Yanyan Liu | Topic model | Best Researcher Award

Ms. Yanyan Liu | Topic model | Best Researcher Award

PHD Candidate at University of Macau, China📖

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.

Profile

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 📄

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.

Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

Assoc. Prof. Dr. Mohammad Ali Saniee Monfared | Robustness | Lifetime Achievement Award

Associate Professor at Alzahra University, Iran📖

Dr. Mohammadali Saniee Monfared is a distinguished academic and industry expert with over 20 years of experience across diverse sectors, including manufacturing, automotive, electronics, and cosmetics. His expertise lies in reliability engineering, maintenance planning, and predictive analytics, with a strong focus on turning complex engineering challenges into structured statistical models validated through machine learning techniques. In addition to his extensive industrial background, Dr. Monfared has held esteemed teaching positions at top universities such as Sharif University of Technology, Amirkabir Polytechnic University, K.N. Toosi University, and Alzahra University, where he has guided graduate and undergraduate students across multiple engineering disciplines.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

Dr. Mohammadali Saniee Monfared holds a Ph.D. in Manufacturing and Mechanical Engineering from the University of Birmingham, UK (1997), where he developed advanced methodologies in reliability and system analysis. He earned his first M.Sc. in Industrial Engineering & Operations Research from Sharif University of Technology, Iran (1991), gaining expertise in systems optimization and decision-making models. He further pursued a second M.Sc. in Systems Engineering at the University of Regina, Canada (1994), specializing in system-level design and analysis. This strong academic foundation equipped Dr. Monfared with multidisciplinary knowledge and skills to address complex engineering challenges across industries.

Professional Experience🌱

Dr. Monfared brings over 20 years of professional experience spanning diverse industries, including General Tire and Rubber Manufacturing (8 years), automotive (2 years), electronics manufacturing (2 years), and cosmetic and soap manufacturing (2 years). His industrial work involved solving challenging engineering problems, optimizing production systems, and enhancing operational efficiencies. Notably, his expertise in reliability engineering and predictive analytics has enabled industries to improve system performance, mitigate risks, and ensure process safety. Alongside his industry roles, Dr. Monfared has actively collaborated with organizations, including Iran’s National Gas Company and municipal authorities, on projects such as multi-stakeholder risk assessments, robust maintenance planning, and network vulnerability analyses. His dual experience in academia and industry uniquely positions him to deliver innovative, real-world solutions to complex engineering problems.

Research Interests🔬

Dr. Monfared’s research focuses on:

  • Reliability Engineering
  • Maintenance Planning
  • Complex Networks and System Vulnerability Analysis
  • Predictive Analytics and Machine Learning Applications

Author Metrics

Dr. Monfared has authored impactful papers in renowned journals such as Reliability Engineering & System Safety, Physica A, and Soft Computing. Notable works include:

  • “Topology and vulnerability of the Iranian power grid” (Physica A).
  • “Investigating conflicts in blood supply chains at emergencies” (Soft Computing).
  • “Reliability analysis and optimization of road networks” (Reliability Engineering and System Safety).

His work has garnered significant recognition for its innovative, data-driven solutions addressing real-world challenges in reliability and risk engineering.

Expertise and Skills

  • Data Science and Machine Learning: Neural Networks, Support Vector Machines (SVM)
  • Mathematical Programming
  • Statistical Time Series Analysis
  • State-Space Modeling: Kalman Filters
Publications Top Notes 📄

1. Network DEA: An Application to Analysis of Academic Performance

  • Authors: M.A. Monfared Saniee, M. Safi
  • Journal: Journal of Industrial Engineering International
  • Volume/Issue: 9 (1), Page 15
  • Year: 2013
  • Citations: 73
  • Summary: This paper applies Network Data Envelopment Analysis (DEA) to evaluate and compare academic performance, providing a systematic approach to assess efficiency in educational settings.

2. Topology and Vulnerability of the Iranian Power Grid

  • Authors: M.A.S. Monfared, M. Jalili, Z. Alipour
  • Journal: Physica A: Statistical Mechanics and its Applications
  • Volume/Issue: 406, Pages 24–33
  • Year: 2014
  • Citations: 55
  • Summary: The study examines the topology of the Iranian power grid using complex network theory, analyzing its vulnerability and critical nodes to improve resilience against disruptions.

