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

Ramachandra M Bhaskara | Data Science and Analytics | Innovative Research Award

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Innovative Research Award

Ramachandra M Bhaskara
Frankfurt Institute for Advanced Studies (FIAS), Germany

Ramachandra M Bhaskara
Affiliation Frankfurt Institute for Advanced Studies (FIAS)
Country Germany
Scopus ID 26030267200
Documents 32
Citations 1,239+
h-index 16
Subject Area Data Science and Analytics
Event Top Teachers Awards
ORCID 0000-0002-7742-0391
Google Scholar _1awcysAAAAJ

Ramachandra M Bhaskara is a computational biophysicist, structural bioinformatician, and data science researcher affiliated with the Frankfurt Institute for Advanced Studies (FIAS), Germany. His interdisciplinary research integrates biology, physics, computational modeling, molecular simulations, machine learning, and data analytics to investigate complex cellular systems and membrane remodeling mechanisms. His scholarly contributions span structural biology, digital cell biology, computational biophysics, and bioinformatics, with research outputs recognized through international collaborations, high-impact publications, scientific leadership, and sustained academic mentorship.[1][2]

Since June 2026, Ramachandra M Bhaskara has served as a Fellow in Digital Cell Biology at FIAS following leadership roles at Goethe University Frankfurt and research appointments at the Max-Planck Institute of Biophysics. His work focuses on computational approaches for understanding cellular architecture across multiple biological scales and developing innovative analytical frameworks for complex biological datasets.[1]

Abstract

This article evaluates the academic profile and research accomplishments of Ramachandra M Bhaskara in the context of the Innovative Research Award associated with the Top Teachers Awards. His work combines computational biology, membrane biophysics, structural bioinformatics, and data science to address fundamental questions in cellular organization and molecular mechanisms. Through the development of computational methodologies, simulation frameworks, and interdisciplinary collaborations, Bhaskara has contributed to advances in membrane remodeling, selective autophagy, protein structure analysis, and digital cell biology.[1][3]

Keywords

Structural Biology, Data Science, Cell Biology, Membrane Biology, Molecular Biophysics, Bioinformatics, Molecular Dynamics, Coarse-graining, Metric Learning and Classification, Theoretical Biophysics, Membrane Remodeling, Integrative Modeling, Digital Cell Biology, Machine Learning, Data Integration.

Introduction

Ramachandra M Bhaskara received his bachelor’s education in Chemistry, Biochemistry, and Biotechnology from Osmania University before joining the Integrated Ph.D. program at the Indian Institute of Science (IISc), Bangalore. During his graduate studies he acquired research experience ranging from field ecology to molecular biophysics, ultimately focusing on computational approaches to understanding protein structure, stability, and evolution. His doctoral work under Professor N. Srinivasan led to the development of analytical frameworks for studying multidomain proteins and earned the prestigious B. H. Iyer Gold Medal for Best Thesis.[1]

Following his doctoral studies, he joined the Max-Planck Institute of Biophysics under Professor Gerhard Hummer, where he investigated membrane remodeling phenomena including curvature generation, membrane fusion, budding, and poration through advanced simulation methodologies. These experiences laid the foundation for his subsequent leadership in computational cell biology and digital biology initiatives in Germany.[1]

Research Profile

Ramachandra M Bhaskara’s academic career reflects sustained engagement in interdisciplinary research combining biological sciences, computational modeling, theoretical biophysics, and data-driven discovery. His appointments include Team Leader for Computational Cell Biology at Goethe University Frankfurt (2020–2026) and Fellow in Digital Cell Biology at FIAS from 2026 onward.[1]

  • Fellow (Digital Cell Biology), Frankfurt Institute for Advanced Studies (2026–present).
  • Team Leader (Computational Cell Biology), Goethe University Frankfurt (2020–2026).
  • Postdoctoral Fellow, Max-Planck Institute of Biophysics (2014–2020).
  • Research Associate, National Centre for Biological Sciences (2013–2014).
  • Ph.D. and M.S., Indian Institute of Science.

