Zhengyi Yao | Artificial Intelligence | Best Researcher Award

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Mr. Zhengyi Yao | Artificial Intelligence | Best Researcher Award

Sichuan Normal University |China

Zhengyi Yao, from Neijiang, Sichuan, China, is a dedicated researcher affiliated with Sichuan Normal University, holding both bachelor’s and master’s degrees in Computer Science and Technology. His work primarily focuses on the Internet of Things (IoT), cybersecurity, cryptography, and artificial intelligence (AI). With a growing presence in academic publishing, he has contributed to several high-impact journals indexed in SCI and Scopus. Mr. Yao has demonstrated a strong commitment to advancing secure, intelligent systems, particularly in logistics and industrial applications. His interdisciplinary approach blends theoretical research with practical implementation, contributing to emerging technologies such as blockchain-enabled IIoT and quantum cryptography. In addition to publishing five journal articles and securing seven patents, he actively contributes to the field through applied innovations aimed at enhancing privacy protection and data security. As a passionate technologist, Mr. Yao is continually exploring transformative solutions in smart systems, emphasizing the ethical and secure integration of AI in modern digital infrastructure.

Profile

Education

Zhengyi Yao completed his academic training at Sichuan Normal University, earning both his bachelor’s and master’s degrees in Computer Science and Technology. His undergraduate studies provided a solid foundation in software development, algorithms, and system architecture, while his postgraduate work emphasized advanced topics such as artificial intelligence, cybersecurity, and cryptographic methods. During his graduate years, he engaged deeply with interdisciplinary studies, aligning computer science with real-world applications in logistics, IoT, and secure communication systems. His academic performance has been marked by consistent excellence and a proactive engagement in research-driven projects. While enrolled, he also explored the practical aspects of emerging technologies, developing tools and frameworks to support digital transformation in industrial systems. His education has been instrumental in shaping his scientific outlook, fostering a commitment to ethical innovation and robust digital security. These academic experiences continue to inform his contributions to academic research and patent development in the tech and security domains.

Experience

Zhengyi Yao has gained substantial experience as a researcher and innovator in the fields of IoT, cybersecurity, cryptography, and AI. While at Sichuan Normal University, he actively participated in multiple collaborative research efforts that examined the integration of blockchain with IIoT systems and privacy-focused AI applications in logistics. Despite limited consultancy and editorial appointments, his practical contributions are demonstrated through five SCI/Scopus-indexed journal publications and seven patents. He has co-authored research tackling challenges in smart logistics security, 5G-based blockchain sensors, and quantum cryptography, showcasing his capability to bridge theoretical and applied computing. Through independent and team-driven efforts, Mr. Yao has contributed to designing secure systems that support data integrity and user privacy in dynamic industrial environments. His hands-on research experience, supported by solid academic training, underpins his drive to innovate in secure computing technologies and has positioned him as a promising young professional in China’s growing digital research landscape

Research Focus

Zhengyi Yao’s research centers on the intersection of emerging technologies like IoT, blockchain, AI, and cybersecurity, with a strong focus on intelligent logistics systems. He explores secure device communication, privacy-preserving data protocols, and cryptographic models for industrial systems. His work on blockchain-enabled IIoT platforms aims to fortify command operations against cyber threats, while his investigations into quantum cryptography are pushing the boundaries of next-generation digital security. One of his key contributions is the development of 5G-based universal blockchain smart sensors, combining speed, scalability, and trust for dynamic logistics applications. His research also examines how AI can be ethically and securely integrated into cyber-physical environments to optimize data flow, user privacy, and system integrity. Through published works and patented innovations, he is shaping solutions to critical security challenges facing smart logistics and industrial platforms. His forward-thinking approach promotes safer, more resilient infrastructures in an increasingly connected digital world.

