Mr. Yang Yang | Computational Modeling | Best Researcher Award

National University of Sciences & Technology (NUST), China

Profiles

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Early Academic Pursuits

Yang Yang's academic journey began with a strong foundation in artificial intelligence and data mining. His keen interest in open-world data mining led him to explore innovative methods for handling complex, evolving datasets. As a student, he displayed exceptional analytical abilities and a deep curiosity for AI-driven solutions. This early dedication laid the groundwork for his later contributions to AI research and interdisciplinary applications.

Professional Endeavors

Currently a professor at Nanjing University of Science and Technology, Yang Yang has established himself as a leading researcher in artificial intelligence. His professional journey includes significant contributions to theoretical and applied AI, particularly in the fields of smart agriculture and smart education. As an active member of the IEEE, he has engaged in numerous high-impact projects, shaping the landscape of AI research and its real-world implementations.

Contributions and Research Focus

Yang Yang specializes in open-environment data mining, addressing key challenges such as modal interaction, decision adaptation, and model evolution. His work has resulted in groundbreaking solutions for reliable multi-modal representation, robust inference decision-making, and continuous evolution modeling. These advancements have significantly improved the robustness of AI models in dynamic environments, making them more adaptable to changes in data features, labels, and content across various tasks. His research has played a pivotal role in enhancing AI-driven decision-making in practical applications.

Impact and Influence

With an impressive citation index of 1,289, Yang Yang's research has been widely recognized and referenced by esteemed academicians and Fellows of globally renowned societies such as IEEE, ACM, and AAAS. His innovative methodologies have influenced AI research and have been successfully implemented in smart agriculture and smart education, contributing to advancements in precision farming and intelligent learning systems.

Academic Citations and Recognitions

Yang Yang has published 22 papers in top-tier SCI, IEEE, and ACM journals, many of which are considered foundational in open-world data mining. His outstanding contributions earned him the Best Paper Award at ACML 2017, highlighting his excellence in AI research. Additionally, his papers have been referenced in prestigious international journals and conferences, further establishing his authority in the field.

Technical Skills

Yang Yang possesses expertise in:
Open-world data mining
AI-driven decision-making models
Multi-modal representation learning
Continuous evolution modeling
Smart agriculture and education applications

His ability to bridge AI theory with practical applications has set new benchmarks in interdisciplinary AI research.

Teaching Experience

As a professor, Yang Yang is deeply committed to mentoring and guiding students in the fields of AI and data science. His expertise has helped shape the next generation of AI researchers by providing them with a strong foundation in theoretical and applied AI. His involvement in prestigious AI competitions, where he has won 20 championships, further demonstrates his dedication to both learning and teaching.

Research Projects and Patents

Yang Yang has led several high-profile research projects, including the Young Scientists Project of the National Key Research and Development Program on Autonomous Software for the Application of Scientific Data in Agricultural Breeding. His research has resulted in three patents, showcasing his ability to transform theoretical AI advancements into tangible, real-world innovations.

Legacy and Future Contributions

Yang Yang’s research continues to push the boundaries of AI by focusing on the development of more adaptive and resilient AI models. His contributions to smart agriculture and smart education are paving the way for future innovations in AI-driven industries. His legacy will be defined by his ability to bridge the gap between theoretical AI research and its practical, real-world applications. Moving forward, he aims to expand his research into more interdisciplinary fields, further enhancing AI's impact on society.

Notable Publications

Adaptive deep models for incremental learning: Considering capacity scalability and sustainability
Authors: Y. Yang, D.W. Zhou, D.C. Zhan, H. Xiong, Y. Jiang
Journal/Conference: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Year: 2019

Complex object classification: A multi-modal multi-instance multi-label deep network with optimal transport
Authors: Y. Yang, Y.F. Wu, D.C. Zhan, Z.B. Liu, Y. Jiang
Journal/Conference: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Year: 2018

Learning to classify with incremental new class
Authors: D.W. Zhou, Y. Yang, D.C. Zhan
Journal/Conference: IEEE Transactions on Neural Networks and Learning Systems
Year: 2021

Deep learning for fixed model reuse
Authors: Y. Yang, D.C. Zhan, Y. Fan, Y. Jiang, Z.H. Zhou
Journal/Conference: Proceedings of the AAAI Conference on Artificial Intelligence
Year: 2017

Semi-supervised multi-modal multi-instance multi-label deep network with optimal transport
Authors: Y. Yang, Z.Y. Fu, D.C. Zhan, Z.B. Liu, Y. Jiang
Journal/Conference: IEEE Transactions on Knowledge and Data Engineering
Year: 2019