil Additives using SOL, QM MD, and machine learning. 🔍📊 STLE 2023 Annual Meeting Digital Proceedings.

3️⃣ Zhang, C. (2022). Scuffing behavior of piston-pin bore bearing in mixed lubrication. ⚙️📖 In T. Parikyan (Ed.), Advances in Engine and Powertrain Research and Technology (pp. 65–95). Springer, Mechanisms and Machine Science 114.

4️⃣ Zhang, C. (2022). Quantum chemical study of mechanochemical reactive mechanisms of engine oil antiwear additives. 🧪⚛️ Proceedings of I4SDG Workshop 2021, MMS 108, pp. 1–9.

5️⃣ Zhang, C. (2021). Scuffing factor and scuffing failure mapping. 🚗🔥 Proceedings of the 2nd World Congress on Internal Combustion Engine, April 21-24, Jinan, China.

6️⃣ Zhang, C. (2018). Analysis of piston scuffing failure based on big data base and cloud computing. ☁️💾 Proceedings of the 2018 World Internal Combustion Engine Congress and Exhibition, November 8-11, Wuxi, China.

7️⃣ Zhang, C., et al. (2007). Effect of loading path on sliding contact status for elastic and plastic media. 🔩⚙️ Proceedings of the STLE/ASME International Joint Tribology Conference, IJTC2007-44481.

8️⃣ Ye, Z.K., Zhang, C., Wang, Y.C., Cheng, H.S, Tung, S. M., Wang, Q., He, J. (2004). An experimental investigation of piston skirt scuffing: a piston scuffing apparatus, experiments, and scuffing mechanism analyses. 🔍🔬 WEAR, 257, 8-31.

9️⃣ Zhang, C., Wang, Q., Cheng, H. S. (2004). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part II: Scuffing Mechanism and Failure Criteria. 🛠️⚡ STLE, Tribology Transactions, 47, 149-156.

🔟 Zhang, C., Cheng, H. S., Qiu, L., Knipstein, K. W., & Bolyard, J. (2003). Scuffing Behavior of Piston-Pin/Bore Bearing in Mixed Lubrication – Part I: Experimental Studies. 🧑‍🔬📊 STLE, Tribology Transactions, 46, 193-199.

Swati Jaiswal | Deep Learning | Women Researcher Award

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Mrs. Swati Jaiswal | Deep Learning | Women Researcher Award

Assistant Professor at DES Pune University, Pune, India

Swati Jaiswal, Ph.D. candidate at VIT Vellore, is an experienced Assistant Professor in Computer Engineering with over 14 years of academic and research expertise. Currently, she is serving at the School of Computer Engineering & Technology, DES Pune University. She has held various teaching and administrative roles across esteemed institutions like PCCOE, ZCOER, and SKNSITS, contributing significantly to academic development and research. Swati’s contributions span diverse fields like Machine Learning, Cybersecurity, Autonomous Vehicles, AI, and IoT, reflected in her numerous publications, patents, and book chapters 📚🔍. Swati’s dedication to research and teaching is complemented by a passion for developing innovative solutions to real-world problems 🤖💡.

Publication Profile : 

Google Scholar

Education🎓

Swati holds a Master’s in Computer Science & Engineering with 86% from RGPV, Bhopal (2012), and a BE in the same discipline with 80% (2009). She is currently pursuing a Ph.D. in the field of AI and Machine Learning at VIT Vellore, under the guidance of Dr. Chandra Mohan B. Her academic journey also includes certifications in various fields like Data Science, Machine Learning, and Software Testing 🎓📜.

Professional Experience💼

Swati began her career as an Assistant Professor at SAMCET Bhopal in 2009, where she coordinated seminars and workshops. Over the years, she worked at several prestigious institutions, including SKNSITS, ZCOER, and PCCOE, contributing to curriculum development, departmental coordination, and research activities. Since June 2024, she has been with DES Pune University, where she continues her academic journey while nurturing the next generation of engineers and researchers. Along with teaching, she has overseen various academic and administrative responsibilities, including time-table coordination, research guidance, and university exams 🏫📊.

Research Interests🔬

Her research primarily focuses on Machine Learning, Artificial Intelligence, Cybersecurity, Autonomous Systems, and Internet of Things (IoT). She has explored deep learning models for real-time systems, especially in autonomous driving, vehicle communication systems, and intelligent robotics. Additionally, Swati is passionate about the application of AI and ML in solving complex real-world problems such as fraud detection, data security, and predictive analytics 💻🔐🚗.