Publications

Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System
Year: 2024
Citation:2

Blockchain-enabled device command operation security for Industrial Internet of Things
Year: 2023
Citation:12

5G-BSS: 5G-Based Universal Blockchain Smart Sensors
Year: 2022
Citation:2

Conclusion

Zhengyi Yao exemplifies the qualities of a dedicated and innovative researcher, with notable contributions to smart logistics, cybersecurity, and cryptographic technologies. His blend of academic rigor and applied invention positions him as a rising leader in secure digital systems.

Kihwan Nam | Artificial Intelligence | Best Faculty Award

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Prof. Kihwan Nam | Artificial Intelligence | Best Faculty Award

Dr. Kihwan Nam is an Assistant Professor in the Department of Management of Technology at Korea University and the founder of Aimtory, a high-technology AI company. With a unique blend of academic expertise and entrepreneurial insight, he specializes in Artificial Intelligence (AI), particularly Generative AI, Explainable AI, and Digital Transformation. He earned his Ph.D. in Information Systems from KAIST and holds degrees in Industrial Engineering and Statistics from Korea University and Yonsei University, respectively. Dr. Nam has an extensive research record, with publications in top-tier journals such as Journal of Marketing Research, Decision Support Systems, and Knowledge-Based Systems. His professional journey includes leadership roles in startups and significant AI industry contributions. He is passionate about bridging the gap between academia and industry through impactful, data-driven solutions that transform business strategies, smart factories, and healthcare systems. Dr. Nam is a leading figure in the fusion of cutting-edge AI technologies with business innovation.

Profile

🎓 Education

Dr. Kihwan Nam’s academic background spans statistics, engineering, and management. He holds a Ph.D. in Information Systems and Management Engineering from the prestigious KAIST College of Business, where he honed his expertise in AI-driven decision support and business analytics. Prior to his doctorate, he completed his M.S. in Industrial Engineering at Korea University, acquiring strong analytical and system optimization skills. His academic journey began with a B.A. in Statistics from Yonsei University, which laid a solid foundation in data analysis and quantitative modeling. This interdisciplinary academic training enables Dr. Nam to approach complex problems from technical, managerial, and data-driven perspectives. Throughout his studies, he cultivated a deep interest in predictive modeling, econometrics, and the integration of AI technologies in organizational contexts, which continues to shape his academic and industrial research today. His educational path reflects a consistent commitment to excellence and innovation across disciplines.

🧪 Experience

Dr. Nam has a dynamic career in both academia and industry. He currently serves as Assistant Professor in the Management of Technology at Korea University, following a faculty role in Management Information Systems at Dongguk University. In industry, he is the founder of Aimtory, a company focused on cutting-edge AI solutions, and previously led Basbai, an AI solution firm, as CEO. He also co-founded Sentience, reflecting his commitment to tech entrepreneurship. His dual roles have enabled him to conduct collaborative research with top-tier companies, implement AI in real-world applications, and train future innovators. Dr. Nam’s expertise extends across AI project development, big data analytics, and digital business transformation. His work in areas like smart factories, healthcare, and financial markets underscores his versatility. His diverse experience positions him as a thought leader at the intersection of research, innovation, and enterprise AI deployment.

🏅 Awards and Honors

Dr. Kihwan Nam has received numerous prestigious accolades for his impactful research and innovation. He was honored with the Best Paper Award from the Korea Intelligent Information System Society (2017) for his work on recommender systems in retail, and again in 2019 by the Information Systems Review Society for a field experiment in recommendation design. His deep learning-based financial distress prediction study was a Best Paper Nominee at the INFORMS Data Science Workshop (2020). In 2022, he secured top honors at the Korea Gas Corporation Big Data Competition and received an innovation award from the Startup Promotion Agency for the Big-Star Solution Platform. In 2023, he earned the Best Researcher Award at Dongguk University. These recognitions reflect his excellence in both theoretical contributions and practical applications of AI, reinforcing his role as a leading figure in AI-driven business analytics and intelligent systems research.

