Yanyan Liu | Topic model | Best Researcher Award

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Ms. Yanyan Liu | Topic model | Best Researcher Award

PHD Candidate at University of Macau, Chinađź“–

Yanyan Liu is a dedicated researcher specializing in Data Mining with expertise in neural topic modeling, natural language processing, and recommendation systems. She is currently pursuing her Ph.D. in Computer Science at the University of Macau, focusing on developing innovative machine-learning frameworks to enhance topic modeling and social influence learning. With a strong academic foundation and a passion for advancing knowledge in her field, she has published in esteemed journals and conferences, including Knowledge-Based Systems and ACM CIKM.

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Scopus Profile

Education Background🎓

  • Doctorate in Computer Science
    University of Macau | Aug 2020 – Present
    Major Courses: Natural Language Processing, Web Mining, Computer Vision, and Pattern Recognition.
  • Bachelor of Computer Science and Technology
    Hunan University | Sep 2016 – Jun 2020
    GPA: 85.21/100
    Major Courses: Database (94/100), Computer Network, Advanced Programming, Data Structure, Computer System.

Professional Experience🌱

Yanyan Liu has been involved in cutting-edge research on neural topic modeling, where she proposed:

  • An efficient energy-based neural topic model integrating a learnable topic prior constraint.
  • A novel topic-guided debiased contrastive learning framework to enhance topic discrimination.
    She has also contributed to social influence learning models for recommendation systems, advancing the field of personalized recommendations.
Research Interests🔬

Her research focuses on Data Mining, Natural Language Processing, Web Mining, Computer Vision, and Pattern Recognition, with a particular interest in applying these technologies for real-world challenges.

Author Metrics

Yanyan Liu has established herself as an emerging researcher in the field of data mining and machine learning, with a growing portfolio of impactful publications in reputed venues. Her work has been featured in journals such as Knowledge-Based Systems and conferences like the ACM International Conference on Information and Knowledge Management (CIKM), demonstrating her ability to address complex problems in neural topic modeling and recommendation systems. Through her innovative contributions, she has garnered recognition for proposing efficient frameworks and methodologies that advance understanding in these domains. Her publications reflect her commitment to high-quality research and her potential to make significant strides in the field.

Publications Top Notes đź“„

1. Cycling Topic Graph Learning for Neural Topic Modeling

  • Authors: Liu, Y., Gong, Z.
  • Journal: Knowledge-Based Systems
  • Year: 2025
  • Volume: 310
  • DOI/Article ID: 112905
  • Citations: 0 (as of now).
  • Summary:
    This paper introduces a novel approach to neural topic modeling using cycling topic graph learning. The method enhances the interpretability and efficiency of topic models by incorporating graph-based structures to represent relationships among topics dynamically. This energy-efficient framework leverages embeddings to achieve improved coherence and relevance in extracted topics.

2. Social Influence Learning for Recommendation Systems

  • Authors: Chen, X., Lei, P.I., Sheng, Y., Liu, Y., Gong, Z.
  • Conference: 33rd ACM International Conference on Information and Knowledge Management (CIKM)
  • Year: 2024
  • Pages: 312–322
  • Citations: 1 (as of now).
  • Summary:
    This conference paper proposes a social influence learning framework tailored for recommendation systems. It explores the role of social connections in shaping user preferences and integrates social influence modeling with machine learning techniques to enhance recommendation accuracy. The model accounts for dynamic social interactions, improving both predictive power and user satisfaction.

Conclusion

Ms. Yanyan Liu is a highly promising researcher with significant achievements in neural topic modeling and recommendation systems. Her innovative contributions, publications in esteemed venues, and dedication to advancing machine learning and data mining make her a strong candidate for the Best Researcher Award. While her citation metrics and collaborative efforts could benefit from further growth, her potential for impactful research and her current accomplishments position her as an excellent choice for this honor.

Her dedication to tackling complex problems and her innovative approach to addressing them not only align with the criteria for the award but also set a strong foundation for her future contributions to the academic and professional world.

