Mercedes Premalatha Ramesh | Computer Science and Artificial Intelligence | Young Scientist Award

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Young Scientist Award

Mercedes Premalatha Ramesh — Senior Research Engineer, Air Traffic Management Research Institute (ATMRI), and Part-time PhD Researcher in Mechanical and Aerospace Engineering, Nanyang Technological University (NTU), Singapore.

Mercedes Premalatha Ramesh
Affiliation Nanyang Technological University (NTU)
Country Singapore
Google Scholar _TNjmuMAAAAJ
Documents 19
Citations 5
h-index 1
Subject Area Multi Agents and Reinforcement Learning
Event Top Teachers Awards
Scopus ID 59234522100
ORCID 0009-0007-7242-0898

Mercedes Premalatha Ramesh is a Senior Research Engineer at NTU’s Air Traffic Management Research Institute and a part-time PhD researcher in Mechanical and Aerospace Engineering. Her academic and professional experience spans aviation, robotics, intelligent manufacturing and semiconductor systems, with research focused on human-AI collaboration, multi-agent reinforcement learning and autonomous systems for safety-critical applications.[1]

Abstract

Mercedes Premalatha Ramesh’s research is situated at the intersection of artificial intelligence, autonomous systems, human factors and safety-critical aviation. Her work addresses multi-agent reinforcement learning, dynamic airspace sectorization, adaptive air traffic control automation, workload-aware decision support and collision-constrained robot swarms. Her research portfolio also includes low-resource language AI, illustrating an interdisciplinary approach to intelligent systems that combines computational methods with operational and human-centred requirements.[1]

Keywords

Computer Science; Artificial Intelligence; Multi-Agent Systems; Reinforcement Learning; Robot Perception; Swarm Robotics; Air Traffic Optimisation; Human-AI Collaboration; Autonomous Systems; Large Language Models.

Introduction

Mercedes Premalatha Ramesh’s academic profile combines engineering education with applied research in aviation and intelligent autonomous systems. She holds a Bachelor of Engineering in Electronics and Communication Engineering and a Master of Technology in Intelligent Systems from the National University of Singapore. Her professional R&D experience includes work associated with A*STAR ARTC, ST Engineering, Micron, Hindustan Aeronautics Limited and ROS-Industrial, providing an interdisciplinary foundation for research involving robotics, industrial systems and artificial intelligence.

At NTU ATMRI, her current research addresses the interaction between human operators and increasingly autonomous decision-support systems. This focus is particularly relevant to air traffic management, where AI-based optimisation must operate within safety, workload, interpretability and human-oversight constraints. Her research therefore considers both algorithmic performance and the conditions under which intelligent systems can be responsibly integrated into operational environments.

Research Profile

The research profile comprises six principal streams: AIR-LION and multi-sector planning for air traffic management simulation; multi-agent reinforcement learning for dynamic airspace sectorisation; adaptive automation and AI-assisted air traffic control coordination; workload-aware human-AI decision support; collision-constrained and shared-control robot swarms; and low-resource Tamil-Malay machine translation. These streams connect reinforcement learning, autonomous control, human factors and language technologies across different safety- and resource-constrained settings.

Her consultancy and industry-oriented R&D experience further connects academic research with practical engineering requirements. Work across aviation, robotics, intelligent manufacturing and semiconductor environments has contributed to a research orientation centred on deployability, system constraints and interdisciplinary collaboration.[1]

Research Contributions

Mercedes Premalatha Ramesh develops research approaches for trustworthy human-AI systems in safety-critical environments. Her contributions combine multi-agent reinforcement learning, human-factors evidence and autonomous-system control to address coordination and decision-support challenges while retaining human oversight. Areas of contribution include dynamic airspace sectorisation, adaptive air traffic control automation, workload-aware decision support and collision-constrained swarm control.[1]

Her interdisciplinary work additionally extends to low-resource language AI, including computational analysis and translation-support systems for Tamil and related language contexts. The resulting research portfolio links simulation and algorithm development with operational constraints and human-centred evaluation, providing a foundation for further investigation of explainable and responsible autonomy.

Publications

Available publication records indicate more than nine peer-reviewed indexed outputs during 2024–2026, covering multi-agent reinforcement learning, air traffic control automation, human-AI decision support, swarm robotics, robot navigation and low-resource language AI. The publication record includes work presented through IEEE venues and Springer-associated research, together with a 2026 article in Education Sciences. Bibliographic records and author profiles provide the principal basis for assessing the documented publication portfolio. [1] [3] [4] [5]

Representative research includes a 2024 IEEE ISIE contribution on quantitative analysis of robot navigation under rain conditions, a 2026 Education Sciences study examining stream-aware vocabulary demands in Singapore secondary Tamil textbooks, and a 2026 Human-Computer Interaction conference contribution addressing linguistic ambiguity in low-resource translation-support systems. [3] [4] [5]

Research Impact

The documented research profile demonstrates an interdisciplinary trajectory spanning artificial intelligence, aviation, robotics and language technologies. Its potential research impact lies particularly in connecting algorithmic approaches such as reinforcement learning with human-centred and safety-oriented requirements. Applications in dynamic airspace management, autonomous coordination and decision support are relevant to the development of intelligent systems that must operate under uncertainty while maintaining human supervision.

The publication record also demonstrates cross-domain collaboration, with research extending from autonomous robot navigation to educational language technologies. The available scholarly metrics report 19 documents, 5 citations and an h-index of 1 in the supplied profile data, while individual publications provide additional evidence of research activity and interdisciplinary engagement. [1] [2]

Award Suitability

For consideration under the Young Scientist Award category, Mercedes Premalatha Ramesh’s profile presents a combination of early-career research activity, multidisciplinary engineering experience and work in emerging AI applications. Particularly relevant elements include research on multi-agent reinforcement learning, human-AI collaboration, autonomous systems and safety-critical air traffic management, together with peer-reviewed scholarly outputs and industry-linked R&D experience.

