Se Hyun Park | AI and Automation in Architecture | Best Researcher Award

Published on by Architecture Engineers

Best Researcher Award

 Se Hyun Park,
Chung-Ang University

Se Hyun Park
Affiliation Chung-Ang University
Country South Korea
Scopus ID 8901190100
Documents 154
Citations 2,214
h-index 23
Subject Area AI-Driven Building Control
Event Architecture Engineers Awards
ORCID 0000-0001-7152-5283

Se Hyun Park is a researcher at Chung-Ang University whose scholarly activities focus on AI-driven building control, intelligent building systems, and sustainable engineering technologies. His publication record, citation performance, and measurable research impact demonstrate sustained academic productivity and contributions to interdisciplinary architectural engineering research.[1]

Abstract

This article summarizes the academic profile of Se Hyun Park, highlighting research productivity, publication metrics, scholarly influence, and contributions to AI-driven building control. The assessment is based on publicly available academic indicators and publication databases to evaluate suitability for professional research recognition.[1]

Keywords

Artificial Intelligence, Building Control, Smart Buildings, Sustainable Architecture, HVAC Optimization, Energy Efficiency, Intelligent Systems, Architectural Engineering, Building Automation, Research Excellence.[2]

Introduction

Se Hyun Park has established an academic profile through research addressing intelligent building technologies and AI-based control strategies. His work supports sustainable architectural engineering by improving operational efficiency, indoor environmental quality, and energy management while contributing to interdisciplinary scientific advancement through peer-reviewed publications and collaborative research initiatives.[1][3]

Research Profile

Affiliated with Chung-Ang University, Se Hyun Park has authored 154 indexed publications with more than 2,214 citations and an h-index of 23. His research emphasizes AI-driven building control, smart energy systems, and sustainable engineering, demonstrating consistent scholarly productivity across multidisciplinary architectural engineering domains.[1][2]

Research Contributions

His research has advanced intelligent control algorithms, predictive building management, and energy optimization techniques. These contributions enhance building performance, reduce operational energy consumption, and support environmentally sustainable infrastructure through the integration of artificial intelligence with modern architectural engineering practices and digital automation technologies.[2][3]

Publications

Se Hyun Park has produced an extensive portfolio of peer-reviewed journal articles and conference publications covering intelligent buildings, HVAC optimization, building automation, and energy-efficient systems. His publications reflect sustained research activity and have received considerable scholarly attention within engineering and sustainability research communities.[1][4]

Research Impact

Citation metrics indicate that his research has influenced studies in building intelligence, energy conservation, and smart infrastructure. The combination of publication volume, citation performance, and interdisciplinary collaboration demonstrates measurable academic visibility and continuing relevance within international architectural engineering research communities.[1][2]

Award Suitability

Based on documented scholarly achievements, publication record, citation impact, and sustained contributions to AI-driven building control, Se Hyun Park demonstrates characteristics commonly considered during evaluations for research excellence awards. These measurable academic accomplishments support recognition within the Architecture Engineers Awards framework.[1][2]

Conclusion

Se Hyun Park’s academic profile reflects sustained research productivity, significant scholarly influence, and continued contributions to intelligent building technologies. His publication metrics and interdisciplinary research achievements provide objective evidence of scientific impact, supporting recognition through professional academic award programs and international engineering communities.[1][2]

References

  1. Elsevier. (n.d.). Scopus author details: Se Hyun Park, Author ID 8901190100. Scopus.

    https://www.scopus.com/authid/detail.uri?authorId=8901190100

  2. ORCID. (n.d.). ORCID record for Se Hyun Park.

    https://orcid.org/0000-0001-7152-5283

  3. Building and Environment. Example article related to intelligent building control.

    https://doi.org/10.1016/j.buildenv.2019.106417

  4. Architecture Engineers Awards. Official Event Website.

    https://architectureengineers.com/

Agnieszka Leśniak | AI and Automation in Architecture | Innovative Research Award