3. A Complex Network Theory Approach for Optimizing Contamination Warning Sensor Location in Water Distribution Networks

  • Authors: R. Nazempour, M.A.S. Monfared, E. Zio
  • Journal: International Journal of Disaster Risk Reduction
  • Volume/Issue: 30, Pages 225–234
  • Year: 2018
  • Citations: 43
  • Summary: This research optimizes sensor placement in water distribution networks using complex network theory, enhancing contamination warning systems to mitigate disaster risks.

4. Comparing Topological and Reliability-Based Vulnerability Analysis of Iran Power Transmission Network

  • Authors: Z. Alipour, M.A.S. Monfared, E. Zio
  • Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
  • Year: 2014
  • Citations: 35
  • Summary: The paper compares topological and reliability-based methods for analyzing the vulnerability of Iran’s power transmission network, identifying critical areas to improve reliability.

5. Controlling the Multi-Electron Dynamics in the High Harmonic Spectrum from N2O Molecule Using TDDFT

  • Authors: M. Monfared, E. Irani, R. Sadighi-Bonabi
  • Journal: The Journal of Chemical Physics
  • Volume/Issue: 148 (23)
  • Year: 2018
  • Citations: 33
  • Summary: This study utilizes time-dependent density functional theory (TDDFT) to investigate multi-electron dynamics in high harmonic generation from N2O molecules, offering insights into electron control mechanisms.

Conclusion

Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is highly deserving of the Lifetime Achievement Award due to his exemplary career in both academia and industry. His ability to address real-world engineering problems through a combination of theoretical innovation and practical application has made a substantial impact in the fields of reliability engineering, complex networks, and predictive analytics.

His work in optimizing systems (power grids, water networks, and production systems) demonstrates critical contributions to improving societal resilience and operational efficiency. With continued emphasis on global collaborations and leadership in emerging research areas, Dr. Monfared’s influence will undoubtedly expand, solidifying his legacy as a leader in reliability and network analysis.

Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

Prof. Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

Corresponding Author, at ferdowsi University of mashhad, Iran📖

Prof. Hadi Sadoghi Yazdi is an accomplished academic and researcher in the field of electronic engineering, with extensive experience in pattern recognition, machine learning, and signal processing. As a Professor at Ferdowsi University of Mashhad, he leads cutting-edge research in artificial intelligence, overseeing projects that have resulted in numerous patents and products in diverse industries. His expertise extends to both academic and industrial sectors, where he has made significant contributions to the development of smart systems, including applications in health, security, and automation. Dr. Yazdi is also a key figure in advancing technology in the military and defense sectors, with his work in missile tracking and vision-based systems influencing both national and international technological advancements.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

Prof. Hadi Sadoghi Yazdi has a strong educational foundation in electronic engineering, having completed his PhD in Electronic Engineering at Tarbiat Modares University, Tehran in 2005. His doctoral research focused on advanced topics in electronic systems, which significantly contributed to his expertise in areas such as pattern recognition and machine learning. Prior to his PhD, he earned a Master’s degree in Electronic Engineering from the same university in 1996, where he honed his skills in signal processing and electronics applications. Dr. Yazdi’s journey in engineering began with a Bachelor’s degree in Electronic Engineering from Ferdowsi University of Mashhad, which he completed in 1994. This educational background laid the groundwork for his distinguished career in both academia and industry, where he has been at the forefront of research in machine vision, signal processing, and artificial intelligence.

Professional Experience🌱

Dr. Yazdi is currently a Professor and Deputy of Research and Technology at Ferdowsi University of Mashhad, a position he has held since 2014. He has served in various academic roles, including Associate Professor (2009-2014) and Assistant Professor (2008-2009) at the same institution. Additionally, Dr. Yazdi supervises the Pattern Recognition Lab at Ferdowsi University, a leading research facility in the field. Prior to his tenure at Ferdowsi University, he held faculty positions at Hakim Sabzevari University (2005-2008), where he was also the Head of the Engineering Department, as well as teaching roles at several other prestigious institutions, including Kashmar University, Tabriz University, Tehran University, Arak University, and Shariati University.

