Deborah Akuoko-Minka | Robotics | Computer Vision Contribution Award

Published on by Computer Scientists Awards

Computer Vision Contribution Award

Deborah Akuoko-Minka
The University of Edinburgh, United Kingdom

Deborah Akuoko-Minka
Affiliation The University of Edinburgh
Country United Kingdom
Google Scholar ID ab0EyjYAAAAJ
Documents 9
Citations 3
h-index 1
Subject Area Robotics
Event Computer Scientists Awards
ORCID 0009-0008-6219-154X

The Computer Vision Contribution Award recognizes scholarly achievements that advance computer vision, intelligent sensing, and robotics through innovative research. Deborah Akuoko-Minka of The University of Edinburgh has contributed to emerging studies involving transient imaging, material-aware perception, optical sensing, and machine vision methodologies. Her recent publications demonstrate an interdisciplinary approach that integrates computer vision with photonics and intelligent robotic perception, supporting research into robust sensing technologies for challenging environments.[1]

Abstract

Deborah Akuoko-Minka’s academic work explores computer vision techniques that combine transient imaging, intelligent sensing, and material-aware analysis. Her research investigates optical signal interpretation for object recognition and material classification while supporting robotics and automated inspection applications. These studies illustrate the integration of computational imaging with practical sensing challenges and contribute to ongoing developments in intelligent visual systems.[2]

Keywords

Computer Vision, Robotics, SPAD Imaging, Material Classification, Intelligent Sensing, Optical Perception, Machine Vision, Time-Resolved Imaging.

Introduction

Modern computer vision increasingly extends beyond conventional image analysis by incorporating temporal, optical, and physical characteristics of observed scenes. Deborah Akuoko-Minka’s publications reflect this direction through research on transient vision and intelligent perception for material-aware recognition. Such investigations support improved sensing accuracy under conditions where traditional imaging approaches may be limited.[3]

Research Profile

Her scholarly profile includes publications in computational imaging, optical sensing, robotics, and machine perception. Available research metrics indicate nine scholarly documents with citations demonstrating the early dissemination of her work. The research portfolio emphasizes experimental validation, reproducible sensing methodologies, and interdisciplinary collaboration between computer vision and photonic technologies.[1]

Research Contributions

  • Development of transient vision techniques for material-aware object detection.
  • Research on SPAD-based time-resolved sensing for homogeneous material analysis.
  • Investigation of optical sensing methods for milk purity assessment.
  • Application of intelligent computer vision approaches within robotics and automated perception.

Publications

  • Beyond Appearance: Transient Vision for Homogenised Milk Purity (2026).
  • Beyond Appearance: Intelligent Spatiotemporal Vision for Material-Aware Object Detection (2026).
  • Time-Resolved SPAD Transients for Milk Purity Assessment (2026).
  • Non-Spectral Time-Resolved SPAD Sensing for Flat Homogeneous Material Classification (2026).

Research Impact

The research demonstrates the growing role of transient imaging and intelligent sensing within computer vision. By combining optical physics with computational analysis, these studies contribute to future developments in robotic perception, quality inspection, industrial automation, and material recognition. The interdisciplinary nature of this work provides a foundation for continued investigation across vision science and intelligent robotics.[4]

Award Suitability

Deborah Akuoko-Minka’s contributions align with the objectives of the Computer Vision Contribution Award by advancing innovative research in computer vision, robotics, and intelligent sensing. Her publications emphasize methodological development, interdisciplinary collaboration, and practical applications of advanced visual technologies while maintaining a strong academic research focus.[5]

Conclusion

The academic profile presented here highlights Deborah Akuoko-Minka’s developing contributions to computer vision and robotics through research on transient sensing, optical perception, and intelligent material analysis. These studies demonstrate an interdisciplinary approach that supports future advances in automated perception, computational imaging, and intelligent robotic systems.[5]

