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