The suitability assessment should be based on independently verifiable evidence, including indexed publications, documented research projects, institutional affiliation, scholarly profiles and supporting certificates. Professional memberships should be listed only where current membership can be documented. NTU identification and relevant academic or professional certificates may be supplied as supporting evidence during the award-submission process.[1] [2]

Conclusion

Mercedes Premalatha Ramesh’s research profile reflects an interdisciplinary focus on artificial intelligence, multi-agent learning, human-AI collaboration and autonomous systems. Her work connects aviation and robotics research with human-centred considerations and extends into low-resource language AI. The combination of applied R&D experience, peer-reviewed publications and research activity in safety-critical intelligent systems provides a documented basis for consideration within an early-career scientific recognition framework.[1]

References

  1. Google Scholar. (n.d.). Mercedes Premalatha Ramesh — Google Scholar author profile. https://scholar.google.com/citations?user=_TNjmuMAAAAJ&hl=en
  2. Elsevier. (n.d.). Scopus author details: Mercedes Premalatha Ramesh, Author ID 59234522100. Scopus. https://www.scopus.com/pages/authors/59234522100
  3. Ramu, U., Ramesh, M. P., Paranthaman, K., Khan, S. G. S., & Fern, T. C. (2024). Generalized framework for quantitative analysis of robot navigation under rain conditions. 2024 IEEE 33rd International Symposium on Industrial Electronics (ISIE), 1–7. https://doi.org/10.1109/ISIE54533.2024.10595805
  4. Pal Thamburaj, K., & Ramesh, M. P. (2026). Stream-aware vocabulary demands in Singapore secondary Tamil textbooks: A morphology-and multiword-unit-sensitive corpus analysis, with a textbook-faithful GenAI item benchmark. Education Sciences, 16(8), 1270. https://doi.org/10.3390/educsci16081270
  5. Pal Thamburaj, K., & Ramesh, M. P. (2026). Deciphering divergence: Visualizing linguistic ambiguity in low-resource translation support systems. International Conference on Human-Computer Interaction, 581–591. https://link.springer.com/chapter/10.1007/978-3-032-30826-9_60

Jindong Wang | Computer Science | Innovative Research Award

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Innovative Research Award

Jindong Wang — College of William & Mary, United States

Jindong Wang
Affiliation College of William & Mary
Country United States
Google Scholar hBZ_tKsAAAAJ
Documents 199
Citations 28,805
h-index 61
Subject Area AI
Event Top Teachers Awards
Scopus ID 57190969217
ORCID 0000-0002-4833-0880

Jindong Wang is a computer scientist whose research spans machine learning, transfer learning, large language models, foundation models, federated learning, trustworthy artificial intelligence, and evaluation. His reported scholarly record includes 199 documents, more than 28,000 citations, and an h-index of 61, alongside research outputs appearing in major venues in artificial intelligence and machine learning.

His academic profile combines theoretical and applied machine learning with open-source research infrastructure, interdisciplinary evaluation, and responsible AI. His work includes widely used resources for transfer learning, semi-supervised learning, robust machine learning, personalized federated learning, and large-language-model evaluation. The profile also records academic service, teaching, invited talks, awards, grants, and research collaborations across academia and industry.[1]

Abstract

Jindong Wang’s research profile reflects sustained work across machine learning foundations, transfer learning, large language models, generative AI, federated learning, and responsible AI. His reported record combines more than 100 research publications, open-source systems, scholarly service, teaching, and externally supported projects, with selected publications receiving substantial citation attention. [1]

Keywords

Machine learning; transfer learning; domain generalization; large language models; foundation models; generative AI; multimodal AI; semi-supervised learning; federated learning; trustworthy AI; responsible AI; robustness; generalization; AI evaluation; personalized AI.

Introduction

Jindong Wang’s research addresses methods for developing machine-learning systems that can generalize across domains, adapt to new data and tasks, and operate reliably under changing conditions. Earlier work emphasized transfer learning, domain generalization, federated learning, and semi-supervised learning, while more recent research extends these interests toward foundation models, large language models, multimodal systems, evaluation, personalization, and trustworthy AI.

His professional experience includes an assistant professorship at William & Mary beginning in 2025 and previous research leadership at Microsoft Research. His academic training includes a Ph.D. in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, following undergraduate study in engineering at North China University of Technology. He also undertook visiting research at The Hong Kong University of Science and Technology.

Research Profile

The research profile covers AI foundations, large language models and generative AI, and responsible AI. In AI foundations, the work includes machine learning, transfer learning, out-of-distribution generalization, semi-supervised learning, and federated learning. In generative AI, the emphasis includes LLM understanding and evaluation, agentic systems, multimodal models, fine-tuning, and adaptation. Responsible AI interests include human-centered AI, AI and society, trustworthiness, and alignment.[1]

  • AI Foundations: machine learning, transfer learning, OOD generalization, semi-supervised learning, and federated learning.
  • Large Language Models and Generative AI: LLM evaluation, foundation-model understanding, agentic AI, multimodal models, fine-tuning, and adaptation.
  • Responsible AI: human-centered AI, trustworthy machine learning, AI and society, safety, and alignment.

Professional service has included editorial responsibilities and program-committee leadership for journals and conferences such as IEEE Transactions on Neural Networks and Learning Systems, Journal of Computer Science and Technology, ACM Transactions on Intelligent Systems and Technology, NeurIPS, ICLR, ICML, KDD, IJCAI, AAAI, CIKM, and AIES.

Research Contributions

A central contribution of Jindong Wang’s work is the development and synthesis of methods for transfer learning and domain generalization, including surveys and algorithms addressing adaptation to previously unseen distributions. These contributions provide conceptual and methodological foundations for learning systems that must operate beyond their original training environments. [2]

His research also contributes to semi-supervised and federated learning. FlexMatch introduced curriculum pseudo-labeling for semi-supervised learning, while related work has examined federated transfer learning and healthcare-oriented distributed learning. Such studies address settings where labeled data, centralized training, or uniform data distributions are limited. [3] [4]

More recent contributions focus on large language models and foundation models, particularly their evaluation, robustness, personalization, multimodal capabilities, and societal implications. The research direction emphasizes systematic assessment rather than relying solely on aggregate benchmark scores, connecting model behavior with robustness, bias, generalization, and practical deployment considerations.

