Sang-Su Lee | Engineering | Best Researcher Award

Best Researcher Award

Sang-Su Lee
Korean Register, Busan, South Korea
Sang-Su Lee
Affiliation Korean Register
Country South Korea
Scopus ID 58871667800
Documents 2
Citations 60
h-index 2
Subject Area Engineering
Event International Popular Scientist Awards

Sang-Su Lee is an engineering researcher affiliated with Korean Register in Busan, South Korea, whose indexed research includes marine engineering, ship propulsion systems, energy-efficiency regulations, shafting safety, and maritime decarbonization. The available Scopus profile information identifies two indexed documents, 60 citations, and an h-index of 2.[1]

Abstract

Sang-Su Lee’s indexed research reflects an engineering focus on maritime propulsion, ship energy efficiency, and the safety implications of technical modifications introduced to meet contemporary environmental-performance requirements. A notable 2024 article in Results in Engineering evaluates a redesigned shafting system following a propeller redesign intended to improve CII and EEXI performance. The study considers a 6500 TEU container ship and examines engine power limitations, torsional vibration, and shaft alignment to assess the safety of the modified propulsion arrangement. [1]

Keywords

Sang-Su Lee; Best Researcher Award; marine engineering; ship propulsion; shafting systems; propeller redesign; CII; EEXI; torsional vibration; shaft alignment; maritime decarbonization; energy efficiency; Korean Register.

Introduction

Maritime transportation is undergoing significant technical and regulatory changes aimed at improving energy efficiency and reducing greenhouse-gas emissions. International maritime regulations have introduced performance indicators including the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII), creating engineering requirements that affect propulsion-system design and vessel operation. Lee’s research addresses this intersection between regulatory performance and mechanical engineering by examining how modifications intended to improve vessel efficiency influence shafting-system safety. [1]

Research Profile

Lee’s documented research profile is situated within engineering applications related to maritime systems. His published work connects ship energy-efficiency requirements with propulsion-system analysis, particularly where changes to propeller or engine configurations can influence mechanical loads and shafting performance. The research therefore occupies an interdisciplinary position between marine engineering, mechanical-system safety, and environmental performance.

Research Contributions

The principal documented contribution concerns the engineering assessment of a redesigned shafting system implemented following propeller redesign intended to improve CII and EEXI performance. The study examined a 6500 TEU container ship and determined engine power limitations under EEXI-related requirements before assessing the resulting implications for CII. The redesigned shafting system was subsequently evaluated through torsional-vibration calculations and shaft-alignment analyses. [1]

Publications

The supplied Scopus record identifies two indexed documents for Sang-Su Lee. One documented publication is the 2024 open-access article listed below.

  1. Lee, Sang-Su. “Evaluating the safety of the redesigned shafting system following the propeller redesign aimed at enhancing CII and EEXI.” Results in Engineering, volume 23, September 2024, article 102352.[1]

Research Impact

According to the supplied researcher profile, Lee’s work has accumulated 60 citations across two indexed documents, with an h-index of 2. Bibliometric indicators can provide a quantitative indication of scholarly visibility, although they do not by themselves establish research quality or significance. In this context, the documented publication addresses a practical engineering problem relevant to ship operators, propulsion-system designers, classification and technical organizations, and researchers studying maritime energy efficiency.

Award Suitability

Sang-Su Lee’s documented research profile provides a relevant basis for consideration for the Best Researcher Award under the International Popular Scientist Awards. The basis for consideration includes an identifiable engineering research record, indexed scholarly publications, citation activity, and research addressing contemporary challenges in maritime propulsion and energy efficiency. [1]

Conclusion

Sang-Su Lee is an engineering researcher affiliated with Korean Register whose documented scholarly work addresses marine propulsion, shafting-system safety, and vessel energy-efficiency requirements. His 2024 publication on redesigned shafting following propeller redesign provides a concrete example of research connecting CII and EEXI objectives with torsional-vibration and shaft-alignment analysis. [1]

