Christian Schachtner | Data Science | Research Excellence Award

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Prof. Dr. Christian Schachtner | Data Science | Research Excellence Award

Full Professor at Hochschule RheinMain, Germany

Prof. Dr. Christian Schachtner has made significant scholarly contributions through his monographs and editorial work in the fields of smart governance, smart cities, and digital transformation in the public sector. In 2025, he edited Smart Public Governance, a volume in the Kohlhammer Publishing series, scheduled for publication in the first quarter of 2026. He also co-edited, with M. Brunzel, the Handbook Smart Cities / Smart Regions, likewise forthcoming from Kohlhammer Publishing in early 2026. His edited book The European Smart City Movement  Case Studies from Around Europe, published by Springer in Chur, presents comprehensive insights into smart city practices across Europe. In the same year, he authored CDOs im öffentlichen Sektor – Perspektiven auf Chief Digital Officers und Strategien zur digitalen Transformation, published by Springer, which explores the evolving role of Chief Digital Officers in public administration. Collectively, these works highlight his expertise in digital governance, urban innovation, and strategic public-sector transformation.

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Featured Publications

 

Shengchao Liu | Computer Science | Research Excellence Award

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Dr. Shengchao Liu | Computer Science | Research Excellence Award

The Chinese University | Hong Kong

Shengchao Liu is a tenure-track Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, whose research lies at the intersection of machine learning, geometry, and scientific discovery. His work focuses on developing foundation models and physics-inspired learning frameworks for molecules, proteins, and materials, with the long-term goal of accelerating discovery in chemistry, biology, and materials science. By integrating multi-modal data, symmetry principles, and domain knowledge, his research bridges theoretical advances in AI with real-world experimental impact. A central theme of Dr. Liu’s research is geometric and symmetry-informed representation learning. He has pioneered group-equivariant and manifold-constrained generative models that respect the underlying physical laws of molecular and material systems. His contributions include SE(3)-invariant pretraining methods, group-symmetric stochastic differential equation models, and rigid flow matching techniques, which have significantly improved the fidelity and interpretability of molecular generation and dynamics modeling. These methods form a unifying framework for learning across molecules, proteins, and crystalline materials, as demonstrated in his influential works at ICLR, ICML, NeurIPS, and AISTATS. Dr. Liu’s work is deeply collaborative and interdisciplinary. He has worked closely with leading researchers across academia and industry, including Mila, UC Berkeley, NVIDIA Research, and national laboratories. As a Principal Investigator, he has led NERSC-supported projects on foundation models for material discovery, leveraging large-scale GPU resources to push the frontier of generative AI for science. His research has also contributed widely used open-source resources, including geometric graph learning benchmarks and toolkits adopted by the broader AI-for-science community.

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Featured Publications


Pre-training Molecular Graph Representation with 3D Geometry

– International Conference on Learning Representations , 2021 | Cited by 574


N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules

– Advances in Neural Information Processing Systems, 2019 | Cited by 295


A text-guided protein design framework

– Nature Machine Intelligence, 2025 | Cited by 225

 

Christos Bouras | Computer Science | Research Excellence Award

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Prof. Christos Bouras | Computer Science | Research Excellence Award

Prof. Christos Bouras | University of Patras | Greece

Professor Christos Bouras is a distinguished academic leader and renowned computer engineering expert, currently serving as Professor in the Department of Computer Engineering and Informatics and Rector of the University of Patras, Greece. He holds a Diploma and a PhD in Computer Engineering and Informatics from the University of Patras. Over the course of his career, he has made substantial contributions to advanced networking technologies, digital communications, and distributed systems while leading major academic, administrative, and international initiatives. His research expertise spans and Beyond Networks, performance analysis of networking and computer systems, mobile and wireless communications, telematics, QoS and pricing mechanisms, e-learning technologies, and networked virtual environments. As an active member of IEEE and ACM, Professor Bouras has built a global reputation for innovative contributions and collaborative research. He has also held several prestigious roles, including Honorary Professor at the College of Information Engineering, Sichuan Agricultural University, China, and President of the University of Patras Property Utilization & Management Company. His long-standing academic leadership is matched by a major international presence in scholarly events. Professor Bouras has participated extensively in international conference committees for more than three decades, contributing to global research dialogue in computing, networking, and educational technologies. His committee roles span top-tier conferences such as ACM STOC, ICALP, IEEE ICALT, ICL, ICWN, ICOMP, GRID Computing, and numerous specialized workshops across Europe, Asia, and North America. His involvement includes organizing committees, program committees, keynote speaking, and advisory roles in areas such as distributed algorithms, multimedia systems, web-based learning, virtual environments, mobile technologies, simulation and modeling, and entertainment computing. Widely respected for his research excellence, international collaboration, and academic leadership, Professor Bouras continues to advance global innovation in computer networks, digital systems, and technology-enhanced learning.