Lechen Li | Data Science | Best Researcher Award

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Assist. Prof. Dr. Lechen Li | Data Science | Best Researcher Award

Assistant Professor, at Hohai University, Chinađź“–

Lechen Li, Ph.D., is a multidisciplinary researcher and engineer specializing in Engineering Mechanics and Data Science. With a strong foundation in computational mechanics and deep learning, he has contributed significantly to smart grid development, structural health monitoring, and intelligent systems. His award-winning work has been presented at leading international conferences and has garnered recognition for its impact on sustainable infrastructure and advanced engineering solutions.

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Scopus Profile

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Google Scholar Profile

Education Background🎓

Dr. Lechen Li is an accomplished scholar in Engineering Mechanics and Data Science with extensive academic and research experience. He earned his Ph.D. in Engineering Mechanics from Columbia University in 2023, achieving an impressive GPA of 3.889/4.0. His doctoral research spanned smart grid development, computational structural dynamics, and data-driven system control. Prior to this, he completed a Master of Science in Data Science at Columbia University in 2019, where he excelled academically with a GPA of 3.917/4.0 and received the prestigious Robert A.W. and Christine S. Carleton Scholarship. Dr. Li’s academic journey began at Sichuan University, China, where he earned his Bachelor’s degree in Engineering Mechanics in 2018. Notably, he secured first prizes in the Zhou Peiyuan National Mechanics Modeling Contest and the First Prize Scholarship twice.

Professional Experience🌱

Dr. Li brings a wealth of industry experience that complements his academic achievements. At Colombo International Container Terminals (CICT) in Sri Lanka, he served as a Data Research Analyst, where he developed machine learning models to optimize port logistics and transportation planning using a dynamic reinforcement learning framework. Earlier, during his tenure as a CAE Analyst at the National Institute of Water, Energy and Transportation in China, Dr. Li conducted advanced simulations using the Extended Finite Element Method (XFEM), providing valuable insights into lateral pile-soil pressure distribution on pile groups.

Research Interests🔬

Dr. Li’s research is centered on:

  • Structural Health Monitoring and Control: Developing advanced deep-learning frameworks for real-time system identification and damage detection.
  • Data-Driven Dynamics: Applying machine learning and signal processing techniques for smart grid optimization and time-series forecasting.
  • Computational Mechanics: Leveraging finite element analysis and XFEM for solving complex engineering problems.
  • Sustainability and Infrastructure: Innovating intelligent systems for energy-efficient monitoring and optimization.

Author Metrics 

  • Publications: Dr. Li has co-authored numerous papers in high-impact journals and conferences, including presenting at the 8th World Conference on Structural Control and Monitoring, where he received the Best Conference Paper Award.
  • Citations: His publications have been widely cited, reflecting the practical and theoretical contributions of his research.
  • Academic Awards: Best Paper Award (8WCSCM, 2022), First Prize in Zhou Peiyuan National Mechanics Modeling Contest (2017).

Publications Top Notes đź“„

1. Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Applied Energy, 2021
  • Citations: 82
  • Summary: This study focuses on improving short-term electricity load forecasting at the apartment level. The authors developed a modified neural network model that integrates auto-regressive features to enhance prediction accuracy. The approach has implications for optimizing energy management and grid operations in residential buildings.

2.Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Energy and Buildings, 2021
  • Citations: 32
  • Summary: This paper examines the effects of COVID-19 lockdowns on residential electricity consumption patterns. The study provides insights into shifts in energy usage due to work-from-home trends and discusses the implications for grid stability and planning.

3.Structural damage assessment through a new generalized autoencoder with features in the quefrency domain

  • Authors: L. Li, M. Morgantini, R. Betti
  • Published in: Mechanical Systems and Signal Processing, 2023
  • Citations: 28
  • Summary: The research introduces a novel autoencoder model that utilizes features in the quefrency domain for structural damage detection. The methodology enhances damage assessment accuracy and offers a new perspective in signal processing for civil infrastructure health monitoring.

