Ezgi İlhan | User experience design | Best Researcher Award

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Dr. Ezgi İlhan | User experience design | Best Researcher Award

Dr. Ezgi Ilhan is an accomplished academic and designer specializing in industrial design, user experience, and gamification. Born in Ankara in 1985, she has cultivated a rich academic and professional career blending design, technology, and research. Currently serving as Assistant Professor at Gazi University, Ankara, she teaches diverse design courses while actively engaging in innovative research. Dr. Ilhan holds a PhD in Industrial Design from Gazi University, an MSc in Game Technologies from Middle East Technical University (METU), and extensive international exposure through Erasmus at Universidad de Valladolid, Spain. With professional experience spanning product design, game design, and user experience, her work focuses on integrating eye-tracking technologies, usability studies, and human-computer interaction. Dr. Ilhan has published extensively in international journals, presented at global conferences, and earned multiple awards, including the European Product Design Award. Fluent in English, with working knowledge of Spanish and German, she exemplifies multidisciplinary expertise in design innovation.

Profile

🎓 Education

Dr. Ezgi Ilhan pursued diverse academic training blending design, technology, and user experience. She earned her PhD in Industrial Design from Gazi University (2015-2021), achieving a remarkable GPA of 3.98/4. Earlier, she completed her MSc in Game Technologies at Middle East Technical University (METU), Ankara (2012-2015), with a GPA of 3.79/4. Her bachelor’s degree in Industrial Design was also from METU (2006-2009), where she transferred successfully from the City and Regional Planning program (2003-2006). As part of Erasmus, she studied Industrial Design Technical Engineering at Universidad de Valladolid, Spain, achieving 9.5/10. Dr. Ilhan’s strong educational foundation began at Dr. Binnaz-Rıdvan Ege Anatolian High School, Ankara, where she excelled in Mathematics-Science (4.98/5). Her academic journey reflects a consistent record of excellence, with multiple high honors and honors at METU, preparing her for interdisciplinary research integrating design, technology, human-computer interaction, and gamification.

🧪 Experience

Dr. Ezgi Ilhan’s professional career spans academia, industry, and research. Since September 2022, she has been an Assistant Professor at Gazi University, Ankara, teaching courses such as Computer-Aided Design, Product Design, and Competition-Oriented Design. She also holds part-time teaching roles at METU, TOBB ETU, and previously at Ostim Technical University. At Atılım University, she progressed from Research Assistant (2014–2021) to Assistant Professor (2022), teaching a wide range of design courses and managing administrative duties. In industry, she worked as a Game Designer at Pixofun (2011–2013), developing gamified applications, simulations, and game-based education programs. Earlier, she served as a Product Designer at Journey (2009–2011), overseeing production stages, cost analysis, and design guidance. Her experience also includes internships and student assistant roles in graphic design and industrial design at METU and Vestel. This diverse experience supports her expertise in blending academic theory with practical design and user experience innovation.

🏅 Awards and Honors

Dr. Ezgi Ilhan has received multiple awards recognizing her excellence in design and academia. Internationally, she won the European Product Design Award MECON (2021) for Office Equipment/Furnishings/Modules and secured a Silver Winner position at the Muse Design Awards MECON (2021) in Furniture/Office Furniture. During her academic journey, she earned METU High Honor and Honor Student distinctions across multiple semesters. She holds various certificates, including the De Gruyter Training Certificate (2019), Industrial Technology Design Certificate (2010), Spanish Course Completion Certificate (2009), and several certifications in human resources management and computer modeling. Dr. Ilhan has also participated in prestigious events such as the Game Developers Conference (GDC) Europe, Global Game Jam Jury, Paris Fashion Week, and numerous international conferences. These accolades highlight her continuous professional development, global engagement, and excellence in design, research, and teaching.

🔬 Research Focus

Dr. Ezgi Ilhan’s research interests lie at the intersection of industrial design, human-computer interaction, user experience, and gamification. Her work emphasizes the integration of eye-tracking technologies to inform design decisions, enhance usability, and improve user interaction with products and digital interfaces. She has explored topics such as mobile app gamification for improving sleep behaviors, aesthetic evaluation using eye-tracking, usability evaluation in gaming environments, and technology-driven design methodologies. With numerous publications in high-impact journals such as International Journal of Human-Computer Studies, Multimedia Tools and Applications, Entertainment Computing, and Displays, her research contributes valuable insights into technology-supported design processes. She actively presents her work at international conferences, addressing global audiences on cutting-edge design approaches. Her multidisciplinary approach bridges technology, psychology, and design, aiming to create more intuitive, user-centered products and digital experiences that foster engagement, functionality, and satisfaction.

✅ Conclusion

Dr. Ezgi Ilhan is a distinguished scholar whose multidisciplinary expertise in industrial design, user experience, and gamification, supported by extensive academic excellence, innovative research, global awards, and diverse professional experience, establishes her as a leading figure advancing human-centered design and technology-driven innovation.