His professional activities extend beyond research and include teaching, scientific mentoring, grant evaluation, journal peer review, editorial responsibilities, workshop organization, and participation in major international research consortia focused on cellular architecture, autophagy, computational biomedicine, and digital biology.[1]

Research Contributions

Ramachandra M Bhaskara has contributed significantly to understanding membrane remodeling mechanisms, selective endoplasmic reticulum autophagy (ER-phagy), protein dynamics, and cellular organization. His research has combined molecular simulations, structural analysis, and computational method development to investigate biological processes that are difficult to observe experimentally.[3][4]

  • Development of computational tools for studying multidomain protein evolution and stability.
  • Advancement of simulation methodologies for membrane curvature induction and remodeling.
  • Research on ER-phagy pathways and ubiquitin-mediated organelle quality control.
  • Integration of data science and machine learning approaches in cellular biology.
  • Leadership in digital biology and FAIR data integration initiatives.
  • Training and supervision of students, doctoral researchers, and postdoctoral fellows.

Publications

Ramachandra M Bhaskara’s most influential publications have advanced understanding of ER-phagy, membrane remodeling, protein flexibility, and cellular quality-control mechanisms through computational and biophysical approaches. Notable contributions include studies published in Nature, Nature Communications, EMBO Reports, and PNAS, demonstrating significant interdisciplinary research impact.[3][4][5][7]

Selected peer-reviewed publications demonstrate Bhaskara’s contributions to membrane biology, computational biophysics, structural biology, and cellular systems research.[3][7]

Research Impact

The research impact of Ramachandra M Bhaskara’s work is reflected through an established publication record, international collaborations, sustained citation activity, invited presentations, and participation in multidisciplinary scientific programs. His work has informed current understanding of membrane dynamics, autophagy, organelle quality control, and computational modeling of biological systems.[1][3]

In addition to research outputs, his contributions include curriculum development, graduate teaching, mentorship of researchers across academic levels, service as a reviewer and editor, and leadership within collaborative research networks. These activities demonstrate a broad academic influence extending beyond publication metrics alone.[1]

Award Suitability

Based on documented scholarly achievements, research leadership, innovation in computational methodology, interdisciplinary collaboration, and educational contributions, Ramachandra M Bhaskara demonstrates characteristics commonly associated with candidates for research excellence recognition. His record includes impactful publications, development of novel computational approaches, leadership of scientific teams, successful mentorship activities, and contributions to emerging fields such as digital cell biology and data-driven biomedical research.[1][3]

Conclusion

Ramachandra M Bhaskara has established a multidisciplinary academic profile spanning computational biophysics, structural biology, membrane biology, and data science. Through innovative methodological development, internationally recognized research, scientific leadership, and educational engagement, he has contributed to advancing knowledge of complex biological systems. His academic trajectory reflects sustained commitment to research excellence and interdisciplinary innovation, supporting consideration for recognition within research and teaching award frameworks.[1][3]

References

  1. Frankfurt Institute for Advanced Studies. (n.d.). Research fellow profile: Ramachandra M Bhaskara. https://fias.institute/en/research/fellows/detail/bhaskara-ramachandra/
  2. Elsevier. (n.d.). Scopus author details: Ramachandra M Bhaskara, Author ID 26030267200. Scopus. https://www.scopus.com/authid/detail.uri?authorId=26030267200
  3. Bhaskara RM et al. (2019). Curvature induction and membrane remodeling by FAM134B reticulon homology domain assist selective ER-phagy. Nature Communications. https://www.nature.com/articles/s41467-019-10345-3
  4. Bhaskara RM et al. (2023). Ubiquitination regulates ER-phagy and remodelling of endoplasmic reticulum. Nature. https://www.nature.com/articles/s41586-023-06089-2
  5. Bhaskara RM et al. Role of FAM134 paralogues in endoplasmic reticulum remodeling, ER-phagy, and Collagen quality control. EMBO Reports. https://link.springer.com/article/10.15252/embr.202052289
  6. Bhaskara RM et al. (2015). Protein flexibility in the light of structural alphabets. Frontiers in Molecular Biosciences. DOI: https://doi.org/10.3389/fmolb.2015.00020
  7. Bhaskara RM et al. (2019). Membrane perforation by the pore-forming toxin pneumolysin. Proceedings of the National Academy of Sciences. DOI: https://doi.org/10.1073/pnas.1904304116

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

Sana Mujeeb | Predictive Analysis | Best Researcher Award

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