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.

Dingming Wu | Computer Science | Best Researcher Award

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Dr. Dingming Wu | Computer Science | Best Researcher Award

 

Profile

  • scopus

Education

He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010.

Work experience

He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback.

Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data.

Publication

  • [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of
    industrial chain information and deep learning for stock prediction[J]. Expert Systems with
    Applications, 2022, 191(7):116257.
    [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme
    learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock
    prediction[J]. Expert Systems with Applications, 2022, 207(24):118006.
    [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on
    k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022,
    252(18):109358.
    [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling
    method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162.
    [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme
    learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021,
    23(4):440.
    Papers to be published:
    [6] Wavelet transform in conjunction with temporal convolutional networks for time series
    prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole
    Author.
    [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive
    Neurodynamics; Status: accept; Position: First Author.
    [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor
    Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL
    INTELLIGENCE; Status: under review; Position: First Author.
    [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal
    Processing and Control; Status: Revise; Position: First Corresponding Author.
    [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks
    EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems;
    Status: under review; Position: Co-First Author.
    [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space.
    Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI);
    Status: under review; Position: Second Corresponding Author.
    [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal:
    EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second
    Corresponding Author.
    [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG
    Decoding. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: Second Corresponding Author.
    [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference:
    International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review;
    Position: Second Corresponding Author.
    [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion
    Recognition. Computer conference: International Joint Conference on Artificial Intelligence
    2025 (IJCAI); Status: under review; Position: First Author.

Ling Mei | Cognitive Science | Best Researcher Award

Posted on by sciencefather

Dr. Ling Mei | Cognitive Science | Best Researcher Award

Doctorate at Wuhan University of Science and Technology, China

Dr. Ling Mei is an accomplished researcher in artificial intelligence and cognitive science, with a robust academic and professional background. He holds a Ph.D. in Engineering from Sun Yat-sen University, one of China’s top universities, and completed a prestigious visiting scholar program at the University of British Columbia (UBC). Currently serving as a tenured faculty and master’s supervisor, Dr. Mei has published 16 papers, including 7 in SCI-indexed journals, contributed to nine books, and has three national invention patents granted. Recognized as a Provincial Research Talent of China in 2024, he work integrates advanced computational models with societal needs, such as urban planning and public safety. Dr. Mei has collaborated internationally with top-tier institutions like UBC and Carnegie Mellon University, cementing he reputation as a leader in he field.

Profile

Google Scholar

Orcid

Education 🎓

Dr. Mei earned he Ph.D. in Engineering from Sun Yat-sen University in 2021, a prestigious institution ranked among China’s top 10 universities. He academic journey also includes a year-long visiting scholar program at the Department of Computer Science, UBC, as part of the National Outstanding Young Researchers Program. This international exposure provided he with cutting-edge knowledge and interdisciplinary skills, enabling he to excel in artificial intelligence and cognitive science.

Work Experience 💼

Currently, Dr. Mei is a tenured faculty member and master’s supervisor at a leading Chinese university. He experience includes overseeing multiple research projects, consulting on seven industry-sponsored projects, and serving as a reviewer for the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He has also been instrumental in fostering international collaborations with institutions like UBC and CMU, contributing to impactful research publications and patents.

Awards and Honors

In 2024, Dr. Mei was recognized as a Provincial Research Talent of China, highlighting he exceptional contributions to science and technology. He has also earned accolades through he impactful patents and high-quality publications.

Research Interests

Dr. Mei’s research focuses on artificial intelligence, pedestrian trajectory prediction, and public safety strategies. He innovations include the LSN-GTDA framework, which integrates behavioral and stochastic factors for better uncertainty management. He interdisciplinary approach bridges cognitive science, computational models, and societal applications, ensuring he work’s relevance and impact.

Research Skills

Dr. Mei possesses advanced skills in AI modeling, thermal diffusion processes, and signal and system theory. He expertise includes patent development, SCI journal publications, and interdisciplinary collaborations. He is adept at integrating computational techniques with practical applications, as seen in he trajectory prediction research.

📚 Publications

Crowd Density Estimation via Global Crowd Collectiveness Metric

  • Journal: Drones
  • Date: 2024-10-28
  • DOI: 10.3390/drones8110616
  • Contributors: Ling Mei, Mingyu Yu, Lvxiang Jia, Mingyu Fu

More Quickly-RRT: Improved Quick Rapidly-Exploring Random Tree Star Algorithm Based on Optimized Sampling Point with Better Initial Solution and Convergence Rate*

  • Journal: Engineering Applications of Artificial Intelligence
  • Date: 2024-07
  • DOI: 10.1016/j.engappai.2024.108246
  • Contributors: Xining Cui, Caiqi Wang, Yi Xiong, Ling Mei, Shiqian Wu

Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

  • Journal: Remote Sensing
  • Date: 2023-02-09
  • DOI: 10.3390/rs15040953
  • Contributors: Ling Mei, Yizhuo He, Farnoosh Fishani, Yaowen Yu, Lijun Zhang, Helge Rhodin

Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

  • Journal: IEEE Transactions on Circuits and Systems for Video Technology
  • Date: 2020-02
  • DOI: 10.1109/TCSVT.2019.2890861
  • Contributors: Ling Mei, Jianhuang Lai, Xiaohua Xie, Junyong Zhu, Jun Chen

Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

  • Type: Book Chapter
  • Year: 2018
  • DOI: 10.1007/978-3-030-03335-4_8
  • Contributors: Xiao Liu, Ling Mei, Dakun Yang, Jianhuang Lai, Xiaohua Xie

Conclusion 

Dr. Ling Mei is a strong contender for the Best Researcher Award due to he robust academic background, impactful research, and significant contributions to AI and cognitive science. To further enhance he candidacy, increasing citation influence and emphasizing community impact would solidify he position as an exemplary researcher deserving of recognition. 🌟