Publications

Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Abdeldjalil Ouahabi | Face recognition | Best Paper Award

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Prof. Abdeldjalil Ouahabi | Face recognition | Best Paper Award

Dr. Abdeldjalil Ouahabi is a distinguished Full Professor and leading researcher at the iBrain INSERM laboratory, University of Tours, France. His pioneering work spans signal and image processing, biomedical engineering, and artificial intelligence. With over 170 peer-reviewed publications, his research has significantly advanced multiple scientific domains. Dr. Ouahabi holds key editorial roles in top-tier journals, including associate editorships at the Journal of King Saud University – Computer and Information Sciences and the International Journal of Imaging Systems and Technology. He is internationally recognized for his academic contributions, collaborative AI research with Fields Medalist Prof. Cédric Villani, and numerous visiting positions, notably at Bucknell University and Qatar University. In addition to scholarly engagement, he frequently appears on media platforms and organizes high-impact workshops and conferences. A respected scientific leader in Algeria and France, he is also an active member of governmental and civic committees focused on research policy and regional development.

Profile

🎓 Education

Although specific degree information is not detailed in the provided content, it is evident that Dr. Abdeldjalil Ouahabi has received extensive academic and research training, leading to his current role as a Full Professor and researcher at the iBrain INSERM laboratory, University of Tours. His educational background must include advanced degrees (PhD or equivalent) in electrical engineering, biomedical engineering, or related fields, enabling him to contribute profoundly to signal processing and AI. His international exposure—highlighted by academic appointments in the United States (Bucknell University) and the Middle East (Qatar University)—reflects a strong foundation in interdisciplinary learning and cross-cultural academic collaboration. His continuous involvement in editorial boards and scientific committees further implies rigorous scholarly preparation and a sustained commitment to academic excellence and innovation throughout his educational and professional journey.

🧪 Experience

Dr. Ouahabi’s professional experience spans academia, research, science communication, and governmental advisory roles. As a Full Professor at the University of Tours, he conducts research at the renowned iBrain INSERM laboratory. He has served as a Visiting Professor at Bucknell University (USA, 2010) and as a Visiting Scholar at Qatar University (2016–2017), conducting pioneering AI research. He is an Associate Editor for leading journals (Elsevier and Wiley) and organizes high-level scientific events, including the EEA Club Congress (2009) and national seminars in Algeria (2023–2024). His expertise has earned him invitations to collaborate with top researchers, including Prof. Cédric Villani. In 2022, he was appointed to Algeria’s DGRSDT Standing Sector Committee and, in 2023, joined a citizens’ panel in France for long-term policy planning. He frequently shares scientific insights on international TV, underlining his role as both a scholar and a public intellectual.

🏅 Awards and Honors

Dr. Abdeldjalil Ouahabi has received numerous honors recognizing his outstanding contributions to research and academia. He was named Outstanding Reviewer by Knowledge-Based Systems (Elsevier, Q1) in 2018 and by Measurement (Elsevier, Q1) in 2016. Early in his career, he earned the Best Paper Award from the IEEE Instrumentation and Measurement Society in 1999. In 2010, he was honored as Outstanding Visiting Professor at Bucknell University in the U.S. His scientific impact also led to collaborative work with Nobel-level minds, such as Fields Medalist Prof. Cédric Villani. He has received national recognition in Algeria, being appointed to the DGRSDT Standing Sector Committee in 2022 and organizing government-endorsed scientific seminars. These honors not only reflect the scholarly quality of his work but also his leadership in advancing scientific policy, mentoring, and international collaboration.

🔬 Research Focus

Dr. Ouahabi’s research integrates image and signal processing, biomedical engineering, and artificial intelligence with a strong focus on interdisciplinary applications. At the iBrain INSERM laboratory, he explores the intersection of computational modeling and medical diagnostics, contributing significantly to healthcare technologies. His work extends into intelligent systems and machine learning, particularly for enhancing neuroimaging, medical signal analysis, and real-time processing frameworks. He actively collaborates with international researchers on projects aimed at societal impact, including an ambitious AI initiative with Prof. Cédric Villani to advance Algeria’s scientific ecosystem. He emphasizes reproducibility, algorithmic transparency, and journal quality, often training PhD candidates and researchers in scientific writing and journal selection. Dr. Ouahabi’s 170+ publications in top-tier journals reflect a consistent output in high-impact research areas. His work not only advances theory but also promotes real-world applications, especially in smart healthcare systems and regional innovation planning.

✅ Conclusion

Dr. Abdeldjalil Ouahabi is a globally recognized professor and researcher whose interdisciplinary contributions to artificial intelligence, biomedical engineering, and signal processing have profoundly impacted both academia and public policy, earning him international awards, editorial positions in top journals, and leadership roles in science communication and national research development.