Open-source activities complement the publication record. The Transfer Learning repository, PromptBench, USB, RobustLearn, and PersonalizedFL provide research materials, implementations, datasets, evaluation utilities, or experimental infrastructure. Their reported adoption illustrates an emphasis on reproducibility and community-accessible research tooling.

Publications

Jindong Wang’s publications advance transfer learning, domain generalization, semi-supervised learning, federated learning, and large-language-model evaluation, with influential works including FlexMatch and domain-generalization research.

Jindong Wang’s publication record spans machine learning, computer vision, natural language processing, federated learning, domain generalization, and foundation-model research. Selected highly cited works include a survey of large language model evaluation, a survey of domain generalization, FlexMatch for semi-supervised learning, deep subdomain adaptation networks, and FedHealth for federated transfer learning.[1] [2] [3] [4] [5]

  1. Chang, Y., Wang, X., Wang, J., et al. (2024). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology.
  2. Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. S. (2022). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering, 35(8), 8052–8072.
  3. Zhang, B., Wang, Y., Hou, W., Wu, H., Wang, J., Okumura, M., & Shinozaki, T. (2021). FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling. NeurIPS 2021.
  4. Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., & He, Q. (2020). Deep subdomain adaptation network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1713–1722.
  5. Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems.

Jindong Wang is also the author of the monograph Introduction to Transfer Learning: Algorithms and Practice, co-authored with Yiqiang Chen and published by Springer Nature in 2023, and contributed the chapter “Activity Recognition” to the Machine Learning for Data Science Handbook. His teaching and tutorial activities further connect the publication program with graduate education and professional dissemination.

Research Impact

The reported scholarly metrics indicate substantial bibliometric visibility, with 199 documents, 28,805 citations, and an h-index of 61 and The author has published 135 Scopus-indexed documents, receiving 15,924 citations and achieving an h-index of 42. His selected publications have appeared in venues including NeurIPS and major IEEE and ACM journals, while his work has also been represented through open-source projects and research tutorials. Citation counts should be interpreted as quantitative indicators of scholarly attention rather than as a complete measure of research quality or societal value. [1]

Reported open-source adoption includes more than 13,000 stars for the Transfer Learning repository, approximately 2,500 for PromptBench, and more than 1,400 for USB, alongside smaller but specialized communities around RobustLearn and PersonalizedFL. These resources support reproducible experimentation and provide researchers with implementations and evaluation infrastructure.

Media and professional dissemination have included coverage or discussion involving Forbes, MIT Technology Review, Microsoft Research, PyTorch-related channels, TechXplore, LlamaIndex, and other technology publications. Invited and keynote talks have addressed foundation models, LLM evaluation, trustworthy AI, multimodal models, agentic systems, and AI safety across universities, conferences, and research forums.

Award Suitability

The documented profile presents several factors relevant to an innovative research recognition: a sustained publication record, significant citation activity, contributions spanning multiple machine-learning areas, open-source research infrastructure, academic and professional service, teaching, and interdisciplinary engagement. The record also lists competitive research awards and grants from organizations including Google, NVIDIA, Amazon, Microsoft, AMD, Cohere Labs, Modal, and William & Mary.[1]

  • Research distinction: substantial publication and citation activity across established machine-learning research areas.
  • Innovation: development of methods, benchmarks, surveys, and open-source tools addressing emerging machine-learning problems.
  • Community contribution: open-source repositories, tutorials, academic service, and research dissemination.
  • Contemporary relevance: active research on foundation models, LLM evaluation, multimodal AI, personalization, robustness, and responsible AI.

Awards and grants reported in the profile include the Gemini Academic Research Award, Google TPU Machine Learning Research Award, AAAI Outstanding SPC Award, NVIDIA Academic Grant Program Award, Amazon Research Award, Google DeepMind Research Award, Microsoft Accelerate Foundation Model Research Grant, AMD University Program AI & HPC Award, Cohere Labs Research Grant, and several best- or outstanding-paper distinctions.

Conclusion

Jindong Wang’s academic profile combines research in machine learning foundations with contemporary work on large language models, foundation models, federated learning, multimodal systems, and responsible AI. The combination of publications, citations, open-source projects, academic service, teaching, awards, grants, and invited research activities provides a broad basis for evaluating his suitability for an innovative research recognition.[1]

References

  1. Google Scholar (n.d.). Google Scholar author Citations details: Jindong Wang,. https://scholar.google.com/citations?user=hBZ_tKsAAAAJ&hl=en&oi=sra
  2. Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. S. (2022). Generalizing to unseen domains: A survey on domain generalization. IEEE Transactions on Knowledge and Data Engineering, 35(8), 8052–8072. https://doi.org/10.1109/TKDE.2022.3178128
  3. Zhang, B., Wang, Y., Hou, W., Wu, H., Wang, J., Okumura, M., & Shinozaki, T. (2021). FlexMatch: Boosting semi-supervised learning with curriculum pseudo labeling. Advances in Neural Information Processing Systems (NeurIPS 2021). https://proceedings.neurips.cc/paper_files/paper/2021/hash/995693c15f439e3d189b06e89d145dd5-Abstract.html
  4. Zhu, Y., Zhuang, F., Wang, J., Ke, G., Chen, J., Bian, J., Xiong, H., & He, Q. (2020). Deep subdomain adaptation network for image classification. IEEE Transactions on Neural Networks and Learning Systems, 32(4), 1713–1722. https://doi.org/10.1109/TNNLS.2020.2988928
  5. Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020). FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems. https://doi.org/10.1109/MIS.2020.2988604

Ean Teng Khor | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Ean Teng Khor
National Institute of Education, Nanyang Technological University, Singapore
Emmanuel Omeje
Affiliation National Institute of Education, Nanyang Technological University
Country Singapore
Scopus 54393424800
Documents 30
Citations 176
h-index 6
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0001-6817-9332
Google Scholar Ra1HNaIAAAAJ