References

  1. Lee, Sang-Su. (2024). Evaluating the safety of the redesigned shafting system following the propeller redesign aimed at enhancing CII and EEXI. Results in Engineering, 23, 102352. Elsevier.
    DOI: https://doi.org/10.1016/j.rineng.2024.102352
  2. Directory of Open Access Journals. (2024). Evaluating the safety of the redesigned shafting system following the propeller redesign aimed at enhancing CII and EEXI. Author: Sang-Su Lee; affiliation information includes Korean Register, Busan, Republic of Korea.
    https://doaj.org/article/3340dc9a80af421f86bc57e253b94603
  3. Elsevier. (n.d.). Scopus author details: Sang-Su Lee, Author ID 58871667800. Scopus.
    https://www.scopus.com/pages/authors/58871667800

Abel Sepulveda | Engineering | Best Researcher Award

Best Researcher Award

Abel Sepúlveda
Eidgenössische Technische Hochschule Zürich, Switzerland

Abel Sepúlveda
Affiliation Eidgenössische Technische Hochschule Zürich
Country Switzerland
Scopus ID 57217417207
Documents 27
Citations 332
h-index 9
Subject Area Engineering
Event International Popular Scientist Awards
ORCID 0000-0001-6493-1582

Abel Sepúlveda is an engineering researcher whose published work addresses the interaction between building design, daylight, solar radiation, thermal comfort, outdoor environmental quality, and energy performance. His research record includes collaborative studies published in journals and scholarly book chapters covering building performance and environmental design. The documented publications provide a basis for considering his work within the scope of a Best Researcher Award in Engineering. [1][2]

Abstract

This article presents an academic recognition profile for Abel Sepúlveda in connection with the Best Researcher Award. His research is situated in Engineering and encompasses building environmental performance, daylight availability, solar radiation, thermal comfort, urban heat mitigation, and optimization of building shading and environmental conditions. The supplied research record reports 27 indexed documents, 332 citations, and an h-index of 9. These indicators are accompanied by publications addressing both methodological development and applied evaluation of building and urban environmental performance. [1]

Keywords

Abel Sepúlveda; Engineering; Building Performance; Daylighting; Solar Radiation; Thermal Comfort; Urban Heat Island; Energy Performance; Environmental Design; Shading Optimization; Best Researcher Award.

Introduction

Research in building and environmental engineering increasingly requires simultaneous consideration of energy use, daylight, thermal conditions, occupant experience, and urban environmental effects. Sepúlveda’s supplied publication record reflects this multidisciplinary direction, with studies examining design-stage solar radiation assessment, daylight and thermal comfort, outdoor thermal comfort, urban heat island mitigation, and static shading systems. [3][4]

Research Profile

The supplied Scopus information identifies Abel Sepúlveda with Author ID 57217417207 and reports 27 indexed documents, 332 citations, and an h-index of 9. These metrics provide bibliometric context for the profile but should be interpreted as database-dependent indicators rather than complete measures of research quality or significance. [1]

Research Contributions

A significant theme in the supplied record is the development and evaluation of methods for balancing competing environmental and energy objectives. The 2023 study on solar radiation-based methods considers early design stages in relation to daylight and thermal comfort in office buildings, linking environmental simulation and design decision-making. [5]

Publications

The following selected publications were supplied as part of the research record. DOI links provide persistent identifiers for the corresponding scholarly works.

  1. Urban Shaderade. Building Space Analysis Method for Energy and Sunlight Consideration in Urban Environments. Book chapter, 2023. Contributors: Francesco De Luca; Abel Sepúlveda.
  2. Solar radiation-based method for early design stages to balance daylight and thermal comfort in office buildings. Frontiers of Architectural Research, 2023. Contributors: Abel Sepúlveda; Seyed Shahabaldin Seyed Salehi; Francesco De Luca; Martin Thalfeldt.
  3. Outdoor Thermal Comfort Optimization in a Cold Climate to Mitigate the Level of Urban Heat Island in an Urban Area. Energies, 2023. Contributors: Nasim Eslamirad; Abel Sepúlveda; Francesco De Luca; Kimmo Sakari Lylykangas; Sadok Ben Yahia.
  4. Assessing the applicability of the European standard EN 17037:2018 for office spaces in a cold climate. Building and Environment, 2022. Contributors: Abel Sepúlveda; Francesco De Luca; Toivo Varjas; Jarek Kurnitski.