Profiles: Google Scholar

Featured Publications

Jurgelionis, A., Fechteler, P., Eisert, P., Bellotti, F., David, H., Laulajainen, J. P., Bouras, C., … (2009). Platform for distributed 3D gaming. International Journal of Computer Games Technology, 2009(1), Article 231863.

Bouras, C., Kollia, A., & Papazois, A. (2017). SDN & NFV in 5G: Advancements and challenges. In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) (pp. xx–xx). IEEE.

Bouras, C., & Tsogkas, V. (2012). A clustering technique for news articles using WordNet. Knowledge-Based Systems, 36, 115–128.

Bouras, C., & Tsiatsos, T. (2006). Educational virtual environments: Design rationale and architecture. Multimedia Tools and Applications, 29(2), 153–173.

Bouras, C., Philopoulos, A., & Tsiatsos, T. (2001). e-Learning through distributed virtual environments. Journal of Network and Computer Applications, 24(3), 175–199.

Bouras, C., Ntarzanos, P., & Papazois, A. (2016). Cost modeling for SDN/NFV based mobile 5G networks. In 2016 8th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (pp. xx–xx). IEEE.

Bouras, C., Konidaris, A., & Kostoulas, D. (2004). Predictive prefetching on the web and its potential impact in the wide area. World Wide Web, 7(2), 143–179.

Hafiz Muhammad Raza ur Rehman | Data Science | Best Researcher Award 

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Assist. Prof. Dr. Hafiz Muhammad Raza ur Rehman | Data Science | Best Researcher Award 

Assist. Prof. Dr. Hafiz Muhammad Raza ur Rehman | Yeungnam University | South Korea

Author Profiles

Scopus

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Google Scholar

Early Academic Pursuits

Dr. Hafiz Muhammad Raza ur Rehman began his academic journey with a strong foundation in information and communication engineering, culminating in a PhD from Yeungnam University, Korea. His doctoral research laid the groundwork for his later contributions in machine learning, multi-agent reinforcement learning (MARL), and data-science. His academic excellence and early engagement with algorithmic design and optimization established his trajectory as a dedicated researcher and educator in computational intelligence.

Professional Endeavors

Following his doctoral studies, Dr. Raza ur Rehman pursued a postdoctoral research position in Korea, focusing on sensor calibration for autonomous vehicles (AVs). Over 5.5 months, he conducted high-level interdisciplinary work aimed at improving the precision and reliability of AV sensor systems. He also gained substantial teaching experience 9 months as an Assistant Professor where he taught undergraduate and graduate courses in machine learning, deep learning, reinforcement learning, and data-science. In addition, his collaboration with the Electronics and Telecommunications Research Institute (ETRI), Korea, on a US Air Force–funded project, exemplified his ability to contribute to large-scale international research efforts.

Contributions and Research Focus

Dr. Raza ur Rehman’s research portfolio reflects a deep commitment to innovation and interdisciplinary integration. His primary focus areas include multi-agent reinforcement learning (MARL), autonomous vehicle systems, natural language processing (NLP), and optimization algorithms. He has authored a patent centered on MARL techniques and published several impactful journal and conference papers. Key publications include “QsOD: MARL-based QMIX with Grey Wolf Optimization” and “Prediction-Based Model for Chemical Compounds.” Moreover, he has presented research such as “Camera Calibration with CNN” at IEEE conferences and six additional papers at Korean academic venues. His current research extends to seven articles under review in internationally reputed journals, reinforcing his commitment to advancing data-science and intelligent systems.

Impact and Influence

Dr. Raza ur Rehman’s interdisciplinary research bridges theory and application spanning from algorithmic optimization to real-world technological integration. His MARL-related patent and publications contribute significantly to the growing body of knowledge in intelligent agent systems. By integrating data-science with advanced computational models, his work influences emerging fields such as autonomous navigation, machine learning-based control systems, and intelligent automation. As a mentor, he continues to inspire students through hands-on projects, fostering innovation and critical thinking in the next generation of engineers and researchers.

