Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

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Assoc. Prof. Dr. Entesar Eliwa | Deep Learning for Robotic Vision | Excellence in Research Award

King Faisal University | Saudi Arabia

Dr. Entesar Hamed I. Eliwa is an Associate Professor at King Faisal University, Faculty of Science, Department of Mathematics and Statistics. She holds a B.Sc. in Computer Science from Minia University, where she also served as a Teaching Assistant before completing her M.Sc. and Ph.D. in the Computer Science Department. After joining King Faisal University as an Assistant Lecturer, her strong research productivity and academic contributions led to her promotion to Associate Professor. Her work focuses on data mining, knowledge discovery, predictive modeling, supervised learning, classification, association rule mining, Deep Learning for Robotic Vision and artificial intelligence. She has successfully completed 28 research projects and is currently leading 6 ongoing studies. Her scholarly influence is reflected in her most recent citation metrics, with 443 total citations across 373 citing documents, demonstrating a solid and expanding research footprint. She has produced 22 research documents contributing to advancements in computational intelligence, and she maintains an h-index of 8, underscoring the depth and consistency of her academic impact. Through her research, publications, and academic service, Dr. Eliwa continues to strengthen the fields of computer science, data analytics, and artificial intelligence within both regional and global research communities.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

El Koshiry, A., Eliwa, E., Abd El-Hafeez, T., & Tony, M. A. A. (2026). The effectiveness of an e-learning platform in developing digital citizenship skills among blind students.  https://doi.org/10.1007/978-3-031-94770-4_21

Hamed, E., & Abd El-Hafeez, T. (2025). Deep learning for sustainable agriculture: Automating rice and paddy ripeness classification for enhanced food security. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100785

Amr, A., Eliwa, E., Tony, A. A., Shalgham, A., & Contributors from King Faisal University; Minia University; Arish University. (2025). The effectiveness of using Box-to-Box technology to develop some of the composite physical and technical capabilities of footballers. Fusion: Practice and Applications. https://doi.org/10.54216/fpa.170224

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A robust deep learning pipeline for multi-class cervical cancer cell identification. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2025.100787

Eliwa, E. H. I., & Abd El-Hafeez, T. (2025). A novel YOLOv11 framework for enhanced tomato disease detection. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.3200

Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

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Mr. Angelos Athanasiadis | Deep Learning for Robotic Vision | Research Excellence Award

Aristotle University of Thessaloniki | Greece

Mr. Angelos Athanasiadis is a Ph.d. candidate in electrical and computer engineering at the Aristotle University of Thessaloniki, specializing in fpga-based acceleration of convolutional neural networks and heterogeneous computing systems. he holds an M.Eng. in electronics and computer systems and an mba with high distinction, combining strong technical expertise with strategic insight. his research focuses on full-precision CNN acceleration, FPGA architectures, cyber-physical systems, Deep Learning for Robotic Vision and distributed embedded system emulation. Angelos has contributed to major eu-funded research projects, including the adviser and redesign projects, and has completed industrial internships at cadence design systems in Munich. He has also worked in r&d and embedded development roles at exapsys and seems pc, strengthening his applied engineering experience. Academically, he has collaborated with Professor Ioannis papaefstathiou and assistant professor nikolaos tampouratzis, contributing to innovations in energy-efficient cnn inference and high-fidelity system emulation. his open-source framework, fusion, integrates qemu and omnet++ using hla/certi for deterministic, timing-accurate, multi-node execution. Although early in his publication journey, angelos has 1 citation, 1 scopus-listed document, and an h-index of 1, reflecting the initial impact of his contributions. Driven by interdisciplinary research, he aims to advance reconfigurable computing for next-generation autonomous and embedded intelligent systems.

Profiles: Orcid | Google Scholar

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal – Works in Progress in Embedded Computing, Article 62.

Katselas, L., Athanasiadis, A., Jiao, H., Papameletis, C., Hatzopoulos, A., & Marinissen, E. J. (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs. In 2017 27th International Symposium on Power and Timing Modeling, Optimization and Simulation (PATMOS) (pp. 1–8). IEEE.

