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

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

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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