Ean Teng Khor is an education researcher, lecturer, and academic leader whose work integrates artificial intelligence, learning analytics, educational data mining, and technology-enhanced learning. Based at the National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore, she has contributed to the advancement of AI-enabled educational innovation through research, teaching, academic leadership, and externally funded projects. Her research portfolio demonstrates sustained engagement with personalised learning systems, educational predictive analytics, generative artificial intelligence, conversational agents, and human–AI collaboration for learning.[1]

Abstract

This article presents an academic profile of Ean Teng Khor and evaluates her scholarly contributions in the fields of Artificial Intelligence in Education, Learning Analytics, Educational Data Mining, and Technology-Enhanced Learning. Through interdisciplinary research, externally funded projects, academic leadership, and international engagement, she has contributed to the development of evidence-based educational innovations designed to support personalised learning, educator development, and digital transformation across educational sectors. Her work demonstrates a sustained focus on integrating advanced computational methods with learning sciences to improve educational outcomes and learner experiences.[1]

Keywords

Artificial Intelligence in Education; Learning Analytics; Educational Data Mining; Generative AI; Conversational AI; Personalised Learning; Machine Learning; Educational Technology; Adaptive Learning; Human–AI Collaboration.

Introduction

The increasing integration of artificial intelligence into educational environments has created opportunities for innovative approaches to teaching, learning, assessment, and learner support. Within this evolving landscape, Ean Teng Khor has established a research profile focused on leveraging data-driven methods and intelligent technologies to support educational improvement. Her academic work spans generative AI, learning analytics, educational data mining, predictive modelling, adaptive learning systems, and technology-enhanced pedagogy.[1]

Her contributions have been developed through collaborations with educators, policymakers, researchers, and institutions across multiple educational sectors. This combination of technical expertise and educational insight has enabled the translation of research findings into practical educational applications that address contemporary challenges in teaching and learning.[1]

Research Profile

Ean Teng Khor earned a Bachelor of Information Technology (Honours) in 2005 and a Master of Science in Information Technology in 2007 from Multimedia University, followed by a Doctor of Philosophy from Universiti Sains Malaysia in 2015. Her academic appointments include positions at Wawasan Open University, East Asia Institute of Management, Nanyang Technological University, and the National Institute of Education.[1]

Her research expertise encompasses AI-enabled learning design, conversational AI, educational predictive analytics, personalised learning systems, machine learning applications in education, digital learning ecosystems, and data literacy development. She currently serves as Founding Leader of the Learning Analytics Special Interest Group at NIE and participates in multiple editorial, reviewing, and conference leadership roles within the international learning sciences community.[1]

Research Contributions

A defining characteristic of Ean Teng Khor’s research is the application of artificial intelligence and analytics to support personalised and adaptive learning. As Principal Investigator and Co-Principal Investigator, she has led and contributed to multiple competitive research projects funded by organizations including Singapore’s Ministry of Education, SkillsFuture Singapore, and AI Singapore.[1]

  • Development of AI-mediated learning systems using retrieval-augmented generation and intelligent interventions.
  • Research on teacher data literacy and evidence-informed educational decision-making.
  • Learning analytics frameworks for workplace and lifelong learning environments.
  • Adaptive AI-supported language learning and multilingual tutoring technologies.
  • Educational predictive modelling for identifying and supporting at-risk learners.

Beyond funded research, she has contributed to academic community development through editorial board memberships, peer review activities, keynote presentations, conference leadership, educator professional development initiatives, and academic assessment roles across international institutions and scholarly organizations.[1]

Publications

Ean Teng Khor’s publication record includes journal articles and conference papers addressing learning analytics, educational technology adoption, microlearning, predictive modelling, and personalised learning. Several publications have appeared in internationally recognized journals and have contributed to scholarly discussions surrounding data-informed educational practice and AI-supported learning environments.[2][3][4][5]

Research Impact

According to the Scopus author profile, Ean Teng Khor has accumulated 176 citations across indexed publications with an h-index of 6. These metrics reflect measurable scholarly engagement within fields related to educational technology, learning analytics, and artificial intelligence in education.[1]

Her impact extends beyond publication metrics through successful supervision of student researchers, leadership of collaborative research initiatives, development of AI-enabled educational tools, and professional engagement with educators and policymakers. The practical orientation of her research has supported knowledge transfer between academic scholarship and educational practice.[1]

Award Suitability

Ean Teng Khor’s academic profile demonstrates several characteristics commonly associated with recognition through research and innovation awards. These include sustained scholarly productivity, leadership of externally funded projects, contributions to educational innovation, international academic service, successful mentorship of emerging researchers, and recognition through multiple research awards and distinctions.[1]

Her research agenda aligns with contemporary priorities in education and digital transformation by addressing responsible applications of artificial intelligence, personalised learning, educator capacity building, and data-informed decision-making. The combination of research excellence, practical impact, and academic leadership provides a substantive basis for consideration within academic recognition programs such as the Top Teachers Awards.[1]

Conclusion

Ean Teng Khor has established a multidisciplinary academic profile at the intersection of artificial intelligence, learning sciences, educational technology, and analytics. Through research leadership, funded projects, scholarly publications, teaching innovation, and service to the international academic community, she has contributed to advancing understanding of how intelligent technologies can support learning and educational improvement. Her record of scholarship and professional engagement reflects an ongoing commitment to evidence-based educational innovation and the responsible integration of AI within learning environments.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Ean Teng Khor, Author ID 54393424800. Scopus. https://www.scopus.com/pages/authors/54393424800
  2. Khor, E. T., & K. M. (2023). A systematic review of the role of learning analytics in supporting personalized learning. Education Sciences, 14(1), 51. DOI: https://doi.org/10.3390/educsci14010051
  3. Teng, K. E. (2014). An Analysis of ODL Student Perception and Adoption Behaviour using Technology Acceptance Model. International Review of Research in Open and Distance Learning, 15(6), 275–288. DOI: https://doi.org/10.19173/irrodl.v15i6.1732
  4. Puah, S., Khalid, M. I. S., Looi, C. K., & Khor, E. T. (2022). Investigating working adults’ intentions to participate in microlearning using the decomposed theory of planned behaviour. British Journal of Educational Technology, 53(2), 367–390. DOI: https://doi.org/10.1111/bjet.13170
  5. Khor, E. T. (2022). A data mining approach using machine learning algorithms for early detection of low-performing students. The International Journal of Information and Learning Technology, 39(2), 122–132. DOI: https://doi.org/10.1108/IJILT-09-2021-0144