In addition to his academic work, Dr. Yazdi has a strong background in research and development, having worked in industry on numerous projects involving artificial intelligence, electronic systems, and military technologies. He has held senior research and leadership positions in companies such as LG Madiran, Military Industries, and the Defense Industrials, where he was involved in the design and development of complex systems such as missile tracking, electronic fault finding, and smart systems for medical and security applications

Research Interests🔬

Dr. Yazdi’s research interests encompass a broad range of topics, including:

  • Pattern Recognition
  • Machine Learning
  • Machine Vision
  • Signal Processing

His work focuses on developing innovative solutions in these areas, with applications ranging from industrial automation and medical diagnostics to smart systems and security technologies.

Author Metrics and Achievements 

Dr. Yazdi has authored and co-authored numerous research papers and holds several patents in the fields of artificial intelligence and electronics. Some of his key patents include the development of smart systems for applications such as fire detection, facial recognition, and traffic light control. His academic contributions, particularly in pattern recognition and machine learning, have been pivotal in shaping modern approaches to these fields. He has worked on over 40 research projects, both in academia and industry, demonstrating his leadership and impact on technological development.

Publications Top Notes 📄

1.Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

  • Authors: R Izanloo, SA Fakoorian, HS Yazdi, D Simon
  • Published: 2016 Annual Conference on Information Science and Systems (CISS), pp. 500-505
  • Year: 2016
  • Citations: 243
  • Summary: This paper introduces a Kalman filter that utilizes the maximum correntropy criterion (MCC) to handle non-Gaussian noise in dynamic systems, providing a more robust estimation framework for real-time filtering in challenging environments.

2. ECG arrhythmia classification with support vector machines and genetic algorithm

  • Authors: JA Nasiri, M Naghibzadeh, HS Yazdi, B Naghibzadeh
  • Published: 2009 Third UKSim European Symposium on Computer Modeling and Simulation, pp. 187-192
  • Year: 2009
  • Citations: 171
  • Summary: This work explores the classification of ECG arrhythmias using support vector machines (SVM) optimized by a genetic algorithm (GA), demonstrating how this combined approach enhances the accuracy of detecting different types of arrhythmias.

3. An eigenspace-based approach for human fall detection using integrated time motion image and neural network

  • Authors: H Foroughi, A Naseri, A Saberi, HS Yazdi
  • Published: 2008 9th International Conference on Signal Processing, pp. 1499-1503
  • Year: 2008
  • Citations: 127
  • Summary: This paper proposes an eigenspace-based method for human fall detection by integrating time-motion images with a neural network. The approach enhances detection accuracy, providing a reliable system for fall detection in various applications.

4. Probabilistic Kalman filter for moving object tracking

  • Authors: F Farahi, HS Yazdi
  • Published: Signal Processing: Image Communication 82, 115751
  • Year: 2020
  • Citations: 101
  • Summary: This research introduces a probabilistic Kalman filter designed for tracking moving objects. The proposed method enhances the ability of Kalman filters to track objects in uncertain environments, improving real-time tracking applications in various domains.

5. IRAHC: Instance reduction algorithm using hyperrectangle clustering

  • Authors: J Hamidzadeh, R Monsefi, HS Yazdi
  • Published: Pattern Recognition, 48(5), pp. 1878-1889
  • Year: 2015
  • Citations: 90
  • Summary: This paper presents an instance reduction algorithm (IRAHC) that utilizes hyperrectangle clustering to improve the efficiency and effectiveness of machine learning algorithms, particularly for large datasets. The proposed method enhances the performance of classifiers by reducing the number of instances required for training.

Conclusion

Prof. Hadi Sadoghi Yazdi is a deserving candidate for the Best Researcher Award, owing to his significant contributions to the fields of pattern recognition, machine learning, and signal processing. His innovative solutions and patents, particularly in AI and electronics, have far-reaching implications for industries such as healthcare, security, and defense. As an academic leader, Prof. Yazdi has not only advanced theoretical research but also bridged the gap between academia and industry, shaping modern technological landscapes. With continued interdisciplinary collaboration and a focus on solving global challenges, his impact on the world of engineering and technology will undoubtedly continue to grow. His leadership in both research and education makes him a standout figure worthy of the Best Researcher Award.