Humaun Kabir | Computer Science and Engineering | Editorial Board Member

Published on by Cognitive Scientist Awards

Md. Humaun Kabir | Computer Science and Engineering | Editorial Board Member

Bangamata Sheikh Fojilatunnesa Mujib Science & Technology University | Bangladesh

Md. Humaun Kabir is an accomplished academic in computer science and engineering whose work bridges advanced computing and biomedical intelligence. He began his academic journey in applied physics and electronic engineering at a leading national university before advancing into graduate study in the same field and later joining a prestigious Japanese institution as a doctoral research fellow. His professional experience includes serving as an assistant professor on study leave at Jamalpur Science and Technology University, where he previously held key administrative roles such as department chair and additional director of the ICT cell. His research interests span biomedical signal and image processing, bioinformatics, brain–computer interfaces, machine learning, and deep learning, with a strong record of publications in national and international venues. He possesses skills in algorithm development, data modeling, neural network design, statistical learning, digital imaging techniques, and computational intelligence. His contributions have earned recognition through academic responsibilities, research achievements, and active involvement in scientific communities. He continues to collaborate globally, contributing to interdisciplinary advancements that support healthcare analytics and intelligent systems. Overall, he is a committed researcher and educator whose work reflects dedication to scientific progress, innovation, and the development of impactful computational technologies.

Profile: Googlescholar

Featured Publications

Islam, M. R., Mojumder, M. R. H., Moshwan, R., Jannatul Islam, A. S. M., Islam, M. A., Rahman, M. S., & Kabir, M. H. (2022). Strain-Driven Optical, Electronic, and Mechanical Properties of Inorganic Halide Perovskite CsGeBr₃. ECS Journal of Solid State Science and Technology, 11(3), 033001

Kabir, M. H., Mahmood, S., Al Shiam, A., Musa Miah, A. S., Shin, J., & Molla, M. K. I. (2023). Investigating Feature Selection Techniques to Enhance the Performance of EEG-Based Motor Imagery Tasks Classification. Mathematics, 11(8), 1921.

Shin, J., Musa Miah, A. S., Kabir, M. H., Rahim, M. A., & Al Shiam, A. (2024). A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities. IEEE Access.

 

HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

Published on by Cognitive Scientist Awards

Prof. HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

Abdelmalek Essaadi University | Morocco

Professor Younes Hmimsa is a distinguished Moroccan academic and researcher serving as a Full Professor at the Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University. His academic background spans animal biology, plant biotechnology, ecology, and wealth management, reflecting a multidisciplinary approach to environmental and agricultural sciences. With advanced degrees from the University of Tetouan and the University of Alicante, he has developed extensive expertise in plant biology, agroecology, and biodiversity conservation. Professionally, he has been a key figure in multiple international research programs such as PRIMA, ARIMNET, and EVOlea, focusing on agrobiodiversity, climate resilience, and sustainable agricultural systems. His research interests include plant phenology, genetic diversity, agroecosystem sustainability, and the socio-ecological dynamics of traditional farming systems in Mediterranean regions. He possesses strong research skills in agromorphological characterization, environmental data modeling, and interdisciplinary collaboration. Professor Hmimsa has coordinated numerous international conferences, seminars, and research networks that bridge scientific innovation and rural development. His scholarly achievements are complemented by prestigious roles as a reviewer, evaluator, and collaborator with global research institutions. A recipient of several academic honors and author of influential publications, he continues to advance sustainable agricultural practices and ecological research in Morocco and beyond, inspiring both scientific and community engagement.