Research Impact

The supplied bibliometric record reports 332 citations and an h-index of 9 across 27 documents. Such indicators suggest that the published work has received measurable scholarly attention, while the actual interpretation of impact should account for publication age, field-specific citation practices, co-authorship, and database coverage. [1]

Award Suitability

For the purposes of an academic recognition profile, the Best Researcher Award can be evaluated against evidence such as publication activity, documented research contributions, scholarly impact, methodological relevance, and alignment with the award’s Engineering category. The supplied record provides evidence across these dimensions without requiring claims beyond the documented publications and bibliometric information.

Conclusion

Abel Sepúlveda’s documented research profile reflects sustained activity in Engineering, particularly in the analysis and optimization of building and urban environmental performance. His selected publications address daylight, solar radiation, thermal comfort, shading, energy considerations, and urban environmental quality through interdisciplinary and applied research. The supplied bibliometric indicators and publication record provide a structured basis for consideration in the Best Researcher Award category.

References

  1. Elsevier. (n.d.). Scopus author details: Abel Sepúlveda, Author ID 57217417207. Scopus.
    https://www.scopus.com/pages/authors/57217417207
  2. Crossref. (n.d.). Metadata records for scholarly publications by Abel Sepúlveda and collaborators.
  3. Sepúlveda, A., Seyed Salehi, S. S., De Luca, F., & Thalfeldt, M. (2023). Solar radiation-based method for early design stages to balance daylight and thermal comfort in office buildings. Frontiers of Architectural Research.
  4. De Luca, F., Sepúlveda, A., & collaborators. (2022–2023). Selected research publications concerning building environmental performance, daylighting, thermal comfort, and energy considerations. Crossref-indexed records.
  5. Sepúlveda, A., Seyed Salehi, S. S., De Luca, F., & Thalfeldt, M. (2023). Solar radiation-based method for early design stages to balance daylight and thermal comfort in office buildings. Frontiers of Architectural Research.

Afşin Baran Bayezit | Engineering | Research Excellence Award

Mr. Afşin Baran Bayezit | Engineering | Research Excellence Award

Research Assistant at Istanbul Technical University | Turkey

Research engineer specializing in maritime artificial intelligence and control systems, with strong expertise in reinforcement learning, machine learning, and control theory for autonomous platforms. Demonstrates proficiency in developing and validating intelligent control algorithms using Python, embedded systems, and ROS, with hands-on implementation in real-world and model-scale environments. Contributed to advanced research in ship dynamics, autopilot systems, and safety modeling through data-driven approaches. Experienced in integrating sensors, actuators, and high-performance computing tools to optimize system performance. Professional experience reflects a consistent focus on innovative, experimentally validated solutions for autonomous maritime systems, delivering impactful contributions to intelligent navigation, system efficiency, and safety.

Citation Metrics (Google Scholar)

40

30

20

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0

Citations
32

i10index
1

h-index
2

🟦 Citations    🟥 i10-index    🟩 h-index


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Young Won Kim | Engineering | Research Excellence Award

Dr. Young Won Kim | Engineering | Research Excellence Award

Senior Researcher | Korea Institute of Industrial Technology | South Korea

Dr. Young Won Kim is a researcher specializing in advanced manufacturing, smart materials, and energy harvesting technologies, with strong expertise in additive manufacturing, digital twin systems, and nano/micro-fabrication. His research focuses on triboelectric and piezoelectric nanogenerators, sensor development, and AI-driven predictive modeling for smart manufacturing applications. He has contributed extensively to high-impact international journals as both lead and corresponding author, particularly in nanomaterials, flexible electronics, and biomedical scaffolds. With 68 publications, 1,085 citations, and an h-index of 17, his work reflects strong academic impact. His professional experience spans academic and industrial research environments, integrating machine learning, materials science, and mechanical engineering to develop innovative systems for energy, healthcare, and intelligent industrial technologies.