🔬 Research Focus

Dr. Nam’s research lies at the intersection of Artificial Intelligence, Business Analytics, and Digital Transformation. He specializes in Generative AI, Explainable AI, LLMs, NLP, and Computer Vision, aiming to drive intelligent decision-making in sectors like healthcare, finance, and manufacturing. His core research explores predictive analytics, recommender systems, robot advisory, and econometric modeling applied to real-world business and technological challenges. By incorporating econometrics with data mining and machine learning, he investigates user behavior, personalization strategies, and large-scale business optimization. His recent projects include stock and cryptocurrency prediction, smart factory optimization, and curated recommendation engines. He is also advancing research in digital transformation (DX) and blockchain-based token economies. Dr. Nam emphasizes bridging theory and application by applying AI innovations to actual business environments, often in collaboration with international enterprises. His work is deeply rooted in the integration of robust statistical methods with scalable, real-world AI systems.

âś… Conclusion

Dr. Kihwan Nam is a visionary academic and AI entrepreneur who merges deep theoretical knowledge with practical applications, shaping the future of AI-driven digital transformation across industries through innovative research, impactful teaching, and real-world solutions

Publications

Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

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Dr. Chaima AOUICHE | Mathematics and Bioinformatics | Outstanding Scientist Award

Dr. Chaima Aouiche is a dedicated academic and researcher in computer science with expertise in artificial intelligence, machine learning, cybersecurity, and bioinformatics. Born on October 24, 1990, in Tebessa, Algeria, she began her academic journey at Larbi Tebessi University and pursued her Ph.D. at Northwestern Polytechnical University (NPU), China. With international exposure, Dr. Aouiche has authored impactful publications on cancer gene prediction, data integration, and AI-based energy systems. She has collaborated across disciplines and countries, contributing to international conferences and peer-reviewed journals. Currently serving as a university teacher in Algeria, she is also a multilingual educator with teaching experience in China and Algeria. Dr. Aouiche combines technical knowledge with strong interpersonal skills and a passion for teaching, traveling, and community service, making her a well-rounded and globally competent scholar committed to innovation and education.

Profile

🎓 Education

Dr. Chaima Aouiche holds a strong academic foundation in computer science. She earned her Bachelor’s degree (2008–2011) and Master’s degree (2011–2013) in Computer Science from Larbi Tebessi University, Algeria, where she was recognized with the “Outstanding Student Award” in 2013. She expanded her horizons by studying the Chinese language for a year (2013–2014) at Northwestern Polytechnical University (NPU) in Xi’an, China. She then pursued a Ph.D. in Computer Science and Technology at NPU (2014–2021), focusing on stage-specific gene prediction, big data integration, and artificial intelligence. Throughout her academic journey, she acquired various global certifications, including Artificial Intelligence Foundations, Advanced Machine Learning, and Deep Learning, further enriching her qualifications. With multilingual skills in Arabic, French, English, and Chinese, she integrates global perspectives into her research and teaching. Her academic path reflects both depth and international breadth.

🧪 Experience

Dr. Chaima Aouiche has a diverse background in academia, industry, and cross-cultural teaching. She began her professional career in project management at MPE-MPI Investments, Tebessa (2011–2013), where she gained hands-on technical and administrative skills. In 2017, she taught English and Arabic in Xi’an, China, enhancing her intercultural communication and educational outreach. Currently, she works as a university teacher in Algeria, engaging in teaching, research supervision, and publication. Her training includes courses in project management, AI, and big data, complemented by technical expertise in programming (Python, Java, R), MATLAB, web technologies, and networking. Her ability to communicate in four languages (Arabic, French, English, Chinese) and her volunteering and mentoring activities reflect her commitment to holistic professional development. Dr. Aouiche’s career is defined by interdisciplinary collaboration, international exposure, and a passion for applied technological solutions, making her an asset in both academia and industry.

