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

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

Huifang Wang | Neuroscience | Best Researcher Award

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 Dr. Huifang Wang | Neuroscience | Best Researcher Award

Huifang Elizabeth Wang is a leading research engineer at INSERM U1106, Aix-Marseille University, France, specializing in computational neuroscience. Her career bridges robotics, brain modeling, and clinical neuroscience, with a primary focus on personalized brain simulations for neurological and psychiatric conditions, notably epilepsy. With over a decade of postdoctoral research across top French and Italian institutes, she has contributed to projects integrating physics-based modeling, large-scale neural dynamics, and effective connectivity. Her academic journey started in robotics and control theory in China and evolved into advanced brain modeling in Europe. She collaborates with renowned neuroscientists like Dr. Viktor Jirsa and has authored numerous high-impact publications in Science Translational Medicine, The Lancet Neurology, and NeuroImage. As PI and co-leader in several EU and national projects, she aims to bridge basic brain science with clinical translation. Wang’s work is pivotal in creating virtual brain twins to personalize epilepsy surgery and psychiatric interventions.

Profile

🎓 Education

Huifang Elizabeth Wang obtained her Ph.D. in Pattern Recognition and Intelligent Systems from Beijing University of Technology in 2008, focusing on optimization algorithms for robotic motion under Prof. Chen Yangzhou. She earned her M.S. from the same institution in 2003, researching advanced traffic control strategies. Her undergraduate degree (B.S.) in Electronic Engineering was awarded by Shandong Institute of Light Industry in 2000. Complementing her engineering foundation, she undertook a research visit at LAAS-CNRS in Toulouse in 2007, developing time-optimal trajectories for car-like robots. Currently, she is finalizing her HDR (Habilitation à Diriger des Recherches) at Aix-Marseille University (Nov 2024) under the supervision of Dr. Viktor Jirsa, with a thesis on “Virtual Brain Twins.” Her education spans multiple disciplines and institutions, combining engineering, neuroscience, and clinical modeling. This interdisciplinary background underpins her leadership in personalized neural modeling and translational neuroscience research.

🧪 Experience

Wang is a Research Engineer at INSERM U1106, Aix-Marseille University (2017–present), leading work on virtual brain twins for clinical use in epilepsy and psychiatry. Prior, she was a Postdoc at the Institut du Cerveau (ICM), Paris (2016–2017), studying human neuron behavior with Pr. Vincent Navarro. At École des Mines de Saint-Étienne (2016), she helped develop a physiological SEEG atlas. From 2012–2016, she worked at INSERM U1106 on brain connectivity under Drs. Bernard and Jirsa. Earlier, she researched robotic control and planning at the University of Pisa (2008–2010) in Prof. Antonio Bicchi’s group. Her expertise spans brain modeling, robotics, and neuroscience, with leadership in multi-institutional EU-funded projects. She has served as PI and co-leader in several major efforts like the Human Brain Project and EPINOV. Her interdisciplinary experience uniquely equips her to bridge theory, technology, and medicine in brain modeling applications.

🏅 Awards and Honors

Huifang Elizabeth Wang has earned prestigious research roles and leadership positions in major European and national initiatives. She is PI for the AMIDEX-funded HR-VEP project and WP4 leader in the Horizon RIA Virtual Brain Twin initiative (2024–2027). Her projects have been supported by the Human Brain Project, France 2030, and Horizon Europe. She served as co-task leader in HBP’s epilepsy-focused work packages and trial coordinator in EPINOV RHU, a national clinical modeling trial. Her work on brain modeling has been published in high-impact journals, underscoring her scientific excellence. She has collaborated with pioneers like Karl Friston and Viktor Jirsa, advancing the fields of functional connectivity and computational neuroscience. Additionally, she has been granted funding by institutions such as Fondation Recherche Médicale and Ligue Française contre l’Épilepsie, recognizing her contributions to translational neuroscience and computational modeling in clinical applications.