Citation Metrics (Scopus)

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Citations
1085

Documents
68

h-index
17

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Featured Publications

Genetic Insights into Avian Influenza Resistance in Jeju Island Chickens

– Journal of Animal Science and Technology, 2025

You Zhang | Engineering | Best Researcher Award

Mr. You Zhang | Engineering | Best Researcher Award

University of Rochester, United States

Dr. You (Neil) Zhang is a Ph.D. candidate in Electrical and Computer Engineering at the University of Rochester, specializing in machine learning for speech, acoustics, and audio signal processing. His research focuses on spatial audio (HRTF personalization), speech anti-spoofing, singing voice deepfake detection, and audio-visual learning. He has held research roles at Dolby, Meta, Microsoft, Tencent, and Bytedance, contributing significantly to areas like perceptual HRTF modeling and audio-visual deepfake detection.

Profile:

🎓 Education:

  • Ph.D. in Electrical & Computer Engineering (Expected 2025)
    University of Rochester

  • M.S., University of Rochester

  • B.Eng., University of Electronic Science & Technology of China

  • Exchange Program, UC Berkeley

🧠 Research Interests:

  • Spatial Audio & HRTF Personalization 🎧

  • Speech Deepfake Detection & Audio Security 🔐

  • Multimodal Learning: Audio-Visual & Emotional Speech Synthesis 🎥🗣️

🏆 Honors & Fellowships:

  • IEEE SPS Scholarship (2024)

  • NIJ Graduate Research Fellowship (2023)

  • ICASSP Rising Star in Signal Processing (2023)

  • Open Scholarship Award @ UR (2025)

🧪 Research & Industry Experience:

  • Dolby Labs 🎶 – Sr. Researcher, Multimodal Spatial Audio

  • Meta Reality Labs 🧠 – HRTF Perceptual Learning

  • Microsoft, Tencent, ByteDance, IngenID 💼 – AI R&D Internships

  • Audio Information Research Lab, UR 🎙️ – Deepfake Detection, AV Speech, HRTF Neural Fields

📚 Selected Publications:

  • IEEE T-MM, SPL, ICASSP, Interspeech, NAACL

  • Co-organizer of SVDD Challenge at SLT 2024 & MIREX 2024

  • Contributor to Handbook of Biometric Anti-spoofing (Springer)

🎤 Talks & Tutorials:

  • Invited speaker at CMU, NII Japan, ISCA SPSC

  • Tutorials @ ASA, ICME, AES (Topics: HRTF, Deepfakes, ML for Acoustics)

🎓 Teaching & Mentorship:

  • TA for Machine Learning, Audio Signal Processing, Random Processes

  • Mentored 10+ undergrad and graduate students in UR, Tsinghua, UESTC

💼 Professional Service:

  • Reviewer for IEEE TASLP, TPAMI, ICASSP, Interspeech, CVPR Workshops

  • Member: IEEE, ASA, ACM, AES

  • DEI Committee @ UR ECE | Organizer of AR/VR Events

💻 Skills:

  • Programming: Python, MATLAB, C, Java

  • Tools: Git, Linux, PyTorch, Slurm

  • Languages: English 🇺🇸, Mandarin 🇨🇳

🏃 Hobbies & More:

  • Half-Marathon Finisher 🏅

  • Loves stand-up paddleboarding, travel, badminton 🌊✈️🏸

Google Scholar Citation Metrics:

  • Citations: 751 (All time) | 751 (Since 2020)

  • h-index: 12 (All time) | 12 (Since 2020)

  • i10-index: 14 (All time) | 14 (Since 2020)

Publication Top Notes:

  1. One-class Learning Towards Synthetic Voice Spoofing Detection
    Y. Zhang, F. Jiang, Z. Duan
    IEEE Signal Processing Letters, vol. 28, pp. 937–941, 2021.