🏅 Awards and Honors

Dr. Aouiche’s academic and professional excellence has been recognized through multiple awards and certificates. She was awarded the Outstanding Student Award by Larbi Tebessi University in 2013. Her further accolades include numerous international certifications, such as the HSK 4 Chinese Proficiency Certificate, Artificial Intelligence and Big Data Training (Xi’an Jiaotong University), AI Foundations Masterclass (2023), and Advanced Machine Learning and Deep Learning Certificates (2024). She has also been recognized for her participation in global academic initiatives, such as the International Winter Camp (2017) and the Silk Road Engineering Science Program (2020). In addition to formal honors, her significant co-authorship on high-impact publications in BMC Bioinformatics, Frontiers in Genetics, and IEEE conferences speaks to her professional standing. These accolades collectively highlight her dedication to academic distinction, global engagement, and technological innovation.

🔬 Research Focus

Dr. Aouiche’s research intersects bioinformatics, artificial intelligence, machine learning, and cybersecurity. Her work has emphasized integrating multiple datasets to predict stage-specific cancer-related genes, mapping copy number variations, and modeling aberrant genomic events. She co-authored key studies published in BMC Bioinformatics, Frontiers in Genetics, and Quantitative Biology, which propose dynamic gene modules and data-driven cancer diagnostics. Recent work explores ensemble learning and AI approaches to detect cyberattacks using integrated datasets, showing a pivot toward cybersecurity and smart systems. Additionally, her research extends into renewable energy, specifically applying AI models to optimize photovoltaic systems and MPPT (Maximum Power Point Tracking) control. Her interdisciplinary approach bridges computational biology and engineering, reflecting her adaptability and innovative vision. Dr. Aouiche is particularly interested in applied AI that addresses real-world challenges in medicine, energy, and security, with a growing focus on industry 4.0 applications.

âś… Conclusion

Dr. Chaima Aouiche is an innovative computer scientist and academic whose international education, multidisciplinary research in AI and bioinformatics, commitment to teaching, and dynamic professional experiences make her a valuable contributor to global science and technology.

Publications

Mustaqeem Khan | Deep learning | Best Researcher Award

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Assist. Prof. Dr. Mustaqeem Khan | Deep learning | Best Researcher Award

Dr. Mustaqeem Khan is an accomplished researcher and educator specializing in speech and video signal processing, with a keen focus on emotion recognition using deep learning. Recognized among the Top 2% Scientists globally (2023–2024), he currently serves as an Assistant Professor at the United Arab Emirates University (UAEU). He earned his Ph.D. in Software Convergence from Sejong University, South Korea, and has authored over 40 high-impact publications in IEEE, Elsevier, Springer, and ACM. His contributions span multimodal systems, computer vision, and intelligent surveillance. With extensive experience in academia and research labs, Dr. Khan has also served as a lab coordinator, team leader, and guest editor. He actively collaborates internationally and mentors graduate students. His technical expertise includes TensorFlow, PyTorch, MATLAB, and computer vision frameworks, making him a key contributor to projects involving emotion detection, UAV surveillance, and medical imaging. He brings innovation, leadership, and academic excellence to his roles.

Profile

🎓 Education

Dr. Mustaqeem Khan holds a Ph.D. in Software Convergence (2022) from Sejong University, Seoul, South Korea, where he achieved an outstanding CGPA of 4.44/4.5 (98%) and earned the Outstanding Research Award. His doctoral dissertation focused on advanced studies in speech-based emotion recognition using deep learning. He completed his MS in Computer Science (2018) at Islamia College Peshawar with a Gold Medal, securing a CGPA of 3.94/4.00, and specialized in video-based human action recognition. His undergraduate degree (BSCS, 2015) was from the Institute of Business and Management Sciences, AUP Peshawar, where he developed a web-based design project. His academic background laid the foundation for his research in multimodal deep learning, AI, and signal processing. Throughout his education, Dr. Khan combined rigorous coursework with impactful research, leading to numerous publications and international recognition.