Academic Cites

His scholarly output includes publications in peer-reviewed international journals, conference presentations, and ongoing submissions to high-impact outlets. The QsOD study and the chemical compound prediction model have attracted interest in computational optimization and artificial intelligence research circles. His IEEE presentation on CNN-based camera calibration further strengthened his academic visibility and recognition within the AI research community.

Legacy and Future Contributions

Looking ahead, Dr. Hafiz Muhammad Raza ur Rehman aims to expand his research on multi-agent reinforcement learning, autonomous systems, and optimization-driven AI architectures. His future work is poised to contribute substantially to global research in data-science, particularly in developing adaptive, intelligent algorithms for complex real-world problems. Through continued teaching, mentorship, and publication, he aspires to leave a lasting legacy in both academia and applied research bridging the gap between theoretical innovation and practical technological advancement.

Featured Publications

Raza, S. N., ur Rehman, H. M., Lee, S. G., & Choi, G. S. (2019). Artificial intelligence-based camera calibration. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 32. IEEE.

Nagulapati, V. M., ur Rehman, H. M. R., Haider, J., Qyyum, M. A., Choi, G. S., & Lim, H. (2022). Hybrid machine learning-based model for solubilities prediction of various gases in deep eutectic solvent for rigorous process design of hydrogen purification. Separation and Purification Technology, 298, 121651.

ur Rehman, H. M. R., On, B. W., Ningombam, D. D., Yi, S., & Choi, G. S. (2021). QSOD: Hybrid policy gradient for deep multi-agent reinforcement learning. IEEE Access, 9, 129728–129741.

ur Rehman, H. M. R., Saleem, M., Jhandir, M. Z., & Hafiz, H. G. I. A. (2025). Detecting hate in diversity: A survey of multilingual code-mixed image and video analysis. Journal of Big Data, 12(1), Article 5.

Younas, R., ur Rehman, H. M. R., Lee, I., On, B. W., Yi, S., & Choi, G. S. (2025). Sa-MARL: Novel self-attention-based multi-agent reinforcement learning with stochastic gradient descent. IEEE Access, 13, Article 5.

Khan, N. U., & ur Rehman, H. M. R. (2025). Real time signal decoding in closed loop brain computer interface for cognitive modulation. Ubiquitous Technology Journal, 1(1), 32–39.

ur Rehman, H. M. R., Haider, S. A., Faisal, H., Yoo, K. Y., Jhandir, M. Z., & Choi, G. S. (2025). A novel framework for Saraiki script recognition using advanced machine learning models (YOLOv8 and CNN). IEEE Access, 13, Article 2.

Kangwon Lee | Computer Science | Best Researcher Award

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Mrs. Mihaela Corina Radu | Reproductive Health | Excellence in Research 

Carol Davila University of Medicine and Pharmacy Bucharest, Romania.

Radu Mihaela Corina is a Romanian midwifery expert and academic dedicated to improving maternal healthcare. She currently serves as an Associate University Assistant at UMF Carol Davila Bucharest, contributing to both the Department of General and Specific Nursing and the Department of Microbiology, Parasitology, and Virology. With extensive clinical experience, she is also the Head Midwife at Dr. Constantin Andreoiu County Emergency Hospital. Beyond academia, she is actively engaged in European midwifery policy, serving as a member of the Ethics Committee of the European Midwives Association and as a National Expert for Romania in an EU-funded midwifery sectoral project.

Profile

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🎓 Education

Radu Mihaela Corina has pursued an extensive academic journey in the field of medicine and midwifery. She is currently a PhD candidate in Medicine (2021 – Present) at Carol Davila University of Medicine and Pharmacy, Romania, where she is advancing her expertise in maternal and reproductive healthcare. She holds a Master’s Degree in Medical & Public Health Management (2019 – 2021) from the same institution, graduating with a perfect 10.00 GPA, demonstrating her dedication to academic excellence and healthcare leadership. Her foundational training in midwifery was completed with a Bachelor’s Degree in Midwifery (2014 – 2018) at UMF Carol Davila, Romania, where she distinguished herself as the Class Leader, showcasing her leadership skills and commitment to the profession from the early stages of her career.

💼 Professional Experience

With a strong background in midwifery and maternal healthcare, Radu Mihaela Corina has been actively contributing to both academia and clinical practice. Since 2021, she has been serving as an Associate University Assistant at UMF Carol Davila Bucharest, where she plays a key role in training future midwives and healthcare professionals. In parallel, she holds the position of Head Midwife at Dr. Constantin Andreoiu County Emergency Hospital since 2022, overseeing maternity care and ensuring the highest standards in obstetric practice.