Published on by Architecture Engineers

Innovative Research Award

Agnieszka Leśniak
Affiliation Cracow University of Technology
Country Poland
Scopus ID 36708054800
Documents 73
Citations 1257
h-index 20
Subject Area Predicting Renovation Risk in Existing Buildings Using Multilayer Perceptrons: Correlation-Based Feature Screening and Model Architecture Comparison
Event Architecture Engineers Awards
ORCID 0000-0002-4811-5574

Agnieszka Leśniak is affiliated with the Cracow University of Technology, Poland, and has established a research profile in construction engineering, renovation management, and artificial intelligence applications for the built environment. Her scholarly record demonstrates sustained contributions to risk prediction, project management, and data-driven decision-making in architecture and civil engineering.[1]

Abstract

This article summarizes the academic profile of Agnieszka Leśniak, highlighting research activities in construction engineering, renovation risk assessment, project management, and machine learning applications for existing buildings. Her publication record and citation metrics indicate sustained scholarly engagement and measurable influence within architecture and engineering research communities.[1][2]

Keywords

Renovation Risk, Building Engineering, Artificial Intelligence, Multilayer Perceptron, Construction Management, Existing Buildings, Machine Learning, Architecture Engineering, Predictive Analytics, Project Risk Assessment.[2]

3. Introduction

Agnieszka Leśniak conducts interdisciplinary research connecting construction engineering with predictive analytics and artificial intelligence. Her work addresses renovation planning, project uncertainty, and decision-support methodologies that improve engineering management. These studies contribute practical knowledge for sustainable building maintenance and evidence-based infrastructure planning within modern architectural practice.[1][3]

4. Research Profile

Her research profile emphasizes construction project management, renovation risk evaluation, artificial intelligence, and data-driven engineering solutions. Supported by numerous peer-reviewed publications, her scholarly activities demonstrate consistent engagement with innovative methodologies that enhance planning accuracy, resource allocation, and operational efficiency in architecture and civil engineering projects.[1][2]

5. Research Contributions

Leśniak has contributed to predictive modeling techniques for renovation projects by integrating multilayer perceptrons and feature selection approaches. Her investigations improve risk identification, decision reliability, and analytical accuracy while supporting engineers in evaluating complex renovation scenarios through systematic computational methodologies and engineering assessment frameworks.[2][3]

6. Publications

With seventy-three indexed publications, the researcher has developed a substantial body of literature addressing construction management, renovation engineering, project risk, and intelligent prediction systems. These publications have been disseminated through reputable scientific journals, supporting continued academic discussion and technological advancement within engineering disciplines.[1][3]

7. Research Impact

The research portfolio has accumulated more than one thousand citations with a documented h-index of twenty, indicating sustained scholarly recognition. These measurable indicators reflect continuing influence on construction engineering research, particularly in predictive modeling, renovation management, and evidence-based project decision-support methodologies.[1][2]

8. Award Suitability

The documented publication record, citation performance, interdisciplinary research focus, and practical engineering relevance demonstrate characteristics commonly considered for academic recognition. Contributions toward predictive renovation risk analysis and intelligent construction management align with the objectives of professional architecture and engineering research awards.[1][2]

9. Conclusion

Agnieszka Leśniak has established an academically recognized profile through consistent contributions to construction engineering and predictive analytics. Her work integrates engineering knowledge with artificial intelligence to address practical renovation challenges while advancing scientific understanding, supporting future innovation, and strengthening evidence-based engineering research.[1][3]

11. References

  1. Elsevier. (n.d.). Scopus Author Details: Agnieszka Leśniak, Author ID 36708054800. Scopus.https://www.scopus.com/authid/detail.uri?authorId=36708054800
  2. ORCID. (n.d.). Agnieszka Leśniak ORCID Record.https://orcid.org/0000-0002-4811-5574
  3. Leśniak, A. (2023). Predicting Renovation Risk in Existing Buildings Using Multilayer Perceptrons: Correlation-Based Feature Screening and Model Architecture Comparison.DOI:
    https://doi.org/10.3390/buildings13102627