🧪 Experience

Dr. Mustaqeem Khan is currently serving as an Assistant Professor at UAEU (2025–Present), focusing on teaching, research, and student supervision. From 2022 to 2024, he was a Postdoctoral Fellow and Lab Coordinator at MBZUAI, where he led AI projects like drone surveillance and collaborated with the Technical Innovation Institute. At Sejong University (2019–2022), he worked as a Research Assistant and IT Lab Coordinator, guiding projects and mentoring graduate students in speech processing and energy informatics. Prior to this, he was a Lecturer (2018–2019) and Research Assistant (2016–2018) at Islamia College Peshawar, where he taught courses in programming, image processing, and AI. He also led computer vision and speech analytics projects. His international collaborations span institutes in South Korea, France, Saudi Arabia, and India, highlighting his global academic footprint. Dr. Khan is deeply involved in editorial roles and research supervision, embodying academic excellence and research leadership.

🏅 Awards and Honors

Dr. Mustaqeem Khan has been recognized as one of the Top 2% Scientists in the world (2023–2024), a testament to his research impact. He received the Outstanding Research Award from Sejong University in 2022 and was a Gold Medalist during his MS in Computer Science at Islamia College Peshawar (2016–2018). His work has earned multiple Best Paper Awards, including from the Korea Information Processing Society (2021) and Mathematics Journal (2020). He was also granted a fully funded Ph.D. scholarship at Sejong University. Dr. Khan has reviewed for over 35 reputed international journals and serves as an editor and guest editor for several leading publications, including MDPI, IEEE, and Springer journals. His patents in speech-based emotion recognition further validate his innovation. These accolades underscore his academic rigor, global recognition, and leadership in signal processing, AI, and intelligent systems.

🔬 Research Focus

Dr. Mustaqeem Khan’s research lies at the intersection of speech signal processing, multimodal emotion recognition, and computer vision. His Ph.D. work established a foundation for deep learning-based systems capable of understanding human emotions through speech. He has since expanded his research to include age/gender detection, action recognition, violence detection, and medical image analysis using AI. His deep learning models—ranging from CNNs to transformers—have been applied across audio, video, text, and sensor-based data. Dr. Khan is particularly interested in cross-modal transformer-based architectures, edge-AI surveillance systems, and emotion recognition for smart cities. He is also exploring medical AI for fetal, retinal, and Parkinson’s disease diagnostics. His work is published in top-tier venues like IEEE Transactions, Nature Scientific Reports, and ACM. Ongoing collaborations with MBZUAI, TII, and Korean institutions focus on real-time AI applications in UAV systems, smart healthcare, and metaverse content generation.

âś… Conclusion

Dr. Mustaqeem Khan is a globally recognized AI researcher and educator specializing in multimodal emotion recognition and computer vision, whose impactful contributions, international collaborations, and innovative deep learning applications continue to shape the fields of signal processing, smart surveillance, and healthcare technologies.

Publications

Melania Ruggiero | Neuroinflammation | Best Researcher Award

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Dr. Melania Ruggiero | Neuroinflammation | Best Researcher Award

Dr. Melania Ruggiero is a postdoctoral researcher at the University of Bari Aldo Moro, specializing in neuroinflammation and neurodegenerative diseases. She earned her Master’s degree in Medical Biotechnology and Nanobiotechnology from the University of Salento in 2020, followed by a PhD in Functional and Applied Genomics and Proteomics from the University of Bari Aldo Moro in 2024. Throughout her academic journey, she has attended specialized training in neuroscience and advanced fluorescence microscopy. Her research focuses on exploring bioactive compounds for neuroprotection, reprogramming astrocytes into neurons, and mitigating neuroinflammatory processes. She has contributed extensively to scientific literature, authoring multiple publications in high-impact journals, and currently serves as a reviewer for journals such as the International Journal of Molecular Sciences and Cells. Dr. Ruggiero actively collaborates with national and international research groups, contributing to innovative approaches in the treatment of neurodegenerative disorders.