Huili Zhang | Computer Science and Artificial Intelligence | Innovative Research Award

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Innovative Research Award

Huili Zhang
Shanghai University, China

Huili Zhang
Affiliation Shanghai University
Country China
Scopus ID 58607120700
Documents 17
Citations 223
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event Top Teachers Awards
ORCID 0000-0002-3336-1756

The Innovative Research Award recognizes researchers whose scholarly activities demonstrate originality, methodological rigor, and measurable impact within their respective disciplines. Huili Zhang of Shanghai University has established a research profile centered on artificial intelligence, medical image analysis, radiomics, and intelligent diagnostic systems. Through interdisciplinary collaboration and the application of advanced machine learning methods to healthcare challenges, Zhang has contributed to the development of computational frameworks that support disease detection, classification, and clinical decision-making.[1]

Abstract

Huili Zhang’s research integrates artificial intelligence and medical imaging technologies to improve diagnostic accuracy and predictive modeling in healthcare. Her publications address multimodal ultrasound analysis, radiomics, deep learning, and knowledge distillation techniques, emphasizing clinically relevant solutions for cancer diagnosis and treatment evaluation. The body of work demonstrates a consistent focus on translating computational innovation into practical medical applications.[2]

Keywords

Artificial Intelligence, Deep Learning, Medical Imaging, Radiomics, Ultrasound Diagnostics, Knowledge Distillation, Computer-Aided Diagnosis, Healthcare Analytics.

Introduction

The convergence of artificial intelligence and healthcare has created opportunities for improved diagnostic efficiency and personalized treatment strategies. Within this evolving landscape, Huili Zhang has contributed to research that applies machine learning and image-based analytics to complex clinical problems. Her studies demonstrate the growing importance of data-driven methodologies in modern medical practice.[3]

Research Profile

As a researcher affiliated with Shanghai University, Zhang has developed expertise in computer science and artificial intelligence with a strong emphasis on biomedical applications. Her scholarly record includes peer-reviewed publications focused on multimodal imaging, radiomics-based prediction models, and intelligent healthcare systems. The available bibliometric indicators demonstrate growing academic influence across interdisciplinary domains.[1]

Research Contributions

  • Development of multi-view and multimodal deep learning frameworks for liver cancer diagnosis using ultrasound imaging.
  • Advancement of generalized knowledge distillation approaches for medical image interpretation.
  • Creation of MRI-based radiomics models for differentiating spinal multiple myeloma from metastatic lesions.
  • Application of dual-modal ultrasound and molecular data integration for predicting chemotherapy response in breast cancer patients.
  • Research into deep learning radiomics for distinguishing benign and malignant breast conditions.

Publications

  1. Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities (2026).
  2. Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images (2024).
  3. Radiomics Model Based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study (2024).
  4. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer (2023).
  5. Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer (2023).

Research Impact

The research output attributed to Zhang reflects a commitment to improving diagnostic workflows through advanced computational techniques. By combining machine learning, radiomics, and multimodal imaging data, her work contributes to enhanced disease characterization and supports evidence-based clinical decision-making. Citation activity and publication placement indicate recognition within the scientific community.[4]

Award Suitability

Huili Zhang’s research portfolio aligns with the objectives of the Innovative Research Award due to its interdisciplinary nature, methodological innovation, and relevance to healthcare technology. The integration of artificial intelligence with clinical imaging illustrates a forward-looking approach that addresses contemporary challenges in medical diagnostics while contributing to scientific advancement.[5]

Conclusion

Huili Zhang represents a growing cohort of researchers leveraging artificial intelligence to transform healthcare diagnostics. Her contributions to medical imaging, radiomics, and deep learning demonstrate both scholarly rigor and practical relevance. These achievements support recognition through the Innovative Research Award and reflect continued potential for future scientific impact.

References

  1. Elsevier. (n.d.). Scopus author details: Huili Zhang, Author ID 58607120700. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58607120700
  2. Zhang, H. (2026). Multi-view doubly supervised knowledge distillation for diagnosis of liver cancers with imbalanced ultrasound imaging modalities.
    DOI: https://doi.org/10.1016/j.engappai.2026.115252
  3. Zhang, H. (2024). Multi-View Disentanglement-based Bidirectional Generalized Distillation for Diagnosis of Liver Cancers with Ultrasound Images.
    DOI: https://doi.org/10.1016/j.ipm.2024.103855
  4. Zhang, H. (2024). Radiomics Model based on MRI to Differentiate Spinal Multiple Myeloma from Metastases: A Two-center Study.
    DOI: https://doi.org/10.1016/j.jbo.2024.100599
  5. Zhang, H. (2023). Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
    DOI: https://doi.org/10.1016/j.acra.2023.03.036
  6. Zhang, H. (2023). Deep Learning Radiomics of Ultrasonography for Differentiating Sclerosing Adenosis from Breast Cancer.
    DOI: https://doi.org/10.3233/CH-221608

Dehui Du | Computer Science | Innovative Research Award

Published on by Top Teachers

Innovative Research Award

Dehui Du
East China Normal Universty, China

Dehui Du
Affiliation East China Normal Universty
Country China
Scopus ID 14044898400
Documents 68
Citations 504
h-index 11
Subject Area Computer Science
Event Top Teachers Awards

The Innovative Research Award recognizes scholars whose research activities demonstrate originality, methodological rigor, and measurable contributions to the advancement of scientific knowledge. Dehui Du of East China Normal Universty has established a research profile in computer science through investigations in causal inference, explainable artificial intelligence, reinforcement learning, large language models, autonomous systems, and rare event detection. His publication record, citation performance, and participation in internationally recognized conferences indicate sustained engagement with contemporary research challenges and emerging computational methodologies.[1]

Abstract

Dehui Du’s research focuses on the intersection of machine learning, causal reasoning, explainable artificial intelligence, and intelligent systems. His scholarly output addresses practical and theoretical problems associated with reinforcement learning, counterfactual analysis, autonomous driving, and large language models. Through conference publications and collaborative research efforts, he has contributed to the development of computational frameworks designed to improve transparency, reliability, and performance in artificial intelligence systems.[2]

Keywords

Artificial Intelligence, Computer Science, Reinforcement Learning, Causal Inference, Explainable AI, Large Language Models, Counterfactual Analysis, Autonomous Driving.