Profile: ORCID

Featured Publications

Olubunmi Kayode AYANWOYE | Generative Artificial Intelligence | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Olubunmi Kayode AYANWOYE | Generative Artificial Intelligence| Best Researcher Award

Federal University Oye-Ekiti | Nigeria

Dr. Olubunmi Kayode Ayanwoye is a distinguished Nigerian scholar and educator whose academic and professional pursuits are rooted in mathematics education, pedagogy, and curriculum innovation. He holds advanced degrees in mathematics education from the University of Ibadan and the University of Ado-Ekiti, complemented by a diploma in computer applications, reflecting his strong interdisciplinary foundation. His career spans teaching and lecturing roles at leading Nigerian institutions, including the Oyo State Teaching Service Commission, Emmanuel Alayande College of Education, and the Federal University Oye-Ekiti, where he currently serves as a lecturer. Dr. Ayanwoye’s research interests encompass general education, mathematics pedagogy, gender issues in learning, curriculum design, and research analytics, with a particular focus on integrating technology and artificial intelligence in education. His research skills include meta-analysis, systematic review, statistical interpretation, and instructional design. A regular participant and presenter at national and international academic conferences, he contributes to advancing educational methodologies and digital readiness in teacher education. Throughout his career, Dr. Ayanwoye has received recognition for his academic excellence, leadership, and commitment to innovative teaching practices. Dedicated to fostering critical thinking and inclusivity in education, he continues to inspire future educators through impactful research, mentorship, and a steadfast dedication to academic and professional excellence.

Profile: ORCID

Featured Publications

Ayanwoye, O. K. (2025). Aims and objectives of teaching mathematics as a school subject. In The Methodology of Science Teaching.

Falebita, O. S., Abah, J. A., Asanre, A. A., Abiodun, T. O., Ayanwale, M. A., & Ayanwoye, O. K. (2025, October). Determinants of chatbot brand trust in the adoption of generative artificial intelligence in higher education. Education Sciences, 15(10), Article 1389.

Ayanwoye, O. K. (2025, October 3). Influence of artificial intelligence tool perceptions on mathematics undergraduates’ academic engagement: Role of attitudes and usage intentions. International Journal of Didactic Mathematics in Distance Education, 2(2), 207–224.

Ayanwoye, O. K. (2025, September 15). Assessment of media capture and ethical challenges in reporting corruption in Nigeria. Journal of African Films and Diaspora Studies (JAFDIS), 8(3).

Ezgi Δ°lhan | User experience design | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Ezgi Δ°lhan | 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

πŸŽ“ 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-RΔ±dvan 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.

πŸ§ͺ 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 AtΔ±lΔ±m 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.

πŸ… 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.

πŸ”¬ 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.

βœ… 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

Published on by Cognitive Scientist Awards

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.

Β Publications

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

    Applied Sciences
    2022-03 |Β Journal articleΒ |Β Author
    DOI:Β 10.3390/app12063154
    CONTRIBUTORS:Β Chan-Uk Yeom;Β 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 |Β Journal articleΒ |Β Author
    DOI:Β 10.3390/app10238495
    CONTRIBUTORS:Β Chan-Uk Yeom;Β Keun-Chang Kwak

Abdeldjalil Ouahabi | Face recognition | Best Paper Award

Published on by Cognitive Scientist Awards

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. CΓ©dric 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

πŸŽ“ 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 exposureβ€”highlighted by academic appointments in the United States (Bucknell University) and the Middle East (Qatar University)β€”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.

πŸ§ͺ 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. CΓ©dric 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.

πŸ… 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. CΓ©dric 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.

πŸ”¬ 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. CΓ©dric 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.

βœ… 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 |Β Journal article
    DOI:Β 10.1016/j.bspc.2025.108072
    CONTRIBUTORS:Β Arezki Larbi;Β Mansour Abed;Β Jaime S. Cardoso;Β Abdeljalil Ouahabi

  • Advanced genetic image encryption algorithms for intelligent transport systems

    Computers and Electrical Engineering
    2025-04 |Β Journal article
    DOI:Β 10.1016/j.compeleceng.2025.110162
    CONTRIBUTORS:Β Ismahane Souici;Β Meriama Mahamdioua;Β SΓ©bastien Jacques;Β 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 |Β Journal article
    DOI:Β 10.3390/microorganisms11102608
    CONTRIBUTORS:Β Fatma Messaoudene;Β Slimane Boukraa;Β Said Chaouki Boubidi;Β Ahlem Guerzou;Β Abdeldjalil Ouahabi

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

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

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

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

Cheng Cheng | Emotion and Cognition | Best Researcher Award

Published on by Cognitive Scientist Awards

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

πŸŽ“ 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.

