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

Peilin Shao | AI and Automation in Architecture | Young Researcher Award

Published on by Architecture Engineers

Assist. Prof. Dr. Peilin Shao | AI and Automation in Architecture | Young Researcher Award

Lecturer | Shanxi Datong University | China

Assist. Prof. Dr. Peilin Shao is a Lecturer at Shanxi Datong University specializing in intelligent manufacturing, with academic training in industrial and manufacturing systems engineering as well as mechanical design, manufacturing, and automation. He has been actively involved in advanced research on assembly process modeling, knowledge graph construction, and AI-driven inference, contributing to projects that integrate large language models with manufacturing knowledge systems and supporting intelligent decision-making in complex engineering processes. His work encompasses assembly deviation analysis, process knowledge graph frameworks, and intelligent question-answering methods, with publications in reputable journals such as the Proceedings of the Institution of Mechanical Engineers, Applied Sciences, and the International Journal of Advanced Manufacturing Technology, alongside conference contributions in intelligent networked systems. Dr. Shao has demonstrated research leadership through collaborative projects and scholarly contributions that advance intelligent manufacturing. His research impact includes 8 citations, 5 publications, and an h-index of 1.

Profiles: Scopus | ORCID

Featured Publications

1. Qiao L., Shao P., Zhao H., Huang Z., An assembly deviation calculation method based on surface deviation modeling for circumferential grinding plane. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf., 2021.

2. Shao P., Huang Z., Qiao L., A novel assembly knowledge graph construction framework enhanced by large language model. Int. J. Adv. Manuf. Technol., 2025, Accepted.

Dr. Peilin Shao’s work advances intelligent manufacturing by integrating large language models with assembly process knowledge, enabling smarter, data-driven engineering decisions. His vision is to accelerate industry transformation through AI-enabled knowledge engineering that fosters sustainable, high-precision, and globally competitive manufacturing practices.

Minoor Lamian | Parametric Design | Best Researcher Award

Published on by Architecture Engineers

Dr. Minoor Lamian | Parametric Design | Best Researcher Award

Faculty Member | Tarbiat Modares University | Iran

Professor Minoor Lamyian is a distinguished Professor in the Department of Reproductive Health and Midwifery at Tarbiat Modares University, Tehran, Iran, specializing in maternal and child health, reproductive health, and health education. She holds a Ph.D. in Health Education, an M.Sc. in Maternal and Child Health from Tarbiat Modares University, and a B.Sc. in Midwifery from Shiraz University of Medical Sciences. With extensive academic service since the early 1990s, she has held leadership positions including Deputy Director of the Midwifery Department and Manager of the Medical Center at Tarbiat Modares University, while mentoring numerous students and professionals in the field. Her research focuses on women’s health literacy, safe motherhood, breastfeeding promotion, breast cancer prevention, and the application of behavior models and qualitative methods in reproductive health. She has authored numerous publications in international journals, including recent works on maternal nutrition, fertility preservation, and the psychosocial dimensions of women’s health, making significant contributions to public health scholarship. Professor Lamyian has also advanced scientific discourse through editorial roles with journals such as the Health Education & Promotion Journal and Social Behavior Research & Health Journal, and she actively contributes as a member of professional societies including the Breastfeeding Promotion Society of Iran. Her international academic engagement includes research collaboration at the University of Helsinki’s Finnish Cancer Registry. Recognized for her leadership, research excellence, and commitment to women’s health, Professor Lamyian continues to serve as a respected educator, researcher, and advocate in reproductive health and maternal well-being. She has 973 citations by 931 documents, 62 published documents, and an h-index of 17.

Profile: ORCID | Scopus

Featured Publications

1. Safaralinezhad A., Lamyian M., Ahmadi F., Hosseinkhani Z., Montazeri A., Development and psychometric properties of the Mental Health Literacy Scale for women of reproductive age: A mixed method study. Health Sci. Rep., 2025, 10.1002/hsr2.71227.

2. Moradi S., Lamyian M., Sahebi L., Investigating the prevalence and factors affecting pre-term and post-term labor in the 6 months before and after the Covid-19 pandemic: A comparative study. Payesh (Health Monitor) J., 2025, 10.61186/apyesh.24.3.355.

3. Bagheri M., Lamyian M., Sadighi J., Ahmadi F., Mohammadi-Nasrabadi F., Development and validation of a food security assessment questionnaire for pregnancy. Payesh (Health Monitor) J., 2025, 10.61186/payesh.24.2.269.

4. Roshandel S., Lamyian M., Azin S.A., Mohammadi E., Haghighat S., Providing fertility preservation services in breast cancer patients and barriers to receiving them in Iran: A qualitative study. J. Breast Dis., 2025, 10.61186/ijbd.18.1.107.

5. Bagheri M., Lamyian M., Sadighi J., Ahmadi F., Mohammadi-Nasrabadi F., Food security during pregnancy: A qualitative content analysis study in Iran. Matern. Child Nutr., 2025, 10.1111/mcn.13725.