Profile

🎓 Education

Dr. Melania Ruggiero’s academic journey began with her Master’s degree in Medical Biotechnology and Nanobiotechnology at the University of Salento (2020), where she gained a strong foundation in molecular biology, biotechnology, and nanomedicine. She subsequently pursued a PhD in Functional and Applied Genomics and Proteomics at the University of Bari Aldo Moro (2024), during which she engaged in extensive research focused on neurodegenerative disorders. Her doctoral studies were enriched by attending specialized courses, including the “Cellular, Behavioural and Cognitive Neuroscience” school and the “Fluomicro@ICGEB-ICGEB Practical Course of Fluorescence Microscopy and High Throughput Imaging.” These programs equipped her with advanced skills in cellular neuroscience, imaging, and data analysis. Her academic training provided a strong interdisciplinary foundation that bridges biotechnology, neuroscience, and clinical research, supporting her ongoing contributions to innovative treatments for neurodegenerative diseases.

🧪 Experience

Dr. Ruggiero’s professional experience encompasses advanced academic research and international collaboration. Currently a postdoctoral researcher at the Department of Biosciences, Biotechnology and Environment at the University of Bari Aldo Moro, she actively investigates neurodegeneration, neuroinflammation, and potential therapeutic interventions. Her research has led to seven completed and five ongoing research projects. Dr. Ruggiero’s expertise includes the study of bioactive compounds for neuroprotection, demonstrating their ability to reduce neuroinflammatory processes and promote astrocyte reprogramming. She has authored numerous peer-reviewed publications and is actively involved in peer-review activities for journals such as International Journal of Molecular Sciences and Cells. Her collaborative network extends across various esteemed institutions, including universities in Italy and Saudi Arabia. Dr. Ruggiero’s experience reflects a commitment to cutting-edge neuroscience research, fostering translational approaches to combat neurodegenerative disorders.

🏅 Awards and Honors

While specific awards are not listed, Dr. Ruggiero’s scholarly contributions demonstrate substantial recognition in the scientific community. Her numerous publications in internationally indexed journals, with a current h-index of 6, signify her growing influence and impact in the field of neuroscience. Serving as a peer reviewer for prestigious journals such as International Journal of Molecular Sciences and Cells highlights her standing as a respected expert. Additionally, her collaborations with multiple international institutions reflect her role as a valued scientific partner in global research networks. Her research findings, particularly on the neuroprotective roles of bioactive compounds and astrocyte reprogramming, position her as a strong candidate for the Best Researcher Award, acknowledging her innovative contributions to neuroscience and neurodegenerative disease research.

🔬 Research Focus

Dr. Ruggiero’s research centers on neuroinflammation and neurodegenerative diseases, with a particular emphasis on bioactive compounds as potential therapeutic agents. Her innovative work has demonstrated that compounds such as resveratrol, vitamin C, irisin, and lactoferrin can reduce astrogliosis and microgliosis, mitigating neuroinflammatory responses that underlie neurodegeneration. Significantly, she has pioneered findings showing that lactoferrin not only attenuates astrocyte reactivity but also reprograms astrocytes into neuronal precursor cells, promoting neurogenesis and counteracting neuronal loss. This groundbreaking research contributes to developing safer, more effective therapies for neurodegenerative disorders, minimizing side effects compared to conventional treatments. Her work integrates cellular biology, molecular neuroscience, and translational medicine, advancing novel therapeutic strategies for conditions such as Alzheimer’s and Parkinson’s disease. Her collaborations across national and international institutions further enhance the multidisciplinary nature and clinical relevance of her research.

âś… Conclusion

Dr. Melania Ruggiero is an emerging leader in neurodegenerative research, whose pioneering work on bioactive compounds and astrocyte reprogramming offers innovative therapeutic avenues, demonstrated through extensive publications, international collaborations, and impactful scientific contributions, making her a strong candidate for the Best Researcher Award.