References

  1. Elsevier. (n.d.). Google Scholar author details: Deborah Akuoko-Minka, Author ID ab0EyjYAAAAJ.
    https://scholar.google.co.uk/citations?hl=en&user=ab0EyjYAAAAJ
  2. Akuoko-Minka, D. (2026). Beyond Appearance: Transient Vision for Homogenised Milk Purity.
    https://doi.org/10.21203/rs.3.rs-10434018/v1
  3. Akuoko-Minka, D. (2026). Beyond Appearance: Intelligent Spatiotemporal Vision for Material-Aware Object Detection.
    https://doi.org/10.21203/rs.3.rs-10412364/v1
  4. Akuoko-Minka, D. (2026). Time-Resolved SPAD Transients for Milk Purity Assessment.
    https://doi.org/10.1364/opticaopen.31964601
  5. Akuoko-Minka, D. (2026). Non-Spectral Time-Resolved SPAD Sensing for Flat Homogeneous Material Classification.
    https://doi.org/10.1364/opticaopen.31957221

Dr. Adina Aniculaesei | Technology | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Adina Aniculaesei | Technology | Best Researcher Award

Dr. Adina Aniculaesei , Postdoctoral Researcher, Department of Computer Science and Engineering, University of Gothenburg and Chalmers University of Technology, Sweden.

Adina Aniculăesei is a passionate researcher and expert in automated safety‑critical systems, currently based in Gothenburg, Sweden. Born in Iași, Romania, she has dedicated her career to making autonomous vehicles and mobile robots safer, focusing on verification, formal methods, and runtime validation. Through years of multidisciplinary research and teaching, she has shaped the future of software engineering for intelligent transportation and collaborative robotics. Her deep knowledge of formal verification and system modeling has positioned her as a leading voice in the realm of dependable and trustworthy autonomous platforms, making significant impacts in both academia and industry.

Publication Profile

Google Scholar

🎓 Education Background

Adina earned her Doctorate (Dr. rer. nat.) in Computer Science from the Clausthal University of Technology, Germany, in 2024, graduating magna cum laude. She holds an M.Sc. in Computer Science from the Technical University of Braunschweig (2011) and a B.Sc. in Computer Science from Alexandru Ioan Cuza University, Romania (2007). An Erasmus–Socrates scholar, she enriched her studies with a year at the Technical University of Braunschweig. Her rigorous training combined formal methods, software engineering, and automated test case generation, making her adept at tackling complex, safety‑critical domains.

💼 Professional Experience

Adina Aniculăesei has worked as a Postdoctoral Researcher at the University of Gothenburg and Chalmers University of Technology (since October 2024), focusing on translating formal behavioral specifications into ROS2 nodes for collaborative robot applications. Previously, she served as a Doctoral Researcher and Research Assistant at TU Clausthal, leading industry collaborations, teaching, and mentoring students. Her experience includes roles across software and systems engineering, with a strong focus on safety, formal verification, and automated test generation for automotive and robotics domains, making her a sought‑after expert and educator in the field.

🏅 Awards and Honors

Throughout her academic journey, Adina Aniculăesei has been recognized for excellence and dedication. She received the Siemens Master Program Scholarship (2007–2009) and the Erasmus–Socrates Scholarship (2005–2006). Her doctoral studies earned her the magna cum laude distinction upon defending her Ph.D. thesis at Clausthal University of Technology in 2024. Additionally, she holds technical certifications including ISAQB Certified Professional for Software Architecture and ISTQB Certified Tester Foundation Level, highlighting her commitment to mastering both theoretical and practical elements of her field.

🔍 Research Focus

Adina Aniculăesei’s research centers on formal verification, automated test generation, and runtime monitoring for automated safety‑critical and collaborative multi‑agent systems. She explores methods for specifying, verifying, and validating complex operational design domains (ODDs) for autonomous vehicles and mobile robots. Her expertise includes formal methods (SPIN, NuSMV, PRISM), test case generation, model checking, and AI‑based environment perception, making her work pivotal in shaping next‑generation transportation and robotics technologies.

✅ Conclusion

With a profound background in formal methods, automated test generation, and verification of safety‑critical systems, Adina Aniculăesei has established herself as an influential expert in both academia and industry. Her dedication to mentoring students, publishing impactful research, and collaborating with international institutions has positioned her as a thought leader in software engineering for dependable, trustworthy, and safe autonomous technologies.