Publications Top Notes📚

  1. Jha, R. K., Kumar, A., Prakash, S., Jaiswal, S., Bertoluzzo, M., Kumar, A., Joshi, B. P., & … (2022). Modeling of the resonant inverter for wireless power transfer systems using the novel MVLT method. Vehicles, 4(4), 1277-1287. [34 citations]
  2. Kachhoria, R., Jaiswal, S., Khairnar, S., Rajeswari, K., Pede, S., Kharat, R., … (2023). Lie group deep learning technique to identify the precision errors by map geometry functions in smart manufacturing. The International Journal of Advanced Manufacturing Technology, 1-12. [12 citations]
  3. Kachhoria, R., Jaiswal, S., Lokhande, M., & Rodge, J. (2023). Lane detection and path prediction in autonomous vehicle using deep learning. In Intelligent edge computing for cyber physical applications (pp. 111-127). [11 citations]
  4. Swati Jaiswal, D. C. M. B. (2017). A survey: Privacy and security to Internet of Things with cloud computing. International Journal of Control Theory and Applications, 10(1), 487-500. [7 citations]
  5. Jaiswal, S., & Rodge, J. (2019). Comprehensive overview of neural networks and its applications in autonomous vehicles. In Computational Intelligence in the Internet of Things (pp. 159-173). [6 citations]
  6. Kati, S., Ove, A., Gotipamul, B., Kodche, M., & Jaiswal, S. (2022). Comprehensive overview of DDOS attack in cloud computing environment using different machine learning techniques. In Proceedings of the International Conference on Innovative Computing. [5 citations]
  7. Raut, R., Jadhav, A., Jaiswal, S., & Pathak, P. (2022). IoT-assisted smart device for blind people. In Intelligent Systems for Rehabilitation Engineering (pp. 129-150). [4 citations]
  8. Jaiswal, S., & Desai, M. (2019). Importance of information security and strategies to prevent data breaches in mobile devices. In Improving Business Performance Through Innovation in Digital Economy (pp. 215-225). [4 citations]
  9. Jaiswal, S., & Chandra, M. B. (2023). An efficient real-time decision-making system for autonomous vehicle using timber chased wolf optimization-based ensemble classifier. Journal of Engineering Science and Technology Review, 16(1), 75-84. [3 citations]
  10. Jaiswal, S., & Balasubramanian, C. M. (2023). An advanced deep learning model for maneuver prediction in real-time systems using alarming-based hunting optimization. International Journal of Advances in Intelligent Informatics, 9(2). [2 citations]
  11. Sorde, C., Jadhav, A., Jaiswal, S., Padwad, H., & Raut, R. (2023). Generative adversarial networks and its use cases. In Generative Adversarial Networks and Deep Learning (pp. 1-11). [2 citations]
  12. Rajeswari, K., Vispute, S., Maitre, A., Kharat, R., Aher, N., Vivekanandan, N., … (2023). Time series analysis with systematic survey on COVID-19 based predictive studies during pandemic period using enhanced machine learning techniques. iJOE, 19(07), 161. [2 citations]
  13. Jadhav, A., Raut, R., Jhaveri, R., Patil, S., Jaiswal, S., Katole, A., … (2021). A device for child safety and security. [2 citations]
  14. Jaiswal, S., Prakash, S., Gupta, N., & Rewadikar, D. (n.d.). Performance optimization in ad-hoc networks. International Journal of Computer Technology and Electronics Engineering. [2 citations]
  15. Jaiswal, S., & Mohan, B. C. (2024). Deep learning-based path tracking control using lane detection and traffic sign detection for autonomous driving. Web Intelligence, 22(2), 185-207. [1 citation]
  16. Raut, R., Jadhav, A., Jaiswal, S., Kathole, A., & Patil, S. (2023). Intelligent information system for detection of COVID-19 based on AI. In Proceedings of 3rd International Conference on Recent Trends in Machine Learning and Artificial Intelligence. [1 citation]
  17. Jaiswal, S., Sarkar, S., & Mohan, C. (2017). COT: Evaluation and analysis of various applications with security for cloud and IoT. In Examining Cloud Computing Technologies through Internet of Things (pp. 251-263). [1 citation]
  18. Prakash, S., Saxena, V., & Jaiswal, S. (2016). Smart grid: Optimized power sharing and energy storage system framework with recent trends and future ahead. In Handbook of Research on Emerging Technologies for Electrical Power Planning and Analysis (pp. 1-12). [1 citation]
  19. Jaiswal, S., Gupta, N., & Shrivastava, H. (2012). Enhancing the features of intrusion detection system by using machine learning approaches. International Journal of Scientific and Research Publications, 166. [1 citation]
  20. Kharat, R. S., Kalos, P. S., Kachhoria, R., Kadam, V. E., Jaiswal, S., Birari, D., … (2023). Thermal analysis of fuel cells in renewable energy systems using generative adversarial networks (GANs) and reinforcement learning. [No citation count]