  2. Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
    S.E. Eskimez, Y. Zhang, Z. Duan
    IEEE Transactions on Multimedia, vol. 24, pp. 3480–3490, 2021.

  3. UR Channel-Robust Synthetic Speech Detection System for ASVspoof 2021
    X. Chen, Y. Zhang*, G. Zhu*, Z. Duan
    ASVspoof 2021 Workshop, 2021.

  4. SingFake: Singing Voice Deepfake Detection
    Y. Zang, Y. Zhang*, M. Heydari, Z. Duan
    IEEE ICASSP, 2024.

  5. An Empirical Study on Channel Effects for Synthetic Voice Spoofing Countermeasure Systems
    Y. Zhang, G. Zhu, F. Jiang, Z. Duan
    Interspeech, pp. 4309–4313, 2021.

  6. SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing
    S. Ding, Y. Zhang, Z. Duan
    IEEE ICASSP, 2023.

  7. A Probabilistic Fusion Framework for Spoofing Aware Speaker Verification
    Y. Zhang, G. Zhu, Z. Duan
    Odyssey: The Speaker and Language Recognition Workshop, pp. 77–84, 2022.

  8. Global HRTF Personalization Using Anthropometric Measures
    Y. Wang, Y. Zhang, Z. Duan, M. Bocko
    Audio Engineering Society (AES) 150th Convention, 2021.

  9. Rethinking Audio-Visual Synchronization for Active Speaker Detection
    A. Wuerkaixi, Y. Zhang, Z. Duan, C. Zhang
    IEEE MLSP, 2022.

  10. CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection
    Y. Zang, J. Shi, Y. Zhang, et al.
    Interspeech, pp. 4783–4787, 2024.

  11. VERSA: A Versatile Evaluation Toolkit for Speech, Audio, and Music
    J. Shi, H. Shim, J. Tian, Y. Zhang, et al.
    NAACL (Demo Track), 2025.

  12. HRTF Field: Unifying Measured HRTF Magnitude Representation with Neural Fields
    Y. Zhang, Y. Wang, Z. Duan
    IEEE ICASSP, 2023.

  13. SVDD 2024: The Inaugural Singing Voice Deepfake Detection Challenge
    Y. Zhang, Y. Zang, J. Shi, R. Yamamoto, T. Toda, Z. Duan
    IEEE SLT, pp. 782–787, 2024.

  14. DyViSE: Dynamic Vision-Guided Speaker Embedding for Audio-Visual Speaker Diarization
    A. Wuerkaixi, K. Yan, Y. Zhang, Z. Duan, C. Zhang
    IEEE MMSP, 2022.

  15. Predicting Global Head-Related Transfer Functions from Scanned Head Geometry Using Deep Learning and Compact Representations
    Y. Wang, Y. Zhang, Z. Duan, M. Bocko
    arXiv preprint, arXiv:2207.14352, 2022.

  16. Learning Arousal-Valence Representation from Categorical Emotion Labels of Speech
    E. Zhou, Y. Zhang, Z. Duan
    IEEE ICASSP, 2024.

  17. SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
    Y. Zhang, Y. Zang, J. Shi, et al.
    arXiv preprint, arXiv:2405.05244, 2024.

  18. ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech
    X. Wang, H. Delgado, Y. Zhang, et al.
    Computer Speech & Language, 2025.

  19. Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions
    K. Zhou, Y. Zhang, S. Zhao, et al.
    arXiv preprint, arXiv:2409.16681, 2024.

  20. Mitigating Cross-Database Differences for Learning Unified HRTF Representation
    Y. Wen, Y. Zhang, Z. Duan
    IEEE WASPAA, 2023.