 

Publications

  • Neonatal EEG classification using a compact support separable kernel time–frequency distribution and attention-based CNN

    Biomedical Signal Processing and Control
    2025-12 | Journal article
    DOI: 10.1016/j.bspc.2025.108072
    CONTRIBUTORS: Arezki Larbi; Mansour Abed; Jaime S. Cardoso; Abdeljalil Ouahabi

  • Advanced genetic image encryption algorithms for intelligent transport systems

    Computers and Electrical Engineering
    2025-04 | Journal article
    DOI: 10.1016/j.compeleceng.2025.110162
    CONTRIBUTORS: Ismahane Souici; Meriama Mahamdioua; Sébastien Jacques; Abdeldjalil Ouahabi

  • Human Cutaneous Leishmaniasis in North Africa and Its Threats to Public Health: A Statistical Study Focused on Djelfa (Algeria)

    Microorganisms
    2023-10-22 | Journal article
    DOI: 10.3390/microorganisms11102608
    CONTRIBUTORS: Fatma Messaoudene; Slimane Boukraa; Said Chaouki Boubidi; Ahlem Guerzou; Abdeldjalil Ouahabi

  • Post-COVID-19 Education for a Sustainable Future: Challenges, Emerging Technologies and Trends

    Sustainability
    2023-04-11 | Journal article
    DOI: 10.3390/su15086487
    CONTRIBUTORS: Sébastien Jacques; Abdeldjalil Ouahabi; Zoe Kanetaki

  • Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

    Cancers
    2022-09 | Journal article | Author
    DOI: 10.3390/cancers14184399
    CONTRIBUTORS: Naoual Atia; Amir Benzaoui; Sébastien Jacques; Madina Hamiane; Kaouther El Kourd; Ayache Bouakaz; Abdeldjalil ouahab

Cheng Cheng | Emotion and Cognition | Best Researcher Award

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Assist. Prof. Dr. Cheng Cheng | Emotion and Cognition | Best Researcher Award

Dr. Cheng Cheng is a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, with a Ph.D. in Computer Science from Dalian University of Technology (2024). Her interdisciplinary expertise lies in affective computing, neural signal processing, and mental health assessment using EEG data. She leads research integrating spatiotemporal and multimodal analysis for emotion recognition and depression detection. Dr. Cheng is recognized for proposing the SASD-MCL model to enhance EEG-based emotion recognition in scenarios with limited annotations. Her publications appear in reputed journals in machine learning and neuroscience. As a committed educator and lab leader, she mentors students, oversees collaborative projects, and contributes to knowledge dissemination across AI and cognitive science domains. She actively participates in academic forums and maintains professional memberships in cognitive computing and brain research societies. Dr. Cheng’s work stands at the intersection of artificial intelligence and human emotion, contributing to advancements in mental health technologies.

Profile

🎓 Education

Dr. Cheng Cheng received her Ph.D. in Computer Science from Dalian University of Technology in 2024, where her dissertation focused on EEG-based affective computing and mental health applications. During her doctoral studies, she specialized in deep learning, neural signal processing, and cross-domain adaptation models. Her academic training included a rigorous foundation in artificial intelligence, biomedical data analysis, and advanced computational neuroscience. Prior to her Ph.D., she completed her undergraduate and postgraduate studies in Computer Science with distinction, building a strong base in algorithm development and machine learning. Her education journey combined theoretical learning with practical projects and industry collaborations, preparing her for cross-disciplinary research in cognitive science. Through coursework, research assistantships, and conference participations, she gained expertise in cutting-edge neural decoding techniques, emotion modeling, and multimodal data fusion. Dr. Cheng continues to apply her educational background to develop innovative models that bridge brain signal processing and artificial intelligence.

🧪 Experience

Dr. Cheng Cheng is currently serving as a lecturer at the Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, where she also leads a neuroscience and AI-integrated research lab. She has experience supervising postgraduate students, conducting collaborative research, and publishing peer-reviewed work in SCI-indexed journals. Her professional journey includes the development of the SASD-MCL framework for EEG-based emotion recognition and participation in multi-domain research initiatives aimed at improving mental health diagnostics. As a lab leader, she promotes interdisciplinary cooperation between neuroscientists and machine learning experts. Dr. Cheng has participated in national and university-funded research projects and regularly presents at conferences focused on cognitive computing and brain signal interpretation. Her previous roles include research assistantships during her doctoral program, where she refined her expertise in neural signal processing and cross-subject learning models. With a deep interest in innovation, she continues to enhance the accuracy and generalizability of emotion detection systems.

🏅 Awards and Honors

Dr. Cheng Cheng has been recognized for her outstanding contributions to affective computing and brain–AI interfacing. Her model SASD-MCL received academic commendation for significantly improving cross-subject EEG-based emotion recognition, achieving a 5.93% and 5.32% accuracy gain on SEED and SEED-IV datasets, respectively. She has received “Best Paper Presentation” at the International Conference on Cognitive Computing and Neural Interfaces and was awarded a Research Excellence Scholarship during her Ph.D. tenure. Her collaborative work on mental health diagnostics has been featured in top-tier journals, earning her invitations to join editorial boards and review panels. She is an active member of IEEE, the Chinese Association for Artificial Intelligence, and other neuroscience societies. Her leadership in mentoring young researchers and spearheading interdisciplinary projects has also been acknowledged by her institution. Nominated for the “Best Researcher Award,” Dr. Cheng continues to set benchmarks in neural data modeling, emotion AI, and computational mental health technologies.

