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

Faheem Arshad | Cognitive Neurosciences | Best Researcher Award

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Dr. Faheem Arshad | Cognitive Neurosciences | Best Researcher Award

Dr. Faheem Arshad is an Assistant Professor of Neurology at the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru. A leading cognitive neurologist in India, he specializes in dementia and neurodegenerative disorders. He played a pivotal role in establishing India’s first cognitive disorders registry at NIMHANS, integrating clinical and research excellence. Dr. Arshad is a Senior Atlantic Fellow for Equity in Brain Health at the University of California, San Francisco (UCSF), and the first fellow from South Asia. His research integrates neurogenetics, biomarkers, imaging, and clinical trials, with a focus on inclusivity and low-literacy populations. He actively contributes to national and international collaborations and holds leadership roles within Indian and global neurology communities. Committed to early diagnosis, caregiver support, and prevention strategies, his work aims to improve brain health equity in diverse settings. His ongoing studies explore social interaction, bilingualism, and digital tools in dementia management.

Profile

Education 🎓

Dr. Arshad received his foundational training in Internal Medicine (MD, 2014) at Sher-i-Kashmir Institute of Medical Sciences (SKIMS), Kashmir. He served as Registrar in Internal Medicine at AIIMS, New Delhi until 2016. He pursued advanced neurological training at the National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, earning a DM in Neurology (2019) and completing a Post-Doctoral Fellowship in Cognitive Neurosciences (2020). He furthered his expertise with a prestigious international fellowship in Global Brain Health Equity at the Global Brain Health Institute (GBHI), University of California, San Francisco (UCSF), USA in 2021. Dr. Arshad also holds the MRCP(UK) Neurology credential (2020), reflecting global competence. His multidisciplinary training spans cognitive neurology, imaging, biomarkers, and dementia prevention, equipping him with a holistic approach to brain health research and patient care.

Experience 👨‍🏫

Dr. Faheem Arshad’s professional journey spans over a decade of clinical, academic, and research excellence. He is currently Assistant Professor of Neurology at NIMHANS, Bengaluru, where he has led initiatives in cognitive neurology since 2021. His early training included roles as Junior and Senior Resident in Internal Medicine at SKIMS and AIIMS, followed by Neurology Residency and Postdoctoral Fellowship at NIMHANS. He became a faculty leader and Convener of the Cognitive Neurology subsection of the Indian Academy of Neurology in 2023. Internationally, he served as an Atlantic Fellow at UCSF’s GBHI (2020–2021), developing leadership in brain health equity. Dr. Arshad is a member of the American Academy of Neurology and other prestigious forums. He has published widely, initiated clinical trials, and built registries that bridge clinical insights with translational research. His work integrates social science, neurobiology, and global health in addressing dementia across underserved populations.

Awards & Recognitions 🏅

Dr. Faheem Arshad has received numerous recognitions for his contributions to neurology and dementia research. He was awarded the Bursary Award at the TSS International Neuropsychiatry Conference (2018) for his work on social cognition in Frontotemporal Dementia. As the first South Asian to become a Senior Atlantic Fellow for Equity in Brain Health at UCSF’s GBHI, he has been globally recognized for championing equity in dementia care and research. His appointment as Convener of the Cognitive Neurology subsection by the Indian Academy of Neurology in 2023 highlights his leadership within the national academic community. He holds the MRCP(UK) in Neurology and is an active member of prestigious societies, including the American Academy of Neurology. These honors reflect his ongoing commitment to research innovation, community-based care models, and international collaboration in the field of neurodegenerative disorders.

Research Interests 🔬

Dr. Faheem Arshad’s research focuses on dementia, particularly Frontotemporal Dementia (FTD), Alzheimer’s Disease, and related neurodegenerative conditions. He investigates cognitive reserve, biomarkers, social cognition, neuroimaging, and bilingualism in dementia resilience. His landmark projects include exploring the role of social interaction in FTD (GBHI-AA), plasmapheresis in Alzheimer’s (ICMR), cognitive testing in low-literacy settings, and bilingualism’s impact on cognitive reserve (NIH-funded). He co-leads a SERB-funded project using speech features for early dementia detection and a DBT-funded imaging study for vascular dementia diagnosis. He established India’s first cognitive disorders registry at NIMHANS, integrating socio-demographics, imaging, and biomarker data. His work bridges clinical neurology and public health, emphasizing inclusive research for underrepresented populations. A strong advocate for clinical trials in LMICs, his studies integrate AI tools, cross-cultural data, and longitudinal analyses to improve early diagnosis and therapeutic strategies in dementia care.

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 📚