🔬 Research Focus

Wang’s research centers on developing personalized virtual brain models to understand and treat brain disorders such as epilepsy and psychiatric conditions. She specializes in large-scale neural modeling using neural mass and field models, enabling individual-specific simulations—a concept known as “virtual brain twins.” Her work integrates multimodal neuroimaging data (e.g., SEEG, MRI) with computational frameworks to predict surgical outcomes and guide interventions. As part of projects like VEP Atlas, EPINOV, and EBRAINS, she builds anatomical-functional atlases for clinical use. She also advances Bayesian techniques for parameter estimation in brain modeling. Her research bridges basic neuroscience with translational applications, using virtual brains to delineate epileptogenic zones and simulate drug-resistant epilepsy spread. In psychiatric disorders, her focus includes simulating and analyzing network dysfunction to support precision psychiatry. By blending machine learning, dynamical systems, and neuroinformatics, Wang’s work pioneers a new frontier in personalized medicine using brain simulations.

✅ Conclusion

Dr. Huifang Elizabeth Wang is an interdisciplinary researcher transforming clinical neuroscience through virtual brain modeling, combining engineering precision with neuroscientific insight. Her pioneering work in virtual brain twins supports individualized diagnosis and treatment of epilepsy and psychiatric disorders, representing a significant advance in precision medicine. With extensive experience, numerous publications, and leadership in high-impact research projects, she bridges theory and practice. Her scientific vision and collaborative leadership continue to shape the future of computational neuroscience and neurotechnology for patient care worldwide.

Publications

Ganesh Basawaraj Birajadar | EEG Signal Processing | Best Researcher Award

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Dr. Ganesh Basawaraj Birajadar | EEG Signal Processing | Best Researcher Award

Associate Professor| Fabtech Technical Campus College of Engineering & Research, Sangola

Dr. Birajadar Ganesh Basawaraj is an Associate Professor with over 12 years of academic and research experience, specializing in Machine Learning, Signal Processing, Biomedical AI, and Computer Vision. Currently serving as the Head of Department at Fabtech Technical Campus, Sangola, he holds a Ph.D. in Electrical & Electronics Engineering from VTU, where his research focused on brain abnormality detection using EEG signal analysis. Dr. Birajadar has published over 10 Scopus/SCI-indexed papers, guided numerous UG/PG projects, and reviewed international conferences (IEEE, Springer). With five patents filed in IoT and robotics and a copyright for a dermatological AI tool, he actively contributes to innovation in biomedical and AI domains. His academic journey and professional excellence reflect in his subject expertise, technical leadership, and student mentorship, making him a valuable contributor to engineering education and applied AI research.

Profile

🎓 Education

Dr. Birajadar earned his Ph.D. in Electrical & Electronics Engineering Sciences from PDACE Kalaburagi (VTU) in March 2024, focusing on AI-based EEG signal analysis for brain abnormality detection. He holds an M.E. in Signal Processing from SKNCOE, Pune University, where he ranked 7th with a CGPA of 7.45 in May 2012. He completed his B.E. in Electronics & Telecommunication from SVERI, Solapur University, with distinction in July 2009. His pre-university education includes H.S.C. (86.50%) from KBP College and S.S.C. (85.20%) from DHK Prashala, both under Pune Board. Dr. Birajadar’s education combines a strong theoretical foundation with hands-on expertise in electronics, signal processing, and AI applications, further strengthened by numerous certifications in Python, MATLAB, IoT, Machine Learning, and Data Science from prestigious platforms like NPTEL, Google, Infosys Springboard, and Coursera, shaping him into a well-rounded academic and research professional.

🧪 Experience

Dr. Birajadar currently serves as Associate Professor and Head of Department at Fabtech Technical Campus College of Engineering & Research, Sangola (from Jan 2025), and previously as Assistant Professor at the same institute (June–Dec 2024). Prior to that, he spent over a decade (2012–2024) as Assistant Professor at Smt. Kashibai Navale SCOE, Pandharpur, where he significantly contributed to teaching and research. He also worked as a Trainee Engineer at ITIE Knowledge Solutions, Bangalore. With over 12 years of academic experience, Dr. Birajadar has taught a wide range of subjects such as Machine Learning, Digital Signal Processing, MATLAB Simulink, Communication Buses, Image and Video Processing, and AI tools. He has delivered expert lectures at various institutions and led curriculum innovation. His strong command of tools like Python, MATLAB, TensorFlow, and LabVIEW complements his hands-on guidance of 18 UG and 2 PG projects in AI and biomedical domains.