🔬 Research Focus

Dr. Cheng Cheng’s primary research focus lies in affective computing, neural signal processing, and mental health assessment using EEG data. She integrates deep learning techniques with brain-computer interface (BCI) methodologies to improve the reliability and scalability of emotion recognition systems. Her SASD-MCL model, based on semi-supervised alignment and contrastive learning, addresses key challenges in cross-subject variability and label scarcity. By leveraging spatiotemporal features and multimodal EEG representations, she advances personalized and generalizable emotion detection systems. Her work also explores multi-domain adaptation and knowledge transfer in biomedical signal classification, enhancing robustness under limited supervision. Dr. Cheng’s research bridges neuroscience and artificial intelligence, contributing to innovations in automated mental health screening tools. She is currently involved in projects involving real-time emotion feedback and cognitive state monitoring using portable EEG devices. Her scientific vision aims to foster machine empathy through intelligent systems capable of understanding and responding to human emotions with clinical and social applications.

✅ Conclusion

Dr. Cheng Cheng exemplifies excellence in interdisciplinary research at the intersection of neuroscience and artificial intelligence. Her pioneering contributions to EEG-based emotion recognition and mental health assessment models offer robust, scalable solutions in affective computing. With a strong academic foundation, impactful innovations, and dedicated mentorship, she stands out as a deserving nominee for the Best Researcher Award.

Publications

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Álvaro García Martín es Profesor Titular en la Universidad Autónoma de Madrid, especializado en visión por computadora y análisis de video. 🎓 Obtuvo su título de Ingeniero de Telecomunicación en 2007, su Máster en Ingeniería Informática y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad Autónoma de Madrid. 🏫 Ha trabajado en detección de personas, seguimiento de objetos y reconocimiento de eventos, con más de 22 artículos en revistas indexadas y 28 en congresos. 📝 Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su investigación ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, análisis de secuencias de video y procesamiento de señales multimedia. 📹 Ha sido reconocido con prestigiosos premios y ha participado en múltiples proyectos europeos de innovación tecnológica. 🚀

Profile

Education 🎓

🎓 Ingeniero de Telecomunicación por la Universidad Autónoma de Madrid (2007). 🎓 Máster en Ingeniería Informática y Telecomunicaciones con especialización en Tratamiento de Señales Multimedia en la Universidad Autónoma de Madrid (2009). 🎓 Doctor en Ingeniería Informática y Telecomunicación por la Universidad Autónoma de Madrid (2013). Su formación ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). 🌍 Durante su doctorado, recibió la beca FPI-UAM para la realización de su investigación. Su sólida formación académica le ha permitido contribuir significativamente al campo del análisis de video y visión por computadora, consolidándose como un experto en la detección, seguimiento y reconocimiento de eventos en secuencias de video. 📹

Experience 👨‍🏫

🔬 Se unió al grupo VPU-Lab en la Universidad Autónoma de Madrid en 2007. 📡 De 2008 a 2012, fue becario de investigación (FPI-UAM). 🎓 Entre 2012 y 2014, trabajó como Profesor Ayudante. 👨‍🏫 De 2014 a 2019, fue Profesor Ayudante Doctor. 📚 De 2019 a 2023, ocupó el cargo de Profesor Contratado Doctor. 🏛️ Desde septiembre de 2023, es Profesor Titular en la Universidad Autónoma de Madrid. 🏆 Ha participado en múltiples proyectos europeos sobre videovigilancia, transmisión de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. 🚀 Ha realizado estancias de investigación en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su experiencia docente abarca asignaturas en Ingeniería de Telecomunicaciones, Ingeniería Informática e Ingeniería Biomédica.

Research Interests 🔬

🎯 Su investigación se centra en la visión por computadora, el análisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. 📹 Especialista en detección de personas, seguimiento de objetos y reconocimiento de eventos en video. 🧠 Desarrolla algoritmos de aprendizaje profundo y visión artificial para mejorar la seguridad y automatización en ciudades inteligentes. 🏙️ Ha trabajado en proyectos sobre videovigilancia, transmisión multimedia y detección de anomalías en video. 🔬 Su investigación incluye procesamiento de imágenes, análisis semántico y redes neuronales profundas. 🚀 Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. 🌍 Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidándose como un referente en el campo de la visión por computadora. 📜

