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

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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.

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.

Citation Metrics (Scopus)

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Citations
62

h-index
5

Documents
15

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Julien Riposo | Blockchain | Research Excellence Award

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Dr. Julien Riposo | Blockchain | Research Excellence Award

J.R. Enterprise | France

Dr. Julien Riposo is a distinguished quantitative researcher, mathematician, and thought leader whose work spans mathematics of blockchain, quantitative finance, risk analytics, and advanced data-driven modelling. His research is recognized globally through publications in major academic outlets, including Nature Methods, Springer Nature journals, MDPI platforms, and interdisciplinary mathematical venues, reflecting a career that bridges theoretical depth with applied innovation. He has developed influential mathematical frameworks across cryptography, financial modelling, high-frequency trading, probability theory, and blockchain verification, notably contributing to emerging fields such as proof-of-solvency, categorical approaches to NLP, staking-yield modelling, convergence of portfolio tilting, and structural modelling of risk factor dynamics. His scholarly influence extends across domains such as 3D genome reconstruction, nucleosome configuration, diffusion models for peer-to-peer networks, and constrained portfolio optimization, highlighting a rare ability to merge abstract mathematics with real-world systems. A recipient of high-profile distinctions including the Wilmott Award and the Louise Arconati Visconti Prize, he integrates research excellence with practical impact, having contributed to algorithmic trading design, blockchain governance analysis, digital asset modelling, and advanced risk methodologies adopted within global financial ecosystems. His works continue to expand the mathematical underpinnings of decentralized finance, governance analytics, and quantitative modelling. His research visibility is further evidenced by citation metrics across major indexing databases. On Google Scholar, his corpus exceeds more than 430 citations with an h-index of 6 and i10-index of 4, demonstrating growing scholarly traction across mathematics, finance, and computational sciences; meanwhile, Scopus-indexed outputs reflect strong cross-disciplinary engagement and international referencing within quantitative finance and mathematical modelling communities. Through his ongoing contributions, Dr. Riposo plays a pivotal role in shaping the mathematical foundations of next-generation financial technologies, making him an outstanding candidate for a Research Excellence Award.

Publication Profile

Orcid | Google scholar

Featured Publications

Lesne, A., Riposo, J., Roger, P., Cournac, A., & Mozziconacci, J. (2014). 3D genome reconstruction from chromosomal contacts. Nature Methods, 11(11), 1141–1143.

Bianca, C., & Riposo, J. (2015). Mimic therapeutic actions against keloid by thermostatted kinetic theory methods. The European Physical Journal Plus, 130(8), 159.

Riposo, J., & Mozziconacci, J. (2012). Nucleosome positioning and nucleosome stacking: Two faces of the same coin. Molecular BioSystems, 8(4), 1172–1178.

Bianca, C., Guerrini, L., & Riposo, J. (2015). A delayed mathematical model for the acute inflammatory response to infection. Applied Mathematics & Information Sciences, 9(6), 2775–2783.

Riposo, J. (2022). Diffusion on the peer-to-peer network. Journal of Risk and Financial Management, 15(2),

Abdelmoaty Mahmoud | Computer Science | Best Research Article Award

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