📚 Publication Top Notes

  • Towards a holistic software systems engineering approach for dependable autonomous systems – Proceedings of the 1st International Workshop on Software Engineering for AI (2018). Cited by 70
  • Towards the verification of safety‑critical autonomous systems in dynamic environments – arXiv preprint (2016). Cited by 42
  • Automated generation of requirements‑based test cases for an adaptive cruise control system – IEEE Workshop on Validation, Analysis and Evolution of Software Tests (2018). Cited by 24
  • UML‑based analysis of power consumption for real‑time embedded systems – IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (2011). Cited by 24
  • Graceful degradation of decision and control responsibility for autonomous systems based on dependability cages – 5th International Symposium on Future Active Safety Technology Toward Zero Accidents (2019). Cited by 14

 

Xiaodong Feng | Robotics | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Xiaodong Feng | Robotics | Best Researcher Award

Prof. Xiaodong Feng at Shaoxing University, China

Feng Xiaodong, born in June 1987, is an Associate Professor at the School of Civil Engineering, Shaoxing University of Arts and Sciences. Recognized as one of the first leading talents in Zhejiang Province’s 5246 Talent Project and a young talent under Shaoxing’s Special Branch Plan, he specializes in structural engineering. With an international academic footprint and a strong background in intelligent structural systems, Dr. Feng has led numerous national-level projects and published extensively in high-impact journals. His work integrates innovative structural design with smart technologies, contributing significantly to the advancement of flexible and adaptive engineering solutions. 🌉📚🤖

Publication Profile

Scopus

Academic Background

Feng Xiaodong completed his undergraduate studies in Civil Engineering at Central South University (2006–2010), followed by a master’s in Solid Mechanics (2010–2012) and a Ph.D. in Structural Engineering (2012–2016), under Professor Guo Shaohua. He then pursued postdoctoral research at Zhejiang University (2018–2020) with Professor Luo Yaozhi. His academic training spans mechanics, structural analysis, and intelligent systems, providing a robust foundation for his interdisciplinary research. 📘🎓🔬

Professional Experience

Dr. Feng began his academic career at Shaoxing University of Arts and Sciences in 2016 as a Lecturer, advancing to Associate Professor and Laboratory Director in 2021. He also served as a visiting scholar at Kyoto University, Japan (2022–2023). Throughout his career, he has led key research initiatives, mentored students, and collaborated with both academic and industrial partners on advanced structural systems. His experience bridges practical engineering applications and cutting-edge research. 🏢👨‍🏫🌍

Awards and Honors

Feng Xiaodong has received multiple prestigious awards, including two First Prizes and two Second Prizes from the China Steel Structure Association for technological innovation and scientific progress in large-span structures. He also received honors from the Invention and Entrepreneurship Award and Zhejiang Province. These accolades recognize his contributions to the development, design, and digital construction of complex spatial structures, as well as intelligent construction technologies. His pioneering work in structural mechanics and smart infrastructure has earned both national and regional acclaim. 🏆🏗️

Research Focus

Dr. Feng’s research revolves around flexible, movable, and intelligent structures, integrating AI and machine learning for structural design and health monitoring. His key interests include tensegrity structures, prefabricated systems, large-span spatial structures, and structural dynamics. He also focuses on collaborative structural-material design and building industrialization. His interdisciplinary approach combines theoretical innovation with practical applications, aimed at advancing the construction industry’s automation and intelligence. 🤖🧠🏗️📊

Publication Top Notes

📄 Vibration control and robustness analysis of tensegrity structures via fuzzy dynamic sliding mode control method
🗓️ Year: 2024 | 📚 Journal: Structures | 📊 Cited by: 3

📄 Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection
🗓️ Year: 2024 | 📚 Journal: Applied Intelligence | 📊 Cited by: 1

📄 Structural-topic aware deep neural networks for information cascade prediction
🗓️ Year: 2024 | 📚 Journal: PeerJ Computer Science | 📊 Cited by: 1

Conclusion

Dr. Feng Xiaodong, an Associate Professor at Shaoxing University of Arts and Sciences, is a nationally recognized expert in Structural Engineering, specializing in intelligent structures, AI-integrated design, and prefabricated construction technologies. With over 20 peer-reviewed publications in leading journals such as Soft Robotics, Structures, and Structural Control & Health Monitoring, he demonstrates consistent research excellence. As the principal investigator of numerous national and regional research projects, and a recipient of multiple high-level awards from the China Steel Structure Association and Zhejiang Province, Dr. Feng has shown both academic leadership and practical innovation. His international exposure as a Visiting Scholar at Kyoto University and his role in training future engineers across disciplines further underscore his qualifications. Dr. Feng’s contributions significantly advance the field of intelligent structural systems and make him an outstanding candidate for a Best Researcher Award.