Awards & Recognitions 🏅

🥇 Medalla “Juan López de Peñalver” 2017, otorgada por la Real Academia de Ingeniería. 📜 Reconocimiento por su contribución a la ingeniería española en el campo de la visión por computadora y análisis de video. 🏛️ Ha recibido financiación para múltiples proyectos de investigación europeos y nacionales. 🔬 Ha participado en iniciativas de innovación en videovigilancia y análisis de video para seguridad. 🚀 Sus contribuciones han sido publicadas en las principales conferencias y revistas científicas del área. 📚 Su trabajo ha sido citado más de 4500 veces y cuenta con un índice h de 16 en Google Scholar. 📊

Publications 

1. Rafael Martín-Nieto, Álvaro García-Martín, Alexander G. Hauptmann, and Jose. M.
Martínez: “Automatic vacant parking places management system using multicamera
vehicle detection”. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martín-Nieto, Álvaro García-Martín, Jose. M. Martínez, and Juan C. SanMiguel:
“Enhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximization”. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Álvaro García-Martín, Juan C. SanMiguel and Jose. M. Martínez: “Coarse-to-fine adaptive
people detection for video sequences by maximizing mutual information”. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós and Álvaro GarcíaMartín: “Semantic-Aware Scene Recognition”. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Álvaro García-Martín, Marcos Escudero Viñolo, Jose M. Martinez, Jesus
Bescós, Jesus Peñuela, Juan Carlos Martinez, Gonzalo Alvis: “Towards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city maps”. Waste Management, June 2022.

6. Javier Montalvo, Álvaro García-Martín, Jesus Bescós: “Exploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Álvaro García-Martín, Jose M. Martinez, Jesus Bescós: “Enhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Information”. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-Viñolo and Alvaro GarciaMartin: “On exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic data”. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Marcos EscuderoViñolo: “Spacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensus”. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Álvaro García-Martín, José M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo Pérez-Villar, Álvaro García-Martín, Jesús Bescós, Juan C. SanMiguel:
“Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesis”. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira, Álvaro García-Martín:
“Improved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuning”. Applied Intelligence. Aug 2024.

13. Javier Galán, Miguel González, Paula Moral, Álvaro García-Martín, Jose M. Martinez:
“Transforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identification”. Waste Management, Feb 2025.

Chunyu Liu | Cognitive Computing | Best Researcher Award

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Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

Grazia Ragone | Human-Computer Interaction | Best Researcher Award

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Dr. Grazia Ragone | Human-Computer Interaction | Best Researcher Award

🔬 Grazia Ragone is a researcher in Human-Computer Interaction (HCI) with a focus on autism and interactive systems. 🏫 She earned her PhD from the University of Sussex, UK, where she investigated social motor synchrony in autistic children through motion capture and sonification. 🎼 With a background in psychology, developmental science, and music therapy, she integrates interdisciplinary methods into assistive technology. 💻 She has extensive teaching experience in research methods, cognitive science, and HCI at the University of Sussex. 🏆 Her research has been recognized with multiple international awards, including Microsoft Research’s Best Student Research Competition. 🌍 She actively contributes as a reviewer and associate chair for HCI conferences and journals. 📖 Her work bridges psychology, technology, and education, aiming to enhance accessibility and interaction for neurodiverse individuals.

Profile

Education 🎓

She completed her PhD in 2023 at the University of Sussex, UK, where her research focused on autism, motion capture, and social motor synchrony. Prior to this, she earned an MSc in Psychological Methods from the University of Sussex in 2018, with a focus on autism and interactional features. She also holds an MPhil in Developmental Psychology from London Metropolitan University (2015), specializing in child development and interaction. In 2014, she completed her BSc in Developmental Psychology at London Metropolitan University, studying early cognitive and social development. She further enriched her expertise with a Master’s in Music & Art Therapy from Tor Vergata University in Rome (2006), where she focused on therapeutic interventions for individuals with special needs. Her academic journey began with a BA in Humanities from the University of Pavia, Italy (2004), where she studied philosophy, linguistics, and cultural studies.

Experience 👨‍🏫

From 2019 to 2023, she worked as a Teaching Assistant at the University of Sussex, UK, where she taught Human-Computer Interaction (HCI), research methods, and professional skills. Prior to this, she served as a Research Assistant at the University of Sussex (2016-2018), focusing on technology designed for neurodiverse children. From 2014 to 2016, she conducted research on autism and interactive environments at London Metropolitan University. Earlier in her career, she was a Research Assistant at CNR-ISTI Pisa, Italy (2008-2014), where she contributed to the development of assistive software for autistic children. Her experience also includes working as a Music Therapist for the Rome City Council (2005-2010), providing therapeutic interventions for autistic children. Additionally, from 2010 to 2019, she worked as a Trainer and Consultant, conducting workshops and training programs for professionals in the field of autism.

Research Interests 🔬

Her research focuses on Human-Computer Interaction (HCI) and autism, developing interactive systems to support neurodiverse individuals. She explores the role of music and sonification in enhancing motor and social skills through auditory feedback. Her work also includes investigating social motor synchrony using motion capture technology. She designs AI-powered assistive technology to support autistic children and applies user-centered design principles to create accessible interfaces for individuals with special needs.