4. A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure systems

  • Authors: L. Li, R. Betti
  • Published in: Journal of Civil Structural Health Monitoring, 2023
  • Citations: 8
  • Summary: This work proposes a machine learning-driven data augmentation technique aimed at improving structural damage classification in civil infrastructure systems. The study addresses the challenges of limited data availability in real-world scenarios and improves model robustness.

5. Experimental investigation of the dynamic mechanical properties of concrete under different strain rates and cyclic loading

  • Authors: L. Gan, Y. Liu, Z. Zhang, Z. Shen, L. Li, H. Zhang, H. Jin, W. Xu
  • Published in: Case Studies in Construction Materials, 2024
  • Citations: 4
  • Summary: This experimental study explores the dynamic mechanical behavior of concrete under varying strain rates and cyclic loading conditions. The findings contribute to understanding the material’s performance in diverse loading scenarios, which is crucial for construction and structural design.

Conclusion

Dr. Lechen Li is undoubtedly a highly deserving candidate for the Best Researcher Award. His innovative contributions to engineering mechanics, data science, and structural health monitoring, combined with his solid academic background, make him a strong contender. His research not only pushes the boundaries of technology but also has significant real-world implications for energy management, infrastructure sustainability, and smart grid optimization.

While there are areas where he can expand his influence—such as increasing collaborations with industry, diversifying research, and engaging more broadly with the public—his current achievements already demonstrate his potential for continued leadership in these fields. His work is set to contribute substantially to the next generation of intelligent systems, and with continued focus on bridging academia and industry, Dr. Li will undoubtedly remain at the forefront of his field.

Hence, Dr. Lechen Li’s selection for the Best Researcher Award is both well-earned and a recognition of his future promise as a trailblazer in engineering and data science.

Graph Analytics Pioneer Award

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Introduction of Graph Analytics Pioneer Award

Welcome to the forefront of innovation and excellence in the realm of data science! The Graph Analytics Pioneer Award is a prestigious recognition designed to honor trailblazers who have significantly contributed to the advancement of graph analytics. This award acknowledges individuals who have demonstrated unparalleled vision and achievement, shaping the future of network science.

Eligibility:

Open to individuals of all ages, the Graph Analytics Pioneer Award celebrates excellence across academia, industries, and research. There are no age limits, ensuring that innovators of all career stages have the opportunity to be recognized.

Qualifications:

Candidates must showcase a remarkable track record in the field of graph analytics, with a focus on pioneering contributions, cutting-edge research, and impactful applications. A strong emphasis is placed on academic achievements, industry leadership, and innovative approaches to network science.

Publications and Requirements:

Applicants should have a noteworthy portfolio of publications, highlighting their significant contributions to graph analytics. Additionally, a comprehensive resume and relevant supporting documents are required to demonstrate the depth and breadth of their work.

Evaluation Criteria:

Candidates will be assessed based on the originality, impact, and relevance of their contributions to the field. The evaluation process considers academic achievements, industry influence, and the overall significance of their work in advancing graph analytics.

Submission Guidelines:

Detailed submission guidelines, including document formats and deadlines, can be found on our official website. Ensure that all required materials, including a biography, abstract, and supporting files, are submitted in accordance with the provided instructions.

Recognition:

The Graph Analytics Pioneer Award offers unparalleled recognition within the global network science community. Awardees will receive a prestigious trophy, a certificate of achievement, and extensive visibility through various platforms.

Community Impact:

Recipients of this award will have the opportunity to make a lasting impact on the network science community, fostering collaboration, and inspiring the next generation of graph analytics enthusiasts.

Biography and Abstract:

A detailed biography and abstract are required to provide a comprehensive overview of the nominee's contributions to graph analytics. These components should offer insights into their journey, achievements, and the transformative nature of their work.

Supporting Files:

Applicants are encouraged to submit supporting files, such as research papers, case studies, or project documentation, to substantiate the impact and relevance of their work in graph analytics.

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