Her passion for maternal education led her to work as a Lecturer in Prenatal Courses at the Rhodos Proviva Family Health Education Center (2020 – 2022), where she provided essential guidance to expectant mothers. Additionally, from 2018 to 2022, she served as the Head Midwife in the Birth Block at Obstetrics and Gynecology Hospital, Ploiesti, where she played a crucial role in labor and delivery management, ensuring safe and effective maternity care. Through these roles, she continues to make a significant impact in both education and clinical midwifery.

🔬 Research Interests

Maternal and Child Health 🏥

Midwifery Education & Practice 👶

Reproductive Health & Ethics 🧬

Medical Policy and Public Health 📊

🏆 Awards & Recognitions

2025: Member of the Ethics Committee, European Midwives Association

2024: National Expert for Romania, EU Project on Midwifery Professional Standards

2022 – Present: AMI Delegate, General Council, International Confederation of Midwives

2020 – Present: Vice President, Association of Independent Midwives, Romania

📚 Selected Publications

(2025) Validation of a Questionnaire Assessing Pregnant Women’s Perspectives on Addressing the Psychological Challenges of Childbirth – Nursing Reports, 15(1):8

(2024) Predictors of Pregnant Women's Decision to Opt for Cesarean Section in Romania – Cureus, 16(9)

(2024) Exploring Factors Influencing Pregnant Women’s Perceptions and Attitudes Towards Midwifery Care in Romania – Nursing Reports, 14(3), 1807-1818

(2024) COVID-19 and Flu Vaccination in Romania: Post-Pandemic Lessons – PLoS ONE, 19(3)

(2023) Similarities in Midwifery Education, Regulation, and Practice Across Europe – European Journal of Midwifery, 7(Supplement 1)

 

 

 

Dr. David Hua | Artificial Intelligence | Best Researcher Award

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Dr. David Hua | Artificial Intelligence | Best Researcher Award

Ball State University, United States.

Dr. David M. Hua is an Associate Professor at the Center for Information and Communication Sciences, Ball State University. With a rich academic background and over two decades of teaching, Dr. Hua has become a pivotal figure in the intersection of technology education, cybersecurity, and higher education. He is recognized for mentoring student-led innovation and his contribution to emerging tech curricula including offensive security, private cloud infrastructure, and sustainability in IT.

Profile

Scopus
Orcid

🎓 Education

Dr. Hua earned his Ed.D. in Higher Education in 2010 from Ball State University, where he also completed an MBA in Information Systems (2000) and a B.S. in Psychological Science (1991). This diverse academic foundation reflects his commitment to both technical expertise and educational leadership.

💼 Experience

Since July 20, 1998, Dr. Hua has served at Ball State University, advancing to the role of Associate Professor. He began as an Assistant Professor in 2000. His teaching spans undergraduate and graduate levels with courses ranging from cybersecurity and network configuration to cloud technologies. Beyond Ball State, his engagements with other institutions and organizations have broadened his interdisciplinary impact on both students and faculty.

🔬 Research Interests

Dr. Hua’s research interests lie at the crossroads of cybersecurity, AI in mental health surveillance, sustainable IT practices, and technology integration in higher education. He is especially passionate about leveraging machine learning to support mental health outcomes and empower student innovation through data-driven methodologies.

🏆 Awards & Mentorship

Dr. Hua has been an active mentor in various student projects, honors theses, and national competitions like the National Cyber League. He’s also served on several doctoral committees, contributing to dissertations in educational leadership and adult learning. His efforts have earned him recognition as a dedicated mentor, innovator, and academic leader.

📚 Publication

AI-Driven Mental Health Surveillance: Identifying Suicidal Ideation Through Machine Learning Techniques
📅 2025 | Big Data and Cognitive Computing
🧾 Cited by: 3 articles (as of early 2025)
👉 DOI: 10.3390/bdcc9010016

Mr. Hussm Rostum | Computer Science | Best Researcher Award

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Mr. Hussm Rostum | Computer Science | Best Researcher Award

Miskolc University, Institute of Automation and Info-communication, Hungary.

Hussam Rostum is a PhD candidate and researcher at the University of Miskolc in Hungary, specializing in computer vision for autonomous drone navigation. With a strong background in telecommunications and electronics, he blends academic excellence with hands-on experience as a part-time software engineer at FIEK. Hussam is known for developing cutting-edge solutions in industrial automation, biomedical imaging, and human–machine interfaces. Fluent in Arabic and English, he brings international insight into interdisciplinary research projects, merging software innovation with engineering systems.