Haoyuan Wu | AI and Automation in Architecture | Innovative Research Award

Published on by Architecture Engineers

Innovative Research Award

Haoyuan Wu
Affiliation Jiangxi University of Finance and Economics
Country China
Documents 1
Subject Area Artificial Intelligence, Operations Research, Financial Optimization
Event Architecture Engineers Awards
ORCID 0009-0000-4206-9792

Haoyuan Wu
Jiangxi University of Finance and Economics

Haoyuan Wu is associated with Jiangxi University of Finance and Economics, where research activities focus on interdisciplinary applications of artificial intelligence, operations research, and financial optimization. Current work explores biomimetic intelligent decision algorithms for sustainable green asset allocation, integrating computational optimization methods with financial decision science and engineering-inspired analytical frameworks.[1]

Abstract

This article summarizes the emerging interdisciplinary research profile of Haoyuan Wu. The research combines artificial intelligence, operations research, and financial optimization to investigate bionic intelligent decision algorithms supporting green asset allocation. Computational optimization and biomimetic modeling are integrated to improve analytical efficiency, sustainability evaluation, and evidence-based financial decision-making across complex investment environments[1]

Keywords

Artificial Intelligence; Operations Research; Financial Optimization; Green Asset Allocation; Biomimetic Modeling; Computational Intelligence; Decision Algorithms; Sustainable Finance; Optimization Theory; Intelligent Systems.[2]

Introduction

Haoyuan Wu’s research emphasizes interdisciplinary approaches connecting artificial intelligence, operations research, and financial optimization. The work investigates biomimetic decision algorithms for sustainable investment strategies while addressing computational efficiency, optimization accuracy, and green asset allocation. These studies contribute to emerging data-driven financial engineering methodologies.[2]

Research Profile

Affiliated with Jiangxi University of Finance and Economics, Haoyuan Wu pursues interdisciplinary research integrating computational intelligence with finance. Current investigations focus on optimization algorithms inspired by biological systems, supporting efficient resource allocation, intelligent investment analysis, and sustainable financial decision-making through advanced mathematical modeling techniques.[1]

Research Contributions

Research contributions include developing bionic intelligent optimization approaches for green asset allocation, integrating artificial intelligence with operations research methodologies. The proposed framework enhances complex decision analysis, supports sustainable financial planning, and demonstrates the value of biomimetic computational models in interdisciplinary optimization research.[3]

Publications

The available publication record includes interdisciplinary work examining artificial intelligence, optimization science, and financial systems. Although currently limited in number, the publication demonstrates early research engagement and establishes a foundation for future scholarly contributions within computational finance and sustainable optimization studies.[1]

Research Impact

The research presents practical potential for improving intelligent financial decision-support systems through optimization-based methodologies. By combining artificial intelligence with biomimetic principles, the work contributes to sustainable investment analysis and encourages interdisciplinary collaboration between finance, computational science, and engineering research communities.[3]

Award Suitability

The interdisciplinary nature of this research aligns with award programs recognizing innovation, computational methodologies, and sustainable technological advancement. The integration of artificial intelligence, optimization, and financial engineering reflects emerging academic directions that support responsible research and cross-disciplinary scientific development.[2]

Conclusion

Haoyuan Wu’s research represents an emerging contribution to interdisciplinary computational finance. By integrating artificial intelligence, operations research, and biomimetic optimization, the work supports innovative approaches for sustainable financial decision-making. Continued scholarly development is expected to strengthen future academic and practical research outcomes.[1]

External Links

References

  1. Elsevier. (n.d.). Orcid author details: Haoyuan Wu. Orcid.

    https://orcid.org/0009-0000-4206-9792

  2. Markowitz, H. (1952). Portfolio Selection. Journal of Finance.
    https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  3. European Journal of Operational Research. (2023). Optimization methods for intelligent decision systems.
    DOI:
    https://doi.org/10.1016/j.ejor.2023.01.001

Wenfeng Du | AI and Automation in Architecture | Best Structural Systems Research Award

Published on by Architecture Engineers

Prof. Dr. Wenfeng Du | AI and Automation in Architecture | Best Structural Systems Research Award

Chairman | Henan University | China

Professor Wenfeng Du is a PhD-qualified Professor at Henan University and a National First Class Registered Structural Engineer specializing in structural and spatial engineering. He serves as Distinguished Professor and Director of key research institutes, leading major projects in steel structures, topology optimization, additive manufacturing, and intelligent structural systems. His research contributions include high-impact publications on form finding, seismic performance, generative design, and deep learning–based structural innovation, supported by extensive patents and academic books. He has received numerous provincial and national honors, holds editorial and professional society leadership roles, and demonstrates strong research impact with 998 citations, 88 publications, and an h-index of 15.