πŸ§ͺ 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.

πŸ… 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 β€œBest 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 β€œBest Researcher Award,” Dr. Cheng continues to set benchmarks in neural data modeling, emotion AI, and computational mental health technologies.

πŸ”¬ 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.

βœ… 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

Published on by Cognitive Scientist Awards

Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Álvaro GarcΓ­a MartΓ­n es Profesor Titular en la Universidad AutΓ³noma de Madrid, especializado en visiΓ³n por computadora y anΓ‘lisis de video. πŸŽ“ Obtuvo su tΓ­tulo de Ingeniero de TelecomunicaciΓ³n en 2007, su MΓ‘ster en IngenierΓ­a InformΓ‘tica y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad AutΓ³noma de Madrid. 🏫 Ha trabajado en detecciΓ³n de personas, seguimiento de objetos y reconocimiento de eventos, con mΓ‘s de 22 artΓ­culos en revistas indexadas y 28 en congresos. πŸ“ Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su investigaciΓ³n ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, anΓ‘lisis de secuencias de video y procesamiento de seΓ±ales multimedia. πŸ“Ή Ha sido reconocido con prestigiosos premios y ha participado en mΓΊltiples proyectos europeos de innovaciΓ³n tecnolΓ³gica. πŸš€

Profile

Education πŸŽ“

πŸŽ“ Ingeniero de TelecomunicaciΓ³n por la Universidad AutΓ³noma de Madrid (2007). πŸŽ“ MΓ‘ster en IngenierΓ­a InformΓ‘tica y Telecomunicaciones con especializaciΓ³n en Tratamiento de SeΓ±ales Multimedia en la Universidad AutΓ³noma de Madrid (2009). πŸŽ“ Doctor en IngenierΓ­a InformΓ‘tica y TelecomunicaciΓ³n por la Universidad AutΓ³noma de Madrid (2013). Su formaciΓ³n 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, recibiΓ³ la beca FPI-UAM para la realizaciΓ³n de su investigaciΓ³n. Su sΓ³lida formaciΓ³n acadΓ©mica le ha permitido contribuir significativamente al campo del anΓ‘lisis de video y visiΓ³n por computadora, consolidΓ‘ndose como un experto en la detecciΓ³n, seguimiento y reconocimiento de eventos en secuencias de video. πŸ“Ή

Experience πŸ‘¨β€πŸ«

πŸ”¬ Se uniΓ³ al grupo VPU-Lab en la Universidad AutΓ³noma de Madrid en 2007. πŸ“‘ De 2008 a 2012, fue becario de investigaciΓ³n (FPI-UAM). πŸŽ“ Entre 2012 y 2014, trabajΓ³ como Profesor Ayudante. πŸ‘¨β€πŸ« De 2014 a 2019, fue Profesor Ayudante Doctor. πŸ“š De 2019 a 2023, ocupΓ³ el cargo de Profesor Contratado Doctor. πŸ›οΈ Desde septiembre de 2023, es Profesor Titular en la Universidad AutΓ³noma de Madrid. πŸ† Ha participado en mΓΊltiples proyectos europeos sobre videovigilancia, transmisiΓ³n de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. πŸš€ Ha realizado estancias de investigaciΓ³n en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. 🌍 Su experiencia docente abarca asignaturas en IngenierΓ­a de Telecomunicaciones, IngenierΓ­a InformΓ‘tica e IngenierΓ­a BiomΓ©dica.

Research Interests πŸ”¬

🎯 Su investigaciΓ³n se centra en la visiΓ³n por computadora, el anΓ‘lisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. πŸ“Ή Especialista en detecciΓ³n de personas, seguimiento de objetos y reconocimiento de eventos en video. 🧠 Desarrolla algoritmos de aprendizaje profundo y visiΓ³n artificial para mejorar la seguridad y automatizaciΓ³n en ciudades inteligentes. πŸ™οΈ Ha trabajado en proyectos sobre videovigilancia, transmisiΓ³n multimedia y detecciΓ³n de anomalΓ­as en video. πŸ”¬ Su investigaciΓ³n incluye procesamiento de imΓ‘genes, anΓ‘lisis semΓ‘ntico y redes neuronales profundas. πŸš€ 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, consolidΓ‘ndose como un referente en el campo de la visiΓ³n por computadora. πŸ“œ