🏅 Awards and Honors

Dr. Birajadar has received several prestigious awards and recognitions. He secured the 7th University Rank in M.E. (Signal Processing) from Pune University and received a Silver Medal in the NPTEL course “The Joy of Computing using Python.” He was awarded second prize in an AICTE-sponsored STTP Idea Competition and received a Letter of Appreciation from the Entrepreneurship Development Cell. He served as the Primary Evaluator in Toycathon 2021 and is a member of the National Institute for Technical Training & Skill Development. He is designated as a reviewer for “Inter Journal of Computing and Digital Systems (IJCDS)” (SCOPUS-indexed) and the “International Journal of Biomedical Engineering and Clinical Science” (2024–2027). Additionally, he has worked as a reviewer for several international conferences and contributed significantly to promoting innovation, technical evaluation, and academic excellence in AI, signal processing, and biomedical applications.

🔬 Research Focus

Dr. Birajadar’s research focuses on the intersection of Artificial Intelligence and Biomedical Engineering, particularly EEG signal analysis for brain abnormality detection. His work explores the use of AI/ML algorithms to interpret non-stationary biomedical signals, offering clinical insights into neurological disorders. He is deeply involved in Biomedical Signal and Image Processing, leveraging Machine Learning, Deep Learning, and Computer Vision techniques for healthcare innovation. He also explores the Internet of Things (IoT), Embedded Systems, and Big Data Analytics to develop smart, real-time solutions such as landmine detection robots, COVID care bots, and smart vending machines—many of which are patented. His contributions span across pattern recognition, signal feature extraction, and intelligent classification systems. Dr. Birajadar integrates academic rigor with practical application, aiming to enhance diagnostics, patient monitoring, and AI-based clinical tools. He has guided several projects and published extensively in indexed journals, cementing his role as a leading researcher in biomedical AI.

✅ Conclusion

Dr. Birajadar Ganesh Basawaraj is a dedicated academician, innovative researcher, and inspiring mentor, whose interdisciplinary expertise in AI, biomedical signal processing, and IoT drives impactful solutions in healthcare and engineering education.

Publications

Alaa Abd-Elsayed | Neuromidulation | Best Researcher Award

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Dr. Alaa Abd-Elsayed | Neuromidulation | Best Researcher Award

Dr. Alaa Abd-Elsayed 🇺🇸 is an American board-certified anesthesiologist and pain medicine specialist at the University of Wisconsin-Madison 🏥, recognized for his leadership, groundbreaking research 🔬, and compassionate patient care 💉, with a prolific academic career as a professor, director, and global speaker 🎤, blending clinical excellence, innovation, and education 📚 in pain management, with over two decades of medical service and leadership roles across Egypt 🇪🇬 and the U.S. 🇺🇸, while holding numerous prestigious certifications 🏅, published research, and leadership awards 🏆, he stands as a dedicated pioneer in improving chronic pain therapy 🔥 and anesthesiology practice worldwide 🌍.

Profile

Education 🎓

Dr. Alaa’s academic journey began at Assuit University 🇪🇬, earning his MBBCh 🩺 in 2000 & MPH 🎓 in 2006; postgrad, he trained extensively in the U.S. 🇺🇸, completing internships, anesthesiology residency, and a pain medicine fellowship 🏥 at the University of Cincinnati 🎯, and a Clinical Research Fellowship at Cleveland Clinic 🧪; board-certified in anesthesiology & chronic pain medicine 💊, and a Certified Physician Executive (CPE) 🏆, he capped his academic prowess with an Executive MBA 🎓 in 2023, mastering both medicine & healthcare leadership 🧠, and attending diverse leadership programs 💼 from AAPL, UW Health, and Faulkner University, cementing a strong foundation in clinical care and strategic innovation ⚡.

Experience 👨‍🏫

With over 20 years in medicine 🩺, Dr. Alaa has held roles from intern 👨‍⚕️ in Egypt 🇪🇬 to Associate Professor 📖, First Division Chief, and Medical Director at UW-Madison 🇺🇸; he’s led UW Health Pain Services 🔥, pioneering chronic pain medicine management 💊; his journey spanned positions at Assuit University, Cleveland Clinic, and University of Cincinnati 🏥; he’s served as chief fellow, staff anesthesiologist, researcher 🔬, educator 📚, and leader, combining advanced clinical practice 🏆 with administrative excellence 💼, mentoring future physicians while driving cutting-edge research 🚀 and pain medicine innovations 🌟.