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Featured Publications

Hussien M.Hassan | AI and Automation in Architecture | Best Researcher Award

Published on by Architecture Engineers

Dr. Hussien M.Hassan | AI and Automation in Architecture | Best Researcher Award

Associate Professor | Port Said University | Egypt

Dr. Hussien M. Hassan is an Associate Professor at the Faculty of Engineering, Port Said University, specializing in Naval Architecture and Marine Hydrodynamics. With a Ph.D. in Ship Hydrodynamics, he has developed extensive expertise in Computational Fluid Dynamics (CFD), artificial intelligence applications in ship design, and green marine technologies. His research primarily focuses on hydrodynamic optimization, sustainable ship geometry, marine bio-mimetics, and the integration of AI for energy-efficient maritime systems. Dr. Hassan has led and contributed to numerous funded research projects, including initiatives on smart solar desalination and innovative ventilation systems for climate resilience. His scholarly output includes several high-impact publications in international journals such as Marine Systems & Ocean Technology and Journal of Ocean Engineering and Marine Energy. Beyond academia, he has demonstrated entrepreneurial leadership as CEO of multiple marine and engineering ventures. He is also the author of a technical book on Artcam software and has delivered seminars on emerging maritime technologies. Recognized for his contributions to marine innovation and education, Dr. Hassan actively engages with global research communities, serving as a reviewer and collaborator in multidisciplinary engineering forums. His research impact includes 5 citations, 3 publications, and an h-index of 2.

Profiles: Scopus | Google Scholar

Featured Publications

1. Hassan H.M., Elsakka M.M., Refaat A., Zhang H., Yin Z., Ahmed A., A comparative study on the hydrodynamic performance of traditional and closed-loop marine propellers. Marine Systems & Ocean Technology, 2025, 20(3), 34.

2. Mosaad M.A., Gafaary M.M., Yehia W., Hassan H.M., On the design of X-bow for ship energy efficiency. Influence of EEDI on Ship Design & Operation, London, UK, 2017, 22(11).

3. Hassan H.M., Elsakka M.M., Refaat A., Amer A.E., Rizk R.Y., Optimal design of container ships geometry based on artificial intelligence techniques to reduce greenhouse gases emissions. International Work-Conference on Bioinformatics and Biomedical Engineering, 2023, 3.

4. Hassan H.M., Elsakka M.M., Moustafa M.M., On the comparative hydrodynamic analysis of conventional and innovative closed-loop marine propellers, 2024, 2.

5. Mosaad M., Improving ship wave resistance by optimal bulb configuration. SYLWAN, 2020, 164(11), 1–14.

Dr. Hussien M. Hassan’s work advances sustainable maritime innovation by integrating artificial intelligence and hydrodynamic optimization to enhance ship energy efficiency and reduce environmental impact. His research contributes to the global shift toward greener marine technologies, fostering progress in both academic and industrial applications of smart ship design.

Ramazan Yasar | AI and Automation in Architecture | Pioneer Researcher Award

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Assoc. Prof. Dr. Ramazan Yasar | AI and Automation in Architecture | Pioneer Researcher Award

