Dae Hyeob Yoon | Cognitive Robotics | Best Researcher Award

Published on by Cognitive Scientist Awards

Mr. Dae Hyeob Yoon | Cognitive Robotics | Best Researcher Award

Chungbuk National University (CBNU) | South Korea

Dae Hyeob Yoon is an emerging undergraduate researcher in the field of mechanical engineering at Chungbuk National University (CBNU), South Korea. Passionate about micro/nanotechnology, sensors, and MEMS, he has already made noteworthy contributions to the scientific community despite being at an early stage of his academic journey. His research focuses on flexible and conductive heating membranes, culminating in his co-authorship of a peer-reviewed article published in Applied Sciences. His efforts were recognized with an award at the Undergraduate Research Opportunities Program (UROP) at CBNU’s College of Engineering. He has also presented his research at the Korean Society of Mechanical Engineers (KSME) and is set to showcase his work at the European Korean Conference (EKC) in Austria. Yoon’s drive and innovative mindset position him as a promising young talent in the world of research and engineering innovation.

Profile

ORCID

Education

Dae Hyeob Yoon is currently pursuing a Bachelor of Science degree in Mechanical Engineering at Chungbuk National University (CBNU). His education integrates fundamental engineering principles with advanced topics such as micro/nanotechnology, sensor systems, and microelectromechanical systems (MEMS). As part of his academic training, he has engaged deeply with interdisciplinary research methods, particularly focusing on the application of electrospinning and electroless plating technologies in developing flexible electronics. Yoon has also participated in institutional programs such as the Undergraduate Research Opportunities Program (UROP), where he honed his skills in scientific inquiry and innovation. Through his coursework and research experiences, he has demonstrated strong analytical thinking, lab competence, and a deep commitment to problem-solving in modern engineering challenges. His ongoing academic development is supported by hands-on research exposure and international presentation opportunities, laying a strong foundation for future graduate studies or industry-based R&D roles.

Experience

Despite being an undergraduate, Dae Hyeob Yoon has gained substantial research experience through academic and applied projects at CBNU. He played a pivotal role in developing a flexible and conductive heating membrane via BSA-assisted electroless plating on electrospun PVDF-HFP nanofibers, a project that led to his first co-authored publication in a Scopus-indexed journal (Applied Sciences). He has participated in CBNU’s UROP, receiving recognition for his work among peers and faculty. His research contributions have also been shared in the KSME poster sessions, with further international exposure expected at the European Korean Conference (EKC) in Austria. In addition to academic research, Yoon has engaged in a consultancy/industry-related project, integrating real-world needs with lab-scale innovation. His proactive involvement in project design, material synthesis, and result interpretation reflects a strong aptitude for research. His experience signifies readiness for more complex roles in research labs or industry innovation teams.

Awards and Honors

Dae Hyeob Yoon has already earned notable academic recognition at an early stage in his career. He was awarded for his outstanding contribution during the Undergraduate Research Opportunities Program (UROP) achievement presentation hosted by the College of Engineering at Chungbuk National University. This accolade highlighted his scientific contribution and presentation skills in a competitive research environment. Additionally, his co-authored paper was published in the peer-reviewed journal Applied Sciences, an achievement that underscores his ability to contribute to high-quality research outputs. His research work has also been selected for presentation at leading scientific gatherings, including KSME (Korean Society of Mechanical Engineers) and the upcoming European Korean Conference (EKC) in Austria. These honors reflect both national and international acknowledgment of his scientific potential. His early achievements position him as a strong candidate for awards recognizing young researchers or best research articles in the engineering domain.

Research Focus

Dae Hyeob Yoon’s research centers on the integration of micro/nanotechnology, sensors, and MEMS to address modern engineering challenges. His recent work involves the development of a flexible and conductive heating membrane using electrospun nanofibers and electroless plating—technologies poised to impact wearable electronics and smart textiles. By employing Bovine Serum Albumin (BSA)-assisted plating techniques, his research introduces a scalable and cost-effective method for producing mechanically stable, low-voltage heating devices. This innovation aims to overcome performance, flexibility, and integration issues found in existing thermal management systems. His focus on applying advanced materials and fabrication techniques in real-world scenarios reflects his goal of contributing to the next generation of personalized and adaptive electronic technologies. With continued work, Yoon’s research can pave the way for biocompatible, energy-efficient, and multifunctional devices suited for health monitoring, environmental sensing, and human-centric smart systems.

Conclusion

Dae Hyeob Yoon exemplifies a highly motivated undergraduate researcher whose early contributions to flexible electronics and MEMS demonstrate exceptional promise, with strong academic grounding, peer-reviewed publication, research awards, and international presentations validating his suitability for the Best Researcher Article Award.

Publications

Title: Development of a Flexible and Conductive Heating Membrane via BSA‑Assisted Electroless Plating on Electrospun PVDF‑HFP Nanofibers
Year : 2025

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

🎓 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