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

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