Profile

Scopus
Orcid
Google Scholar

🎓 Education

Hussam holds a BSc and MSc in Telecommunication and Electronic Engineering, equipping him with deep theoretical and practical knowledge in signal processing, system design, and electronics. Currently, he is pursuing a PhD in Information Science at the University of Miskolc, focusing on AI-based vision systems for autonomous drone operations.

💼 Experience

Hussam serves as an Assistant Researcher and Part-time Software Engineer at FIEK, where he builds C# monitoring software, implements PLC-to-PC communications, and automates data workflows using Linux, Docker, and Excel. His professional journey includes work as a Full Stack Developer and Telecom Engineer, with experience in GUI development, DevOps collaboration, and .NET technologies.

🔬 Research Interests

📸 Computer Vision & Image Processing

🤖 Autonomous Systems & Drone Navigation

🩺 Biomedical Imaging & Oxygen Saturation Estimation

🔬 Optical System Design (Zemax)

⚙️ Industrial Automation & Data Visualization

🧠 Human–Machine Interfaces & Sensor Integration

📚 Selected Publications

Enhancing Machine Learning Techniques in VSLAM for Robust Autonomous Unmanned Aerial Vehicle Navigation
📅 2025-04-02 | 📰 Electronics
📌 Focus: Improving Visual SLAM with machine learning for UAVs in complex environments.
🔗 DOI: 10.3390/electronics14071440
👥 Co-author: József Vásárhelyi

Comparing the Effectiveness and Performance of Image Processing Algorithms in Face Recognition
📅 2024-05-22 | 📚 Conference Paper
📌 Focus: Evaluation of various image processing techniques for face recognition applications.
🔗 DOI: 10.1109/ICCC62069.2024.10569864
👥 Co-author: József Vásárhelyi

FPGA Implementation in Mobile Robot Applications: State of the Art Review
📅 2023-12-20 | 📰 Multidiszciplináris Tudományok
📌 Focus: Overview of FPGA-based systems in robotics.
🔗 DOI: 10.35925/j.multi.2023.2.21
👥 Co-authors: Omar M. Salih, Noha Hammami

An Overview of Energies Problems in Robotic Systems
📅 2023-12-14 | 📰 Energies
📌 Focus: Challenges in energy management for robotic systems.
🔗 DOI: 10.3390/en16248060
👥 Co-authors: József Vásárhelyi, Omar M. Salih, Rabab Benotsname

A Review of Using Visual Odometry Methods in Autonomous UAV Navigation in GPS-Denied Environments
📅 2023-12-01 | 📰 Acta Universitatis Sapientiae, Electrical and Mechanical Engineering
📌 Focus: Use of visual odometry for UAVs in GPS-denied settings.
🔗 DOI: 10.2478/auseme-2023-0002
👥 Co-author: József Vásárhelyi

 

 

 

 

Dr. Zhe Wang | Wireless Network | Best Researcher Award

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Dr. Zhe Wang | Wireless Network | Best Researcher Award

Guangxi Minzu University, China.

Dr. Zhe Wang is an Assistant Professor at the School of Artificial Intelligence, Guangxi Minzu University. He earned his PhD in Electric Power and Intelligent Information from Guangxi University in 2019. His research focuses on Simultaneous Wireless Information and Power Transfer (SWIPT), wireless power transfer, optimization, and AI applications. Dr. Wang has contributed significantly to the field of federated learning and privacy-preserving AI techniques, with publications in high-impact journals.

Profile

Scopus

🎓 Education

Dr. Zhe Wang holds a PhD in Electric Power and Intelligent Information from Guangxi University, China, which he obtained in 2019. His academic journey has been centered on advancing research in wireless power transfer, optimization techniques, and AI applications in energy systems. With a strong foundation in electrical engineering and intelligent systems, Dr. Wang has contributed to cutting-edge innovations in Simultaneous Wireless Information and Power Transfer (SWIPT). His expertise bridges the gap between power systems and artificial intelligence, driving new methodologies for efficient and intelligent energy solutions.

💼 Experience

Dr. Zhe Wang is an Assistant Professor at the School of Artificial Intelligence, Guangxi Minzu University, a position he has held since 2021. Prior to this, he served as a Lecturer at the School of Information Engineering at the same university from 2019 to 2020. His academic contributions focus on advancing research and education in artificial intelligence and information engineering, fostering innovation in these rapidly evolving fields.

