Victor Grillo Sobrinho | Emotion and Cognition | Best Researcher Award

Published on by Cognitive Scientist Awards

Ms. Victor Grillo Sobrinho | Emotion and Cognition | Best Researcher Award

 State University of Rio de Janeiro, Rio de Janeiro, CEP 20550-900 |  Brazil

Victor Grillo Sobrinho is a dedicated professional in the field of physical education and exercise sciences, with expertise in exercise physiology, resistance training, and electrostimulation. He has built his academic and professional career with a strong commitment to promoting health, performance, and well-being across different populations, particularly older adults. His work integrates both practical teaching in schools and specialized training in fitness institutions, reflecting a balance between pedagogy and applied sports science. Victor has served as a professor of physical education at Colégio Oliveira Mallet, where he has contributed to youth development in physical fitness and sports. In addition, he has gained extensive experience in electrostimulation training at Eletro Treino, working with advanced methods to enhance muscular strength and functionality. His academic involvement includes participation in research groups focusing on exercise, aging, and affectivity, consolidating his role as a professional bridging education, research, and practice.

Profile

ORCID

Education

Victor Grillo Sobrinho’s academic journey began with undergraduate studies in physical education at Centro Universitário Augusto Motta, where he earned both his licentiate and bachelor’s degrees. His early academic focus explored strength gains through electrostimulation training, guided by Dr. Francisco Navarro. To deepen his expertise, he pursued postgraduate specialization in exercise physiology and training prescription at Universidade Estácio, producing a systematic review on electrostimulation for strength development. Expanding his scope, he completed a specialization in physical training for older adults at Faculdade de Minas, with research on affective perception in resistance training among the elderly. His commitment to advancing scientific knowledge led him to pursue a master’s degree in Exercise and Sport Sciences at Universidade do Estado do Rio de Janeiro. His dissertation focused on validating psychometric scales such as the Feeling Scale and Felt Arousal Scale for older populations, underlining his dedication to research in aging, exercise, and well-being

Experience

Victor Grillo Sobrinho has extensive teaching and professional experience across academic and fitness environments. He began his career as a physical education teacher at Colégio Oliveira Mallet, where he has been responsible for instructing middle and high school students, fostering a culture of health and movement. His passion for advanced training methodologies is evident in his role at Eletro Treino, where he serves as a specialist in electrostimulation-based fitness programs, utilizing MIHA technology to improve muscular performance. Additionally, he has worked at renowned fitness centers, including Bodytech and Wellness Fit Club, delivering functional training, resistance exercise, and muscle conditioning to diverse populations. His teaching philosophy combines scientific knowledge with practical strategies to enhance performance and health outcomes. Victor’s professional trajectory reflects his ability to engage with different audiences, from young learners to elderly individuals, while actively participating in research groups focused on training and aging.

Awards and Honors

Throughout his career, Victor Grillo Sobrinho has been recognized for his contributions to exercise science and education. His participation in academic events and congresses highlights his role as both a researcher and practitioner committed to advancing knowledge in physical education and sports sciences. At the VI Congresso Internacional de Educação Física e Desporto, he presented research on the reliability of the Feeling Scale and Felt Arousal Scale in elderly populations, gaining recognition for his innovative work in psychometric evaluation in exercise contexts. Similarly, at the X Congresso Brasileiro de Metabolismo, Nutrição e Exercício, his presentation on validating affective and arousal scales among older adults demonstrated his commitment to bridging exercise science with applied gerontology. His academic achievements, including completing advanced postgraduate training and securing a master’s degree with a relevant dissertation in the field, further mark his distinction. These accomplishments reflect his dedication to improving physical training and well-being.

Research Focus

Victor Grillo Sobrinho’s research primarily focuses on the intersection of exercise, aging, and affectivity. He investigates how different training methodologies, particularly resistance training and electrostimulation, influence physical performance, strength, and emotional responses among older adults. His work explores psychophysiological aspects of exercise, emphasizing the validation of affective scales such as the Feeling Scale and Felt Arousal Scale to better understand the psychological dimensions of physical training. By integrating physiological outcomes with subjective well-being, Victor aims to optimize training prescriptions for elderly populations, ensuring both health benefits and motivational adherence. His broader interests include exercise physiology, functional training, and innovative approaches such as electrostimulation to enhance muscular performance. Participation in research groups like GEESI strengthens his role in collaborative projects addressing geriatric exercise science. His academic and applied focus demonstrates a commitment to promoting healthy aging through scientifically informed, personalized training interventions

Conclusion

ictor Grillo Sobrinho stands as a committed educator, researcher, and practitioner in exercise sciences, blending academic rigor with professional expertise, advancing the fields of physiology, electrostimulation, and geriatric fitness, while fostering healthier lifestyles through evidence-based practices

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