Introduction

Recent advances in artificial intelligence increasingly require interpretable, reliable, and data-efficient learning systems. Researchers working at the intersection of machine learning and causal reasoning play an important role in addressing these challenges. Dehui Du’s work reflects this direction by integrating explainability, counterfactual reasoning, and advanced learning architectures into practical computational frameworks that support decision-making and predictive performance.[3]

Research Profile

With 68 indexed publications, 504 citations, and an h-index of 11, Dehui Du has developed a scholarly profile characterized by interdisciplinary research across machine learning and intelligent computing. His collaborations span topics including causal inference, experience replay methods, language model reasoning, autonomous systems, and counterfactual identifiability. These areas are increasingly relevant to both academic research and industrial applications.[1]

Research Contributions

  • Development of explainable reinforcement learning approaches supported by causal inference.
  • Advancement of counterfactual generation techniques for rare event detection.
  • Research on preference-guided reverse reasoning for large language models.
  • Theoretical investigations into exogenous isomorphism and counterfactual identifiability.
  • Contributions to imitation learning frameworks for autonomous driving systems.

Publications

  1. Enhancing Rare Event Detection via Counterfactual Generation with Exogenous Variables.
  2. ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning.
  3. Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
  4. Exogenous Isomorphism for Counterfactual Identifiability.
  5. Multi-Task Invariant Representation Imitation Learning for Autonomous Driving.

Research Impact

The research output of Dehui Du demonstrates influence across multiple areas of artificial intelligence. His publications appear in recognized venues such as WWW, AAAI, ACL, ICML, and ICRA, reflecting engagement with leading scholarly communities. The combination of theoretical and applied research contributes to improved interpretability, reliability, and effectiveness of machine learning systems in real-world environments.[4]

Award Suitability

Dehui Du’s academic accomplishments align with the objectives of the Innovative Research Award. His work addresses contemporary challenges in artificial intelligence through innovative methodologies and interdisciplinary perspectives. The quality of publication venues, measurable citation indicators, and contributions to explainable and trustworthy AI collectively support consideration for recognition within the Top Teachers Awards framework.[5]

Conclusion

The scholarly record of Dehui Du reflects sustained contributions to computer science research, particularly in machine learning, causal inference, and intelligent systems. Through publications, collaborations, and methodological innovations, he has contributed to the advancement of explainable and reliable artificial intelligence technologies. These achievements provide a strong foundation for recognition through the Innovative Research Award.

References

  1. Elsevier. (n.d.). Scopus author details: Dehui Du, Author ID 14044898400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=14044898400
  2. Du, D., Tian, L., Chen, Y., Li, Y., & Li, Y. (2025). ERCI: An Explainable Experience Replay Approach with Causal Inference for Deep Reinforcement Learning.
  3. Yuan, J., Du, D., Zhang, H., Di, Z., & Naseem, U. (2025). Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up.
  4. Chen, Y., & Du, D. (2025). Exogenous Isomorphism for Counterfactual Identifiability.
  5. Peng, J., Yu, X., Wang, J., Tian, L., & Du, D. (2025). Multi-Task Invariant Representation Imitation Learning for Autonomous Driving.
  6. Tian, L., Du, D., & Chen, Y. (2026). Enhancing Rare Event Detection via Counterfactual Generation with Exogenous Variables.

Jisheng Dang | Computer Science | Research Excellence Award

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Research Excellence Award

Jisheng Dang
Professor, Lanzhou University, China
Jisheng Dang
Affiliation Lanzhou University
Country China
Scopus ID 57216844335
Documents 36
Citations 207
h-index 10
Subject Area Computer Science
Event Top Teachers Awards
IEEE Xplore 37088932779

Jisheng Dang is a Chinese computer scientist and academic researcher affiliated with the School of Information Science and Engineering at Lanzhou University. His research primarily focuses on multimodal learning, video understanding, computer vision, video object segmentation, and embodied intelligence. He has contributed to several peer-reviewed publications in internationally recognized journals and conferences, including IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IJCAI, AAAI, and ICME.[1] His scholarly activities additionally include professional reviewing services for major international conferences and journals such as IEEE TPAMI, CVPR, ICML, NeurIPS, ACM MM, and ICLR.[2]

Abstract

Jisheng Dang is a faculty member at Lanzhou University specializing in computer vision, multimodal large language models, and video understanding. His research focuses on video object segmentation, adaptive memory networks, and intelligent visual reasoning systems. He has published scholarly work in internationally recognized journals and conferences, contributing to advancements in multimodal artificial intelligence and efficient video analysis technologies.[3] The article further evaluates his academic contributions and professional suitability for recognition within the framework of the Top Teachers Awards initiative.

Keywords

Computer Vision, Multimodal Learning, Video Understanding, Video Object Segmentation, Artificial Intelligence, Long-Video Understanding, Adaptive Memory Networks, Large Language Models, Intelligent Transportation Systems, Video Data Processing, IEEE Transactions on Image Processing, Neural Networks.