Awards & Recognitions 🏅

She has received several prestigious awards and honors for her contributions to autism research and assistive technology. In 2021, she was awarded the Best Student Research Award by Microsoft Research at the ASSETS Conference. Her work was also recognized with the Best Work in Progress Award at the IDC Conference on autism research in 2020. In 2013, she received the Horizon Research Award from London Metropolitan University for outstanding research. Her contributions to autism research earned her a Massachusetts Senate Citation in 2012, and in 2011, she was honored with the Rotary Club Research Award from CNR Pisa for excellence in autism studies.

Publications 📚

  •  Supporting and understanding autistic children’s non-verbal interactions through OSMoSIS, a motion-based sonic system
    International Journal of Child-Computer Interaction
    2025-02 | Journal article
    DOI: 10.1016/j.ijcci.2025.100726
    CONTRIBUTORS: Grazia Ragone; Judith Good; Kate Howland

  • Child-Centered AI for Empowering Creative and Inclusive Learning Experiences

    Proceedings of ACM Interaction Design and Children Conference: Inclusive Happiness, IDC 2024
    2024 | Conference paper
    DOI: 10.1145/3628516.3661157

    EID:

    2-s2.0-85197894406

    Part ofISBN: 9798400704420
    CONTRIBUTORS: Ragone, G.; Ali, S.A.; Esposito, A.; Good, J.; Howland, K.; Presicce, C.

  • Designing Safe and Engaging AI Experiences for Children: Towards the Definition of Best Practices in UI/UX Design

    arXiv
    2024 | Other
    DOI: 10.48550/arXiv.2404.14218

    EID:

    2-s2.0-85192517180

    Part of ISSN: 23318422
    CONTRIBUTORS: Ragone, G.; Buono, P.; Lanzilotti,

Gerardo Fernandez | Eye tracking | Excellence in Innovation

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Dr. Gerardo Fernandez | Eye tracking | Excellence in Innovation

Gerardo Abel Fernández 🇦🇷, born on October 29, 1976, in Bahía Blanca, Argentina, is a researcher specializing in neuroscience and cognitive science 🧠. He is a professor and adjunct researcher at CONICET, focusing on eye movement-based biomarkers for neurodegenerative diseases 👀. His work integrates philosophy, cognitive psychology, and technology to advance Alzheimer’s diagnosis 🏥.

Profile

Education 🎓

🎓 Gerardo Abel Fernández obtained a degree in Philosophy (2003) from Universidad Nacional del Sur (UNS), Argentina, with a specialization in Logic and Epistemology. He later pursued a PhD in Philosophy (2011) at UNS, with his thesis titled “Dynamic word processing during reading: Mental strategies driving visual exploration”, earning a perfect 10/10 with special mention. His academic journey includes postdoctoral research as a fellow at AGENCIA (ANPCYT) and the DAAD Max Planck Institute in Berlin. His educational background bridges philosophy, neuroscience, and cognitive psychology, forming a solid foundation for his pioneering research in eye movement analysis and Alzheimer’s biomarkers. His expertise in cognitive science and technological innovation has led to the development of diagnostic tools for early neurodegenerative disease detection. 📚🔍🧠

Experience 👨‍🏫

💼 Dr. Gerardo Abel Fernández has extensive experience in neuroscience research and technological innovation. He served as a Professor of Audiovisual Language at UNS (2011–2013) and is currently an Adjunct Researcher at CONICET, focusing on non-endemic degenerative pathologies. He has worked as a Visiting Scholar at Heriot-Watt University and Strathclyde University (UK), contributing to the development of eye-tracking biomarkers for Alzheimer’s disease. Dr. Fernández is also a scientific reviewer for prestigious journals like PlosOne, Journal of Alzheimer’s Disease, and Neuropsychologia. As CTO of Viewmind, he leads biocognitive and functional performance measurement innovations. He has patented cognitive evaluation methods and received grants from institutions like ANPCYT and DAAD. His interdisciplinary expertise spans cognitive neuroscience, machine learning applications in diagnostics, and technological development for neurodegenerative disease assessment. 🏅🔬👁️

Research Interests 🔬

🔬 Dr. Gerardo Abel Fernández specializes in cognitive neuroscience, neurodegenerative disease biomarkers, and eye-tracking technology. His research focuses on early Alzheimer’s detection through oculomotor behavior analysis. He has developed innovative methods to study visual exploration, reading difficulties, and memory impairments in neurodegenerative conditions. His work integrates machine learning and artificial intelligence for cognitive assessment tools. As a Visiting Scholar in the UK, he contributed to developing biomarkers for Alzheimer’s disease. His patented eye-tracking system has clinical applications in detecting mild cognitive impairment and Alzheimer’s disease. He has published extensively in peer-reviewed journals, exploring predictive eye movement models and their correlation with cognitive decline. His cutting-edge research bridges philosophy, neuroscience, and technology, offering non-invasive diagnostic solutions for early-stage neurodegeneration. His ultimate goal is to revolutionize cognitive healthcare through technological innovation. 🧠👁️📊