Assoc. Prof. Dr. Seulki Lee – Cognitive Robotics Systems – Best Researcher Award

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Assoc. Prof. Dr. Seulki Lee - Cognitive Robotics Systems - Best Researcher Award

Kwangwoon University - South Korea

Author Profile

SCOPUS

Summary

Dr. Seulki Lee, an associate professor at Kwangwoon university, is a leading academic in the field of construction engineering with a strong focus on digital transformation. her expertise spans digital knowledge representation, smart technology acceptance, off-site construction evaluation, and robotics simulation. with a comprehensive academic background from kwangwoon university and significant research experience at seoul national university, she has developed advanced methodologies integrating ontology, bim, digital twins, and power electronics. recognized by multiple prestigious awards, she continues to shape the future of construction technology through research, education, and mentorship.

Early academic pursuits

Dr. Seulki Lee’s academic journey began at Kwangwoon university, where she completed her bachelor’s degree in architectural engineering (2004–2008). her passion for construction and digital systems led her to pursue a master’s (2008–2010) and subsequently a ph.d. in construction management (2010–2014) from the same institution. Cognitive Robotics Systems her early research focused on integrating digital technologies with architectural processes—a foundation that continues to shape her work in domains such as building information modeling (bim), ontology, and power electronics in construction automation systems.

Professional endeavors

After earning her doctoral degree, Dr. lee served as a senior researcher at the institute of construction and environmental engineering, seoul national university (2014–2021). during this period, she deepened her expertise in simulation, prefabrication, and construction knowledge management. in march 2021, she returned to her alma mater, kwangwoon university, as an associate professor in the department of architectural engineering. her dual roles in research and academia have allowed her to mentor students, lead funded projects, and collaborate on national and international research initiatives, especially those integrating digital twins and robotics with power electronics.

Contributions and research focus

Digitalization of construction knowledge: She works on developing ontology-based frameworks for representing and automating reasoning over construction data using rdf, sparql, and large language models (llms). Smart technology acceptance and education: Her research explores how professionals adopt digital tools, focusing on the technology acceptance model (tam) and the development of Cognitive Robotics Systems effective safety-oriented training methods. Off-site construction and dfx evaluation: She has introduced structured evaluation systems for design for manufacturing and assembly (dfma) and quality control of prefabricated components. Construction robotics and simulation: She is a pioneer in integrating robot-friendly environments, bim, and digital twins to simulate and evaluate robotic task efficiency—especially in synergy with power electronics used in autonomous systems.

Impact and influence

Dr. Lee’s contributions have been recognized through numerous prestigious awards. these include the president’s commendation from the korean society of civil engineers (ksce) in 2025, the jeong won-seok special award in 2024, Cognitive Robotics Systems and multiple outstanding paper awards from kicem and aik. in addition, her student supervision excellence awards (2021–2024) highlight her role as a mentor and educator shaping the next generation of engineers.

Academic cites

Her work is frequently cited in areas of ontology-based construction knowledge, smart construction technologies, and construction robotics. she has contributed to numerous peer-reviewed publications and conference papers, often at the intersection of bim, simulation, and digital literacy. her academic visibility has made her a recognized authority in Cognitive Robotics Systems integrating semantic web technologies and ai reasoning into architectural and civil engineering workflows.

Legacy and future contributions

As a thought leader in the digitization of construction systems, dr. lee continues to influence policy, research, and education in smart construction. her future work aims to further automate decision-making processes through advanced reasoning engines and enhance human-robot collaboration for safer and more efficient work environments. with a Cognitive Robotics Systems commitment to standardization, sustainability, and educational reform, she is poised to leave a lasting legacy in both academia and industry.

Notable Publications

  1. Title: Automated Inference of Context-Specific Hazards in Construction Using BIM and Ontology
    Authors: [Author names not provided]
    Journal: Automation in Construction
    Year: 2025
  2. Title: A Practical Image Augmentation Method for Construction Safety Using Object Range Expansion Synthesis
    Authors: [Author names not provided]
    Journal: Buildings
    Year: 2025

Conclusion

Dr. Seulki Lee’s contributions reflect a deep commitment to modernizing construction practices through digital innovation and interdisciplinary integration. her impactful research in automation, smart education, and robotics-enabled environments—supported by technologies such as power electronics—positions her as a visionary leader in engineering. as construction rapidly evolves, her work will remain pivotal in fostering safer, smarter, and more efficient industry practices while inspiring future scholars and practitioners.