Awards & Recognitions 🏅

Dr. Alaa’s distinguished career is crowned with awards 🌟 like the Raj/Racz Excellence Award 🥇, Physician of the Year 🏅, America’s Top Doctors 👏, Fellow of ASA 🧠, and recognition as a World Expert 🌍 in pain by Expertscape; multiple top research, poster 🖼️, and abstract prizes 🧾 from ASIPP, MARC, ASPN, ASA, INS & WSA 🏆 highlight his prolific contributions, while his books 📚 were ranked among the best in anesthesiology and pain medicine 💊; his research has shaped clinical practices 🌡️ and his leadership has been applauded across national and global stages 🎤, underlining his impact as a clinician, educator, and thought leader 💡.

Research Interests 🔬

Dr. Alaa’s research explores pain management innovation 🔥, neuromodulation ⚡, spinal cord stimulation 🧠, dorsal root ganglion therapies 💉, and anesthesiology outcomes 🧾; he’s passionate about translating bench-to-bedside discoveries 🏥, optimizing patient-centered chronic pain therapies 💊, and advancing perioperative safety 🌡️; his peer-reviewed publications 📚, clinical trials 🧪, and systematic reviews ⚗️ have influenced global practices 🌍, securing his place among top 0.05% scholars worldwide 🏆; his scientific vision combines clinical evidence, bioethics, and real-world health solutions for pain relief and anesthetic care 🧠💡.

Publications 

Ata Jahangir Moshayedi | Brain Stimulation | Best Researcher Award

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Dr. Ata Jahangir Moshayedi | Brain Stimulation | Best Researcher Award

Dr. Ata Jahangir Moshayedi is an Associate Professor at Jiangxi University of Science and Technology 🇨🇳 with a PhD in Electronic Science 🎓 from Savitribai Phule Pune University 🇮🇳. He is a prolific academic 🧠 with over 90 publications 📚, three authored books 📖, two patents 🧾, and nine copyrights 📝. A distinguished member of IEEE ⚡, ACM 💻, Instrument Society of India 🧪, and Speed Society of India 🚀, he contributes to editorial boards 🗞️ and international conferences 🌐. His interdisciplinary expertise bridges robotics 🤖, AI 🤖, VR 🕶️, and embedded systems 🔧, driving innovation in education and technology 🚀.

Profile

Education 🎓

Dr. Moshayedi earned his PhD in Electronic Science from Savitribai Phule Pune University 🇮🇳, specializing in robotics and automation 🤖. His educational path is deeply rooted in multidisciplinary technologies like embedded systems 🔧, machine vision 👁️, and AI 🧠. With academic training grounded in both theory 📘 and application 🛠️, he cultivated expertise across digital systems 💡 and bio-inspired robots 🦾. He integrates engineering principles with computer science 💻 to develop cutting-edge innovations in virtual and intelligent systems 🌍. His educational achievements laid the foundation for his impactful career in academic research and mentoring 📈.

Experience 👨‍🏫

Dr. Moshayedi has served as Associate Professor at Jiangxi University of Science and Technology 🇨🇳 since 2018. He leads modules in Robotics 🤖, Embedded Systems 💻, and Digital Image Processing 📷. He supervises UG and PG research 🧪, formulates grant proposals 💡, and serves as a module leader and tutor across advanced computer engineering courses 🧑‍🎓. His role includes designing learning materials 📘, aligning curriculum with accreditation standards 🎯, and evaluating student performance 🎓. He has extensive teaching experience in C/C++ programming 💾, algorithm analysis 📊, and mobile app programming 📱, ensuring comprehensive academic development.

Awards & Recognitions 🏅

🥇2024: Best Mentor, Jiangxi University 👨‍🏫 | 🏅2022: Book Award (Unity in Embedded System Design and Robotics) 📖 | 🥉2022: 3rd National & 1st Provincial Prize, Handy Pipe Detector, China Computer Design Competition 🛠️ | 🥉2021: 3rd National & 2nd Provincial, PEA Project (Pandemic Exam Assistant) 🧪 | 🏆2021: Innovation Award, Iran National Festival 🌍 | 🥉2021: 3rd National & 2nd Provincial, RDK Cloud Robot, Intelligent Service Robot Challenge ☁️🤖 — All reflecting his excellence in guiding innovation, mentoring students 👨‍🎓, and advancing global tech competitions 🌐.