🔬 Research Interests

Simultaneous Wireless Information and Power Transfer (SWIPT)

Wireless Power Transfer

Optimization Techniques

AI Applications in Power Systems

Privacy-Preserving AI & Federated Learning

🏆 Awards & Recognitions

Outstanding Research Contribution Award – Guangxi Minzu University

Best Paper Award – International Conference on Artificial Intelligence Applications

Innovation Excellence Honor – SWIPT & Wireless Power Transfer Research

📚 Publications

1️⃣ "A Review of Privacy-Preserving Research on Federated Graph Neural Networks"

Journal: Neurocomputing (2024)

Cited by: 2 articles

2️⃣ "A Review of Secure Federated Learning: Privacy Leakage Threats, Protection Technologies, Challenges, and Future Directions"

Journal: Neurocomputing (2023).

Cited by: 22 articles

 

 

Prof. Ping-Feng Xu | Mathematical Statistics | Best Researcher Award

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Prof. Ping-Feng Xu | Mathematical Statistics | Best Researcher Award

Northeast Normal University, China.

Ping-Feng Xu is the Deputy Dean at Northeast Normal University, specializing in complex network analysis, graphical models, causal inference, educational statistics, and psychological measurement. He earned his degree in Probability Theory and Mathematical Statistics from Northeast Normal University in 2010. He has held visiting positions at Hong Kong Baptist University, University of Wisconsin-Madison, Hang Seng School of Management, and Hang Seng University of Hong Kong. With a strong research background, he has contributed significantly to structural learning, latent variable selection, and high-dimensional graphical models.

Profile

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Education 🎓

Prof. Ping-Feng Xu completed his undergraduate studies at Northeast Normal University in 2010, where he majored in Probability Theory and Mathematical Statistics. Following his academic foundation, he expanded his research and professional experience through visiting scholar positions at several prestigious institutions. These include Hong Kong Baptist University, where he gained insights into advanced statistical methods, University of Wisconsin-Madison, which enhanced his expertise in complex network analysis, Hang Seng School of Management in Hong Kong, and Hang Seng University of Hong Kong, where he collaborated on projects involving educational statistics and causal inference.

Experience 🏫

Prof. Ping-Feng Xu currently serves as the Deputy Dean at Northeast Normal University, where he plays a key role in academic leadership and research direction. His professional contributions extend beyond the university, as he holds influential positions in major academic organizations. Since April 2023, he has been the Vice Chairman of the Education Statistics and Management Branch of the Chinese Society of Applied Statistics, where he helps shape the development of statistical education in China. Additionally, he has served as the Executive Director of the Data Science and Artificial Intelligence Branch of the same society since March 2018, working to promote the integration of data science and AI in applied statistics.

Research Interests 🔬

Complex Network Analysis

Graphical Models

Causal Inference

Educational Statistics

Psychological Measurement

Awards & Recognitions 🏆

Vice Chairman, Education Statistics and Management Branch, Chinese Society of Applied Statistics

Executive Director, Data Science and Artificial Intelligence Branch, Chinese Society of Applied Statistics

Selected Publications 📚

The Improved EMS Algorithm for Latent Variable Selection in M3PL Model
Applied Psychological Measurement (2025)
Contributors: Laixu Shang, Ping-Feng Xu, Na Shan, Man-Lai Tang, Qian-Zhen Zheng

Fitting Penalized Estimator for Sparse Covariance Matrix with Left-Censored Data by the EM Algorithm
Mathematics (2025)
Contributors: Shanyi Lin, Qian-Zhen Zheng, Laixu Shang, Ping-Feng Xu, Man-Lai Tang

Bayesian Adaptive Lasso for the Detection of Differential Item Functioning in Graded Response Models
Journal of Educational and Behavioral Statistics (2024)
Contributors: Na, Ping-Feng Xu

A Generalized Expectation Model Selection Algorithm for Latent Variable Selection in Multidimensional Item Response Theory Models
Statistics and Computing (2024)
Contributors: Laixu Shang, Qian-Zhen Zheng, Ping-Feng Xu, Na Shan, Man-Lai Tang

Transmission Mechanisms of Geopolitical Risks to the Crude Oil Market—A Pioneering Two-Stage Geopolitical Risk Analysis Approach
Energy (2023)
Contributors: Jing-Wen Jiao, Jun-Ping Yin, Ping-Feng Xu, Juan Zhang, Yuan Liu