Introduction

The rapid advancement of artificial intelligence and multimodal machine learning has increased the demand for efficient video understanding systems. In this field, Jisheng Dang has contributed to research on long-video processing, adaptive memory modeling, and unified video segmentation frameworks, supporting developments in computer vision and intelligent multimedia analysis.[4]

Jisheng Dang completed his doctoral research at Sun Yat-sen University under Professor Jianhuang Lai and Professor Huicheng Zheng, and later served as a Research Fellow at the NExT++ Laboratory, National University of Singapore, under Professor Tat-Seng Chua. These experiences strengthened his international collaborations in video analysis and multimodal intelligence research.[2]

Research Profile

Jisheng Dang is a tenured Associate Professor at the School of Information Science and Engineering, Lanzhou University, China. His research focuses on multimodal learning, video understanding, video object segmentation, embodied intelligence, and multimodal large language models, with additional contributions in adaptive memory networks and spatiotemporal information processing.[1]

Jisheng Dang actively serves as a reviewer for leading journals and conferences, including IEEE TPAMI, CVPR, ICML, NeurIPS, AAAI, and IJCAI. He has also collaborated with prominent institutions such as the National University of Singapore, Tsinghua University, and Peking University, along with industry partners including Tencent and Huawei.[2]

  • Research Areas: Computer Vision, Multimodal Large Language Models, Video Object Segmentation
  • Professional Reviewing Experience Across International AI Conferences and Journals

Research Contributions

Jisheng Dang has contributed to efficient and scalable frameworks for video segmentation and multimodal reasoning. His research on adaptive memory systems and spatio-temporal propagation methods improves computational efficiency in long-video processing and video analysis.[3]

His scholarly contributions include the proposal of innovative frameworks such as TW-GRPO, DeSa2VA, and MUPA, designed to improve segmentation accuracy and contextual reasoning in multimodal systems. These approaches attempt to bridge theoretical machine learning research with practical industrial applications involving intelligent transportation systems and advanced multimedia analysis.[5]

  • Research on unified video segmentation frameworks for accurate and efficient video object tracking.
  • Development of quality-guided dynamic memory approaches for long-video understanding systems.
  • Investigation of hallucination mitigation techniques in large video-language models.
  • Contributions to multimodal reasoning and adaptive contextual memory architectures.
  • Participation in interdisciplinary collaborations linking AI theory with industrial applications.

Publications

Selected publications associated with Jisheng Dang include journal articles and conference papers published in IEEE Transactions on Image Processing, IEEE ICME proceedings, and Neural Networks. Several publications focus on efficient video segmentation, dynamic memory networks, and multimodal understanding systems.[3]

  1. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35.
  2. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35.
  3. Wang, B., Wen, F., Dang, J., He, H., Wang, X., Zhu, N., & Weng, J. (2025). Mitigating Hallucination in Large Video-Language Models with Injected Semantics. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  4. Wang, B., Jiao, J., Dang, J., Jiang, Q., Lin, J., Chen, Z., Wang, T., & Yang, J. (2025). Quality-Guided Dynamic Memory for LLMs-based Long-Term Video Understanding. Proceedings of the 2025 IEEE International Conference on Multimedia and Expo (ICME).
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609.

Research Impact

The research impact associated with Jisheng Dang is reflected through peer-reviewed publications, scholarly citations, interdisciplinary collaborations, and professional service activities. His work has contributed to research discussions surrounding multimodal large language models, long-video understanding, and scalable segmentation systems.[1]

His publications in IEEE Transactions on Image Processing and conference proceedings have contributed to ongoing advancements in efficient visual processing architectures and multimodal reasoning systems. The application relevance of his research additionally extends to intelligent transportation systems, automated visual understanding, and multimedia analytics.[4]

  • Peer-reviewed publications in internationally indexed journals and conferences.
  • International collaborations with academic and industrial institutions.
  • Reviewer contributions to high-impact AI and computer vision venues.
  • Research contributions in video understanding and multimodal AI systems.
  • Academic recognition through thesis awards and institutional honors.

Award Suitability

Jisheng Dang has made sustained contributions to computer vision and multimodal machine learning through scholarly publications, collaborative research, and academic service. His publication record and participation in leading international conferences and journals reflect active engagement in the global artificial intelligence research community.[2]

His research in video segmentation, adaptive memory networks, and multimodal understanding systems reflects strong contributions to research excellence and academic innovation. His scholarly achievements and professional collaborations support his recognition within the Top Teachers Awards program.[5]

Conclusion

Jisheng Dang has established a research profile centered on multimodal learning, computer vision, and video understanding technologies. Through scholarly publication, international collaboration, and professional academic service, he has contributed to advancements in video segmentation frameworks and adaptive memory systems for artificial intelligence applications. His academic activities, publication record, and interdisciplinary research collaborations collectively reflect a sustained engagement with contemporary developments in artificial intelligence and multimedia computing research.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Jisheng Dang, Author ID 57216844335. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57216844335
  2. IEEE. (n.d.). IEEE Xplore Author Profile: Jisheng Dang. IEEE Xplore Digital Library. https://ieeexplore.ieee.org/author/37088932779
  3. Dang, J., Zheng, H., Guo, Y., Lai, J., Hu, B., & Chua, T.-S. (2026). Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649360
  4. Dang, J., Zheng, H., Chen, Z., Li, Z., Guo, Y., & Chua, T.-S. (2026). Fast Track Anything With Sparse Spatio-Temporal Propagation for Unified Video Segmentation. IEEE Transactions on Image Processing, Volume 35. https://doi.org/10.1109/TIP.2025.3649365
  5. Zhang, L., Dang, J., Zhang, S., Gan, W., Wang, J., Hu, B., Feng, G., & Peng, H. (2026). Graph-enhanced dual low-rank correlation embedding for spatio-temporal EEG fusion in depression recognition. Neural Networks, Volume 198, Article 108609. https://doi.org/10.1016/j.neunet.2026.108609

Sema Servi | Computer Science | Best Research Article Award

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Assist. Prof. Dr. Sema Servi | Computer Science | Best Research Article Award

Selçuk University | Turkey

Asst. Prof. Dr. Sema Servi is a researcher in computer engineering with a strong foundation in applied mathematics, specializing in machine learning, artificial intelligence, and numerical methods for complex problem solving. Her work focuses on data-driven approaches, including clustering algorithms, optimization techniques, and computer vision applications in healthcare and engineering. She has contributed to interdisciplinary research spanning digital competence analysis, bioinformatics, and intelligent systems. Asst. Prof. Dr. Sema Servi actively supervises postgraduate research and advises innovative, technology-driven projects supported by national programs. She has a solid research impact with 62 Scopus citations, 15 indexed documents, and an h-index of 5.