Awards & Recognitions 🏅

🏆 Dr. Gerardo Abel Fernández has received numerous awards for his contributions to neuroscience, cognitive evaluation, and Alzheimer’s diagnostics. His eye-tracking research for Alzheimer’s detection earned the Dr. José Borda Clinical Psychiatry Prize at the 22nd International Congress of Psychiatry. He won the Novartis Innovation Award for his work on measuring cognitive performance in health and disease. As CTO of Viewmind, his team received international recognition, including the Fit4Start Luxembourg Award for health applications and the Medica Innovation Prize in Düsseldorf. His research and patented cognitive evaluation equipment have been acknowledged by ANMAT (Argentina’s National Administration of Drugs, Foods, and Medical Technology) and INPI (Argentina’s National Patent Office). Dr. Fernández’s groundbreaking innovations in neurocognitive assessments have positioned him as a leading figure in technological advancements for early Alzheimer’s detection. 🏅🧠🔬

Publications 📚

  • Oculomotor behaviors and integrative memory functions in the alzheimer’s clinical syndrome

    Journal of Alzheimer’s Disease
    2021 | Journal article
    DOI: 10.3233/JAD-201189

  • A non-invasive tool for attention-deficit disorder analysis based on gaze tracks.

    ACM International Conference Proceeding Series
    2019 | Conference paper
    DOI: 10.1145/3309772.3309777

  • Microsaccadic behavior when developing a complex dynamical activity

    Journal of Integrative Neuroscience
    2018 | Journal article
    DOI: 10.3233/JIN-170057

    EID:

    2-s2.0-85053731401

Jianbang Liu | AI-driven emotion | Best Researcher Award

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Dr. Jianbang Liu | AI-driven emotion | Best Researcher Award

JianBang Liu is a faculty member at the Xinyu University, China, where he actively contributes to both research and education. His research interests lie at the intersection of Artificial Intelligence (AI), Human-Computer Interaction (HCI), and Artificial Sentiment Analysis, with a specific focus on developing AI-driven emotion and cognition analysis. He has published extensively in international journals, significantly advancing the fields of HCI and AI. He continues to explore innovative applications of these technologies, aiming to bridge theoretical research with practical implementations.

Profile

Education

JianBang Liu obtained his Master’s degree from Qilu University of Technology (Shandong Academy of Sciences), China, in 2018. He then completed his Ph.D. at the Institute of Visual Informatics, UniversitiKebangsaan Malaysia (National University of Malaysia), specializing in Human-Computer Interaction (HCI) and Artificial Intelligence (AI).

Research Interests

Artificial Intelligence (AI), Human-Computer Interaction (HCI), AI-driven emotion and cognition analysisRe

Research Innovation

Completed/Ongoing Research Projects: State the number of research projects you have completed or are currently working on.

Citation Index: Provide information about your citation index in relevant databases such as SCI, Scopus, etc.

Consultancy/Industry Projects: Indicate the number of consultancy or industry-sponsored projects you have been involved in.

Books Published (ISBN): Specify the number of books you have published with ISBN numbers.

Patents Published/Under Process: Mention the number of patents you have published or are currently in the process of publishing.

JournalsPublished: State the number of articles you have published in indexed journals.

Editorial Appointments: If applicable, list any editorial positions you hold in journals or conferences.

Collaborations: Describe any significant collaborations you have been part of in your research career.

Professional Memberships: List memberships in professional organizations or societies relevant to your field.

Areas of Research: Specify the main areas or topics you focus on in your research work.

Books /Chapters in Books：

Local optimal Issue in Bees Algorithm: Markov Chain Analysis and Integration with Dynamic Particle Swarm Optimization Algorithm (Intelligent Engineering Optimisation with the Bees Algorithm (978-3-031-64935-6/ 978-3-031-64936-3 (eBook)))

Publication

  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Emotion assessment and application in human-computer interaction interface based on backpropagation neural network and artificial bee colony algorithm (SCI Q1)
  • Personalized Emotion Analysis Based on Fuzzy Multi-Modal Transformer Model (SCI Q2)
  • Immersive VR Learning experiences from the perspective of telepresence, emotion, and cognition(SSCI Q1)

Gilbert Giacomoni | Coupling Human | Best Researcher Award

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Dr. Gilbert Giacomoni | Coupling Human | Best Researcher Award

Profile

Education

He holds a Doctorate in Engineering & Management from Mines ParisTech (Paris Sciences & Letters University), showcasing expertise in the intersection of technology and management. Additionally, they have earned a Master’s in Research for Scientific Management Methods from Paris 9 Dauphine University, in collaboration with Mines ParisTech (Scientific Management Center) and École Polytechnique (Center for Management Research), with a focus on decision-support systems. Further expanding their interdisciplinary knowledge, they also hold a Professional Master’s in Artificial Intelligence & Ethnology from Paris 7 University (Ethnology/Semantics) and Paris 8 University (Artificial Intelligence), blending AI with human sciences for a unique analytical perspective.