Awards & Recognitions πŸ…

πŸ₯‡ Medalla “Juan LΓ³pez de PeΓ±alver” 2017, otorgada por la Real Academia de IngenierΓ­a. πŸ“œ Reconocimiento por su contribuciΓ³n a la ingenierΓ­a espaΓ±ola en el campo de la visiΓ³n por computadora y anΓ‘lisis de video. πŸ›οΈ Ha recibido financiaciΓ³n para mΓΊltiples proyectos de investigaciΓ³n europeos y nacionales. πŸ”¬ Ha participado en iniciativas de innovaciΓ³n en videovigilancia y anΓ‘lisis de video para seguridad. πŸš€ Sus contribuciones han sido publicadas en las principales conferencias y revistas cientΓ­ficas del Γ‘rea. πŸ“š Su trabajo ha sido citado mΓ‘s de 4500 veces y cuenta con un Γ­ndice h de 16 en Google Scholar. πŸ“Š

PublicationsΒ 

1. Rafael Martín-Nieto, Álvaro García-Martín, Alexander G. Hauptmann, and Jose. M.
MartΓ­nez: β€œAutomatic 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:
β€œEnhancing 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 GarcΓ­a-MartΓ­n, Juan C. SanMiguel and Jose. M. MartΓ­nez: β€œCoarse-to-fine adaptive
people detection for video sequences by maximizing mutual information”. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro LΓ³pez-Cifuentes, Marcos Escudero-ViΓ±olo, JesΓΊs BescΓ³s and Álvaro GarcΓ­aMartΓ­n: β€œSemantic-Aware Scene Recognition”. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Álvaro García-Martín, Marcos Escudero Viñolo, Jose M. Martinez, Jesus
BescΓ³s, Jesus PeΓ±uela, Juan Carlos Martinez, Gonzalo Alvis: β€œTowards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city maps”. Waste Management, June 2022.

6. Javier Montalvo, Álvaro GarcΓ­a-MartΓ­n, Jesus BescΓ³s: β€œExploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environments”. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Álvaro GarcΓ­a-MartΓ­n, Jose M. Martinez, Jesus BescΓ³s: β€œEnhancing 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-ViΓ±olo and Alvaro GarciaMartin: β€œOn exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic data”. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo PΓ©rez-Villar, Álvaro GarcΓ­a-MartΓ­n, JesΓΊs BescΓ³s, Marcos EscuderoViΓ±olo: β€œSpacecraft 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 GarcΓ­a-MartΓ­n, JosΓ© 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:
β€œTest-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:
β€œImproved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuning”. Applied Intelligence. Aug 2024.

13. Javier GalÑn, Miguel GonzÑlez, Paula Moral, Álvaro García-Martín, Jose M. Martinez:
β€œTransforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identification”. Waste Management, Feb 2025.

Chunyu Liu | Cognitive Computing | Best Researcher Award

Published on by Cognitive Scientist Awards

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. πŸ“š 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. πŸŽ“ She completed postdoctoral training at Peking University. πŸ”¬ 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. πŸ“ 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. πŸš€

Profile

Education πŸŽ“

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. βž• She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. πŸ”’ Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. πŸ–₯️ 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. πŸ”¬ Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. πŸ“Š Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience πŸ‘¨β€πŸ«

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. πŸŽ“ 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. πŸ” Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). πŸ“‘ She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. πŸ”¬ Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. πŸš€

Research Interests πŸ”¬

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. πŸ“‘ 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. πŸ€– By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. πŸ† Her research also inspires the development of new AI theories and algorithms based on principles of brain function. πŸ“Š She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. πŸš€ Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. πŸ…

 

Awards & Recognitions πŸ…

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. πŸ… She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. πŸŽ–οΈ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. πŸ“œ 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. πŸ“ She has also been honored with research grants and funding for AI-driven cognitive studies. πŸ”¬ 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. πŸš€

Publications πŸ“š