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Xulei Cao | Computer Science | Research Excellence Award

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Mr. Xulei Cao | Computer Science | Research Excellence Award

University of Science and Technology of China | China

Mr. Xulei Cao research centers on advancing intelligent communication systems, large-scale machine learning, and adaptive networked environments, with a primary emphasis on vehicular ad hoc networks (VANETs), device–edge–cloud collaboration, and large language models. His work explores street-centric and microtopology-based routing strategies to address the challenges of dynamic mobility, frequent topology changes, and complex urban communication environments, proposing opportunistic routing protocols that leverage link correlation to enhance reliability, reduce packet loss, and optimize end-to-end performance. He has contributed to routing solutions grounded in urban road structure awareness, improving scalability and robustness in dense vehicular networks and supporting next-generation intelligent transportation systems. In parallel, his research extends into intelligent computing frameworks that integrate device, edge, and cloud layers to enable efficient distributed learning, resource-aware decision-making, and latency-sensitive AI applications. He also investigates algorithmic innovation within large language models, emphasizing scalability, deployment efficiency, and real-world applicability. Additionally, his work on biometric recognition, including palmprint feature extraction and direction coding, demonstrates expertise in pattern recognition and vision-based authentication systems. Supported by growing scholarly recognition, his work has been cited 212 times overall, including 101 citations since 2020, with an h-index of 3 and an i10-index of 2, underscoring the increasing impact and relevance of his contributions to networking, artificial intelligence, and intelligent mobility research.

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Abdalilah Alhalangy | Computer Science | Innovative Research Award

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Assoc. Prof. Dr. Abdalilah Alhalangy | Computer Science | Innovative Research Award

Qassim university | Saudi Arabia

Assoc. Prof. Dr. Abdalilah Alhalangy, Ph.D., is an Associate Professor in Computer Engineering at Qassim University, Kingdom of Saudi Arabia, specializing in advanced areas of artificial intelligence, machine learning, intelligent systems, and cybersecurity. His research spans deep learning, ensemble methods, neural networks, computer vision, wireless networks, cloud computing, big data analytics, robotics, augmented reality, mobile applications, image and video analysis, GIS, and e-learning systems. He has a particular focus on artificial neural networks, wavelet neural networks, fuzzy logic, evolutionary algorithms, and computational intelligence, applied to enhancing the security and functional performance of intelligent systems. Dr. Al-Halangy has published 6 documents cited by 59 Scopus-indexed papers, achieving a Scopus h-index of 3 and an i10-index of 2 on Google Scholar, with a total of 131 citations. His work has earned recognition in fields ranging from Arabic speech emotion recognition and fake account detection in mobile networks to generative AI-driven cybersecurity systems and the evaluation of e-learning effectiveness. Dr. Al-Halangy’s research is characterized by its innovative integration of AI techniques to solve complex real-world problems, positioning him as a leading contributor to modern computing challenges. He has received accolades including the Innovative Research Award for his contributions to the development of secure, intelligent, and efficient computational systems. His work continues to impact both academic research and practical applications, advancing the state of intelligent and adaptive technologies globally.

Publication Profile

Scopus | Orcid | Google Scholar

Featured Publications

  • Alhalangy, A., & AbdAlgane, M. (2023). Exploring the impact of AI on the EFL context: A case study of Saudi universities.

  • Alhalangy, A. (2024). Deep learning, ensemble and supervised machine learning for Arabic speech emotion recognition. Engineering, Technology & Applied Science Research, 14, 1-10.

  • Hassan, A., & Alhalangy, G. I. A. (2023). Fake accounts identification in mobile communication networks based on machine learning. SSRN.

  • Alhalangy, A., Elhadi, O. A. M., & Mohamed, E. H. G. (2025). E-learning effectiveness and efficiency in Kassala and Gedaref universities: An IS-impact evaluation. UtilitasMathematica, 122(2), 1301-1317.

  • Alhalangy, A. (2025). Generative AI-driven information system for behavioral detection of zero-day cyber attacks. UtilitasMathematica, 122(2), 1194-1210.

Weidong Ji | Computer Science | Editorial Board Member

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Dr. Weidong Ji | Computer Science | Editorial Board Member

Harbin Normal University | China

Dr. Weidong Ji is a distinguished Chinese scholar known for his innovative contributions to artificial intelligence in education, learning analytics, and intelligent recommendation technologies, with a strong research presence reflected across international journals and conferences. His work integrates machine learning, knowledge graphs, deep learning models, and cognitive theories to enhance personalized learning, online education systems, student engagement assessment, and data-driven educational decision-making. With a Scopus record of 39 documents, 102 citations from 100 citing documents, and an h-index of 6, Dr. Ji’s scholarly influence continues to grow as his recent works advance emerging domains such as quantum-constructivism-based knowledge tracing, lightweight human–computer interaction models, and user-preference-driven recommendation algorithms. Google Scholar metrics (if available) would further extend his citation visibility, demonstrating the expanding global use of his models in adaptive learning, course recommendations, and real-time student behavior analysis. He also contributes to the academic community through participation in peer-review processes and editorial activities that support the development of high-quality research in AI-driven educational technology. Dr. Ji’s recent publications showcase cutting-edge computational frameworks such as neural knowledge graph reasoning, lightweight vision models for engagement analytics, and personalized prediction architectures for sparse learning environments. His contributions position him as an emerging leader at the intersection of educational psychology and computational intelligence, emphasizing practical applicability, algorithmic efficiency, and innovative pedagogical design, demonstrating excellence aligned with this award category as an Editorial Board Member.

Publication Profile

Scopus

Featured Publications

  • Authors unavailable. (2025). Knowledge graph convolutional networks with user preferences for course recommendation. Scientific Reports.

  • Authors unavailable. (2025). CQSA-KT: Research on personalized knowledge tracing based on quantum-constructivism in sparse learning environments. Knowledge Based Systems.

  • Authors unavailable. (2025). LightNet: A lightweight head pose estimation model for online education and its application to engagement assessment. Journal of King Saud University Computer and Information Sciences.