Publications

Aymane Edder | Ai and Iot in Healthcare | Best Researcher Award

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Mr. Aymane Edder | Ai and Iot in Healthcare | Best Researcher Award

Edder Aymane is a highly motivated third-year PhD student specializing in Tiny Machine Learning (TinyML) for vital signs monitoring. Based in Casablanca, Morocco, his research focuses on developing efficient machine learning models deployable on embedded systems to analyze and interpret biomedical data in real-time, ultimately enhancing health monitoring and diagnostics. His technical expertise spans TinyML, embedded systems, IoT, biomedical signal processing, model optimization, and programming languages such as Python, C/C++, and MATLAB. Edder has gained hands-on experience through internships at ERAMEDIC, CHU Ibn Rochd, MEDICINA, and BRET LAB, contributing to various healthcare technology projects including the development of remote monitoring prototypes for COVID-19 patients. His academic journey reflects a strong foundation in biomedical engineering, industrial science physics, and medical analysis, complemented by extensive practical skills. Fluent in Arabic and English, with intermediate proficiency in French, Edder Aymane is committed to advancing real-time healthcare solutions through innovative machine learning applications.

Profile

🎓 Education

Edder Aymane’s educational path demonstrates a strong interdisciplinary foundation. He is currently pursuing his PhD at UM6SS, Casablanca (2023–present), focusing on TinyML applications for healthcare monitoring. He earned his Engineering Degree in Biomedical Sciences from ENSAM Rabat (2019–2022), where he worked on practical projects such as remote vital parameters monitoring for COVID-19 patients. Prior to that, he completed preparatory classes in Industrial Science Physics (PSI) (2017–2019), which equipped him with a strong base in physics and engineering principles. Throughout his academic training, Edder engaged in various internships at leading healthcare and research institutions, including ERAMEDIC, CHU Ibn Rochd, and MEDICINA, where he gained real-world experience in laboratory analysis, medical device installation, and healthcare informatics. His academic career combines theoretical learning with hands-on practice, positioning him well for advanced research in biomedical machine learning and embedded systems.

🧪 Experience

Edder Aymane has developed extensive professional experience through diverse internships and research projects related to healthcare technology and biomedical engineering. At ERAMEDIC (July–Sept 2021), he contributed to the installation of Neuro-Navigation Surgery Software and pre-installation of radiology rooms. During his time at MEDICINA (July–Aug 2020), he performed internal and external quality control of biochemistry, hematology, and serology automat systems. At CHU Ibn Rochd, he was involved in setting up COVID-19 services and developing remote monitoring prototypes for vital signs during the pandemic. His end-of-studies internship at BRET LAB (2023–present) further strengthened his research expertise in biomedical signal processing and TinyML model development. These experiences allowed him to apply his technical skills in embedded systems, IoT integration, and machine learning, giving him a well-rounded profile in both research and applied biomedical technologies.

🏅 Awards and Honors

While specific formal awards and honors are not listed, Edder Aymane’s consistent selection for highly technical internships and research projects at reputable healthcare institutions demonstrates recognition of his expertise and potential. His involvement in cutting-edge projects such as the development of a prototype remote vital parameters monitor for COVID-19, installation of complex neuro-navigation systems, and leadership roles during his internships indicate a high level of trust from supervisors and collaborators. His acceptance into the PhD program at UM6SS to work on emerging fields like TinyML reflects both academic and professional acknowledgment of his abilities. Additionally, his multidisciplinary skillset in machine learning, embedded systems, and biomedical signal processing showcases his outstanding technical competency, positioning him as a promising researcher poised for future honors as his academic career progresses.

🔬 Research Focus

Edder Aymane’s research focuses on leveraging Tiny Machine Learning (TinyML) for real-time health monitoring and diagnostics. His work involves developing highly efficient machine learning models optimized for deployment on embedded systems with limited computational resources. Specifically, he focuses on analyzing biomedical signals such as ECG data, enabling continuous monitoring of vital signs directly from wearable or portable devices. His research integrates advanced signal processing techniques, noise filtering, IoT protocols (MQTT, CoAP, BLE), and real-time data interpretation, contributing to more accessible, scalable healthcare solutions. By combining biomedical engineering with embedded AI, Edder aims to bridge the gap between sophisticated machine learning models and practical, low-power medical devices. His work has significant implications for early diagnostics, remote patient monitoring, and scalable healthcare delivery, particularly in resource-limited settings. This research contributes to the growing field of personalized, preventive healthcare powered by intelligent, real-time monitoring systems.