Research Interests 🔬

Dr. Moshayedi’s research integrates robotics 🤖, AI 🧠, and embedded systems 🔧. His work on bio-inspired robots 🐜, mobile robot olfaction 👃, and sensor modeling 🧪 explores intelligent perception and environmental interaction 🌫️. He develops machine vision-based systems 👁️, virtual reality environments 🕶️, and smart embedded architectures 🖥️. His focus on plume tracking 🌬️ and cloud robotics ☁️ brings autonomous systems closer to real-world application. Merging theory and practice 🔍, his research propels innovation across intelligent systems, cyber-physical interaction 🌐, and real-time automation, making significant strides in modern engineering and applied AI 🤖.

Publications 

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,

Nuray Alaca | Motor İmagery | Best Researcher Award

Posted on by Cognitive Scientist Awards

Prof Dr. Nuray Alaca | Motor İmagery | Best Researcher Award

 

Profile

Education

She completed her Doctorate in Physiology at Marmara University, Institute of Health Sciences, Turkey, from 2011 to 2015. Prior to that, she earned her Postgraduate degree in Orthopedic Rehabilitation from the same institute between 1999 and 2002. Her academic journey began with an Undergraduate degree in Physiotherapy and Rehabilitation from Istanbul University, School of Physical Therapy and Rehabilitation, where she studied from 1995 to 1999.

Research 

Her research focuses on physiotherapy and rehabilitation, with a strong academic background in the field. She has been serving as an Associate Professor at Acibadem Mehmet Ali Aydinlar University, Faculty of Health Sciences, Department of Physiotherapy and Rehabilitation, since 2021. Prior to this, she worked as an Assistant Professor from 2016 to 2021 and as a Lecturer from 2011 to 2015 at the same institution.

She has been actively involved in various research projects in physiotherapy and rehabilitation, supervising both postgraduate and doctoral students. Her ongoing research includes a randomized controlled trial on graded motor imagery as an adjunct to physiotherapy in chronic rotator cuff-related pain and an investigation into the sensorimotor representation of body schema in adolescent idiopathic scoliosis compared to healthy individuals. In 2024, she supervised a postgraduate study on the effect of manual lymph drainage in treating breast edema in patients undergoing breast-conserving surgery and adjuvant radiotherapy. Additionally, she is leading a doctoral study examining the effects of tactile stimulation, swimming exercise, or their combination on oxidative stress, inflammation, amyloid beta, neurogenesis, neurotrophic factors, and molecular signaling in an Alzheimer’s model. Her recent research includes a 2023 retrospective study on the effects of tumor type and treatments on lymphedema levels in breast cancer patients undergoing complex decongestive lymphedema therapy. She also supervised a 2021 postgraduate study exploring the effects of swimming exercise, low-level laser, or their combination on degeneration, inflammation, oxidative stress, utrophin protein, and irisin peptide in Duchenne muscular dystrophy (MDX) mice.

Publication

  • Comparison of Pressure Feedback Biofeedback Training with Conventional Lumbar Dynamic Strength Exercises for Chronic Low Back Pain: A Prospective Randomized Trial.

    Alternative therapies in health and medicine
    2024-11-18 | Journal article
    PMID: 39565706
    CONTRIBUTORS: Öztürk Ü; Çörekçi AA; Alaca N; Subaşı F

  • Comparison of static balance and pressure distribution in individuals with unilateral lower limb amputation: The role of barefoot, heel support, and vision.

    Prosthetics and orthotics international
    2024-11-08 | Journal article | Author
    DOI: 10.1097/pxr.0000000000000403
    PMID: 39514704
    CONTRIBUTORS: Tatar Y; Nilüfer Kablan; Nejla Gercek; Nuray Alaca

  • Kinesiophobia, physical activity levels and barriers in breast cancer patients, survivors, and healthy controls: A case-control analysis.

    JPMA. The Journal of the Pakistan Medical Association
    2024-08-01 | Journal article
    DOI: 10.47391/jpma.10558
    PMID: 39160708
    CONTRIBUTORS: Alaca N; Karayazi KT; Arslan DC; Karakus MB; Uras C

  • The immediate effect of thoracolumbar fascia taping on biomechanical properties, low back pain and balance in individuals with transfemoral amputation.

    Journal of back and musculoskeletal rehabilitation
    2024-01 | Journal article
    DOI: 10.3233/bmr-230314
    PMID: 38517772
    CONTRIBUTORS: Çalışkan Z; Alaca N; Kablan N