Ming Liu | Knowledge Graph | Best Researcher Award

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Prof. Ming Liu | Knowledge Graph | Best Researcher Award

Professor at Harbin Institute of Technology, China📖

Ming Liu, born in 1981, is a Professor and Ph.D. supervisor at Harbin Institute of Technology, China. He is a recognized expert in Knowledge Graph, Knowledge Mining, and Bioinformatics. Liu has made significant contributions to these fields, leading several prominent research projects, including National Key R&D Program Projects and the Natural Science Foundation of China. He has been honored with various awards, including the 1st Prize of Science and Technology of Heilongjiang Province and the 1st Prize of Innovation and Entrepreneurship Competition of China Artificial Intelligence Society. Liu has authored over 20 high-quality papers and one English-translated book, with publications in prestigious journals and conferences like TKDE, TOIS, IJCAI, and ACL.

Profile

Google Scholar Profile

Education Background🎓

Ming Liu completed his Ph.D. in Computer Science at the Harbin Institute of Technology, China, where he also earned his B.Sc. and M.Sc. degrees. His academic journey in the field of computer science laid a strong foundation for his expertise in areas such as Knowledge Graphs, Knowledge Mining, and Bioinformatics. Throughout his education, he was recognized for his exceptional academic performance, earning numerous accolades and awards, which propelled him into a successful research and teaching career.

Professional Experience🌱

Professor Ming Liu currently serves as a faculty member and Ph.D. supervisor at Harbin Institute of Technology. He has successfully led several key projects funded by the National Key R&D Program, the Natural Science Foundation of China, and the China Postdoctoral Science Foundation. His professional experience includes contributions to leading academic conferences, having served as an Area Chair for major events such as ACL 2024, EMNLP 2024, and NAACL 2024, focusing on Knowledge Graphs. Additionally, he has chaired programs for IJCKG 2024 and CCKS 2023. Liu’s leadership extends to mentoring graduate and Ph.D. students, guiding their research projects, and fostering the development of new insights in knowledge mining and bioinformatics.

Research Interests🔬

Professor Liu’s research interests are deeply rooted in the intersection of computer science and life sciences. His primary focus lies in Knowledge Graphs, Knowledge Mining, and Bioinformatics. He is particularly interested in advancing Natural Language Processing (NLP) through the application of deep learning techniques, aiming to improve the extraction and representation of knowledge from large datasets. Liu’s work seeks to enhance the way information is processed and understood, offering innovative solutions for complex problems in fields such as bioinformatics and data mining. He also explores how these technologies can be leveraged to develop intelligent systems with practical applications in healthcare and other industries.

Author Metrics

Professor Liu has an impressive track record in academic publishing, with over 20 high-quality papers published as the first author or corresponding author in prestigious journals and conferences such as TKDE, TOIS, IJCAI, and ACL. His publications cover a range of topics including Knowledge Graphs, Knowledge Mining, and Natural Language Processing. Liu is also the author of a book titled Natural Language Processing based on Deep Learning, which was translated into Chinese and published by China Machine Press in 2017. His research contributions have earned him several awards, including the 1st Prize of Science and Technology from Heilongjiang Province in 2011, the 1st Prize of Innovation and Entrepreneurship Competition from the China Artificial Intelligence Society in 2021, and the Best Paper Award at CCKS 2023.

Publications Top Notes 📄

1. Molweni: A Challenge Multiparty Dialogues-Based Machine Reading Comprehension Dataset with Discourse Structure

  • Authors: J Li, M Liu, MY Kan, Z Zheng, Z Wang, W Lei, T Liu, B Qin
  • Published: arXiv preprint arXiv:2004.05080 (2020)
  • Citations: 102
  • Abstract: This paper introduces Molweni, a multiparty dialogues-based machine reading comprehension dataset. It focuses on incorporating discourse structure to improve understanding and reasoning in dialogue systems, particularly in complex multiparty conversations.

2. A Survey of Chain of Thought Reasoning: Advances, Frontiers, and Future

  • Authors: Z Chu, J Chen, Q Chen, W Yu, T He, H Wang, W Peng, M Liu, B Qin, T Liu
  • Published: arXiv preprint arXiv:2309.15402 (2023)
  • Citations: 98
  • Abstract: This survey paper discusses the progress, challenges, and future directions in the field of chain-of-thought reasoning, a crucial area in machine learning and artificial intelligence. It provides a comprehensive overview of the theoretical advancements, practical applications, and ongoing research in this domain.

3. Topic-to-Essay Generation with Neural Networks

  • Authors: X Feng, M Liu, J Liu, B Qin, Y Sun, T Liu
  • Published: International Joint Conference on Artificial Intelligence (IJCAI), 4078-4084 (2018)
  • Citations: 85
  • Abstract: This paper explores the use of neural networks for topic-to-essay generation, proposing a model that automatically generates essays from given topics. The work investigates various architectures and techniques in neural networks to enhance the quality and coherence of the generated essays.

4. Visible Light-Driven Jellyfish-Like Miniature Swimming Soft Robot

  • Authors: C Yin, F Wei, S Fu, Z Zhai, Z Ge, L Yao, M Jiang, M Liu
  • Published: ACS Applied Materials & Interfaces 13 (39), 47147-47154 (2021)
  • Citations: 75
  • Abstract: This paper discusses the development of a visible light-driven miniature swimming soft robot that mimics the movement of a jellyfish. The study presents an innovative approach to soft robotics, utilizing visible light to actuate the robot, which could have applications in fields like medicine and environmental monitoring.

5. Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

  • Authors: F Liu, B Liu, C Sun, M Liu, X Wang
  • Published: Entropy 17 (4), 2140-2169 (2015)
  • Citations: 74
  • Abstract: This paper focuses on the use of deep belief networks for link prediction in signed social networks. It investigates how these networks can predict the formation or dissolution of links based on the signs of relationships and the network’s structure, which has applications in social network analysis and recommendation systems.

Conclusion

In conclusion, Professor Ming Liu has demonstrated exceptional leadership, innovative contributions, and profound impact on research in areas of Knowledge Graphs, Bioinformatics, and Natural Language Processing. His high-quality publications, award-winning research, and mentorship solidify his standing as one of the leading researchers in his field. The combination of his academic rigor, technical expertise, and commitment to advancing science makes him an excellent candidate for the Best Researcher Award.

By expanding his interdisciplinary collaborations, focusing on ethical implications, and increasing public engagement, Liu can further enhance the societal impact of his groundbreaking work. However, even without these additional improvements, his record of excellence and innovation positions him as a deserving recipient of this prestigious award.