Research Interests

Gilbert Giacomoni research interests span multiple interdisciplinary domains, including behavioral economics, where they explore decision-making processes and human behavior in economic contexts. Their work in sustainability and innovation focuses on key sectors such as health, food, and the environment, addressing pressing global challenges. Additionally, they specialize in strategy and organization, examining business structures, leadership, and strategic decision-making. Their expertise in information systems encompasses cutting-edge technologies like artificial intelligence (AI), data science, and machine learning, leveraging digital transformation to drive efficiency and innovation across industries.

Work experience

Dr. Gilbert Giacomoni has an extensive academic and professional background, integrating research, leadership, and industry expertise. They are a member of Paris Saclay Applied Economics (PSAE), a joint research center affiliated with France’s National Research Institute for Agriculture, Food, and Environment (INRAE). Additionally, they are associated with AgroParisTech, a part of Paris Saclay University, ranked 13th globally.

As the Head of the “Industrial Economy, Public Management, and Innovation” Research & Teaching Unit, Dr. [Name] leads studies in information systems (AI, data science, machine learning), behavioral economics, sustainability & innovation (health, food, environment), and strategy & organization.

Beyond academia, they serve as a board member of Codegaz, a humanitarian association. Their 20+ years of industry experience span operational leadership in sectors such as industry, healthcare, and services, with affiliations including Armines, Airbus Defence & Space, Louvre Group, Compagnie des Cristalleries de Baccarat, and Mutualité Fonction Publique. They also have a strong background in entrepreneurship, business creation, and innovative technologies.

Books /Chapters in Books：

– Giacomoni G. (2022), “Finance durable, bien commun et décisions d’investissement : la
dimension économique incomprise du concept fondationnel d’Utilité commune”, in Pluchart
J.-J. & Cadet I. (Dir.) (2022), Les paradoxes de la finance durable et responsable, ESKA (Eds), pp.
283-305, France. Labélisé FNEGE (RSE).
– Giacomoni G. & Sardas J.-C., (2014), “Why innovation requires new scientific foundations for
manageable identities of systems” (Part II – Chap.4), in R&D Strategy and Operations –
Innovation and IT in an International Context, T’Eni D. & Rowe F. (Eds), Palgrave MacMillan
(Publisher).
– Giacomoni G. & Jardat R., (2014), “L’innovation par l’hybridation: une hydre scientifique”, in
Pesqueux Y., Freitas Gouveia de Vasconcelos I., Simon E., L’Entreprise durable et le
changement organisationnel – L’Organisation innovatrice et durable, éditions – ems –
Management & Société, Chap.1, pp. 27-54, hors collection.

Honors and Awards

– National winner, creation of innovative technology companies, Ministry of Youth, National
Education and Research, Official Journal of 9-11-2002.
– Capital-IT Best40s Selection (10th edition, 2003): European Meeting on Financing of New
Information and Communication Technologies, 4 April.
– Master 2003 Tremplin-Entreprises (capital investissement), French Senate (prix délivré dans
l’hémicycle par le Président J.-P. Raffarin) & Essec, 8-9 Juillet.
– Prix Spécial 2005 du Ministère de l’Education Nationale, de l’Enseignement Supérieur et
de la Recherche – Ministère Délégué à la Recherche.
– Gold Medal 2005, International Jury – World Intellectuel Property Organization (WIPO) &
Swiss Federal Government, 33th International Exhibition of Inventions Geneva, April.

Publication

  • Giacomoni G. (2024), A Blind Spot in the Reframing of a Universe of Possibles: Towards a
    Suitable Model for Decision-Making Theory and A.I., Journal of Applied Mathematics and
    Physics, Scientific Research Publishing, 12, 2172-2189.
  • Giacomoni G. (2023), Participatory financing of sustainable entrepreneurship : A postmodern
    Markowitz portfolio theory, Management & Sciences Sociales, n°35, Diversité des modèles
    d’affaires en Afrique.
  • Giacomoni G., (2023), The need for products interchangeability: an unsolved problem of
    semantic conflicts no Product Definition System can support perfectly, Journal of The
    Knowledge Economy, Springer, vol. 36, n°2.
  • Cuénoud T., Giacomoni G., Dang R. & Houanti L’H. (2022), Influence of Geography in the
    Crowdfunding of a Local Microbrewery, Innovations – Journal of Innovation Economics &
    Management, Special issue “Platforms, communities and ecosystems in a digital age”, De
    Boeck Supérieur (Eds).
  • Giacomoni G. (2021), Towards a General Framework for (early-stage of) Innovation Shaped
    with A.I. to Create and Transform Market Offerings, European Management Review (Wiley
    Online Library), Vol. 19, n° 1, pp. 107-122.