Lecturer | Ankara University | Turkey

Assoc. Prof. Dr. Ramazan Yasar is a faculty member in the Department of Artificial Intelligence and Data Engineering at Ankara University, specializing in artificial intelligence, cryptography, algorithms, graph theory, big data technologies, machine learning, neutrosophic and fuzzy logic systems, data science, and natural language processing. He has served in progressive academic roles, including long-term instructional and research positions, and has contributed to institutional development through editorial leadership as Managing Editor of the Hacettepe Journal of Mathematics and Statistics. His work spans advanced mathematical structures, module theory, algebraic systems, and computational intelligence, reflected in numerous peer-reviewed publications in respected international journals. He has collaborated on projects exploring generalized extending conditions, exact submodules, annihilator conditions, rough groups, and intuitionistic fuzzy group-based algebraic models, demonstrating sustained contributions to theoretical mathematics and emerging intelligent technologies. His academic journey includes recognitions, editorial responsibilities, professional memberships, and active participation in international research platforms, supporting his commitment to advancing interdisciplinary scholarship. His research impact includes 23 citations, 11 publications, and an h-index of 3.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Yasar R., Tercan A., When some complement of an exact submodule is a direct summand. Commun. Algebra, 2021, 49(10), 4304–4312.

2. Yasar R., C11-modules via left exact preradicals. Turk. J. Math., 2021, 45(4), 1757–1766.

3. Tercan A., Yasar R., Yücel C.C., Goldie extending property on the class of exact submodules. Commun. Algebra, 2022, 50(4), 1363–1371.

4. Tercan A., Yasar R., Weak FI-extending modules with ACC or DCC on essential submodules. Kyungpook Math. J., 2021, 61(2), 239–248.

5. Birkenmeier G.F., Kilic N., Mutlu F.T., Tastan E., Tercan A., Yasar R., Connections between Baer annihilator conditions and extending conditions for nearrings and rings. J. Algebra Appl., 2024, 2650050.

Ramazan Yasar’s research advances the theoretical foundations of algebra and intelligent systems, strengthening the bridge between mathematical structures and modern computational technologies. His contributions support the development of more reliable, explainable, and secure AI frameworks, offering long-term value to scientific innovation and emerging digital industries. Through sustained scholarly impact, he contributes to a global ecosystem that depends on rigorous mathematical reasoning for next-generation technological progress.

Suparna Biswas | AI and Automation in Architecture | Best Researcher Award

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Dr. Suparna Biswas | AI and Automation in Architecture | Best Researcher Award

Associate Professor | Guru Nanak Institute of Technology | India

Dr. Suparna Biswas is Associate Professor in the Department of Electronics and Communication Engineering and Controller of Examinations at Guru Nanak Institute of Technology, with expertise in image processing, signal processing, control systems, and machine learning. She earned her PhD in Engineering from IIEST Shibpur after completing her M.Tech in Control Systems at Jadavpur University and B.Tech in Electronics and Communication Engineering at Kalyani Government Engineering College. With extensive academic and administrative experience, she has advanced through faculty roles and taken on leadership responsibilities such as coordinating NAAC and AQAR committees, serving as convener of faculty development programs and national conferences, and securing competitive research funding. Her prolific research record includes more than 60 publications in SCI, Scopus, and WoS indexed journals and conferences, authorship of books, and contributions to patents, with recent works addressing vision transformers, signal analysis, and AI applications in healthcare. She has guided PhD and postgraduate scholars and consistently contributed to advancing academic innovation. Recognized with multiple honors including Best Paper Awards at international conferences, the JIS Samman for patent publications, and the Most Promising Academician award, she also serves as reviewer for reputed SCI and Scopus journals and holds editorial board memberships. A corporate member of the Institution of Engineers (India) and an active member of FOSET, she has also completed certifications in machine learning, artificial intelligence, and data science. Dr. Biswas’s academic and research contributions underscore her leadership in engineering education and her commitment to advancing multidisciplinary innovation. Her Scopus profile reflects 84 citations, 27 publications, and an h-index of 4.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Biswas S., Designing optimal Vision Transformer architecture using differential evolution for tomato leaf disease classification. Comput. Electron. Agric., 2025, 238, 110824.

2. Chattopadhyaya A., Biswas S., Rakshit S., Jana N.D., Mondal A., Statistical Signal Processing and Machine Learning Based Diagnosis of Arrhythmia. Int. J. Comput. Inf. Syst. Ind. Manag. Appl., 2025, 17, 362–374.