âś… Conclusion

Edder Aymane is an emerging biomedical researcher specializing in TinyML for vital signs monitoring, with a strong foundation in biomedical engineering, embedded systems, IoT, and machine learning; his hands-on experience across leading healthcare institutions and advanced research in real-time healthcare monitoring position him as a promising innovator poised to advance scalable, efficient, and accessible healthcare solutions through cutting-edge embedded AI technologies.

Publications

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

Ahmad Muhammad | Medical Image Analysis | Best Researcher Award

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Mr. Ahmad Muhammad | Medical Image Analysis | Best Researcher Award

Muhammad Ahmad is a passionate AI researcher and software engineer currently pursuing a Master’s in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC). With a Bachelor’s in Computer Science from the University of South Asia, Lahore, he has gained extensive experience in generative AI, LLMs, deep learning, and medical image analysis. He has served as a Software Engineer at E-teleQuote Inc. (USA), where he led projects involving LLaMA 3.1, sentiment analysis, and real-time chatbot systems. His academic contributions include first-author publications on Alzheimer’s disease and brain tumor diagnosis using hybrid deep learning models. Recognized with multiple awards and scholarships, including a fully funded Master’s scholarship, Ahmad brings together strong programming skills, leadership experience, and a commitment to innovation in healthcare AI. His work reflects a deep interest in combining machine learning with medical imaging to solve real-world challenges through intelligent systems.

Profile

🎓 Education

Muhammad Ahmad holds a Master’s degree in Information and Communication Engineering from UESTC, Chengdu, China, where he maintains a GPA of 3.54/4.0 and focuses on generative AI, LLMs, deep learning, and medical image analysis. Previously, he earned a BS in Computer Science from the University of South Asia, Lahore, Pakistan, graduating with a CGPA of 3.16/4.0. His final year project—Walmart Weekly Sales Prediction—reflected his early commitment to machine learning. His academic journey has been bolstered by self-motivated learning, with certifications from Stanford University, IBM, and DeepLearning.AI in TensorFlow, machine learning with Python, and data analysis. Alongside his formal education, Ahmad has organized machine learning workshops and led ACM and IEEE student chapters, showcasing a combination of technical proficiency and community leadership. His educational background lays a strong foundation for interdisciplinary AI research, especially in biomedical applications.

🧪 Experience

Muhammad Ahmad has valuable industry experience as a Software Engineer in AI at E-teleQuote Inc. (Florida, USA), where he led projects utilizing LLaMA 3.1 for document processing and chatbot development. He developed robust NLP solutions, including sentiment analysis and speech recognition systems, while deploying and optimizing AI models for production environments. Earlier, during his internship at Quid Sol (Lahore), he worked on deep learning-based object detection, segmentation, and noise reduction, applying feature engineering and model optimization techniques. Beyond technical roles, he held leadership positions, including Vice-Chair of the ACM Society and event organizer for IEEE, fostering innovation within academic communities. Ahmad’s experience combines hands-on coding with strategic project leadership in AI, making him adept at translating theoretical machine learning concepts into real-world applications, particularly in healthcare and image analysis domains.

🏅 Awards and Honors

Muhammad Ahmad’s academic excellence and leadership have earned him multiple awards. He received a fully funded scholarship from the University of Electronic Science and Technology of China (UESTC) to pursue his Master’s studies in AI. In 2020, he was awarded a semester scholarship for conducting a high-impact workshop on machine learning at the University of South Asia. His community engagement was recognized by the Rooh Foundation and the Government of Pakistan for volunteer work with the Humanity Welfare Foundation. In technical competitions, he secured 1st place at COMSATS University’s Web Development Competition (April 2018) and 2nd place at Superior University (September 2018), demonstrating his early programming excellence. Additionally, Ahmad has earned respected certifications in machine learning, deep learning, and data analysis from Stanford, IBM, and CognitiveClass.ai, highlighting his continuous pursuit of technical mastery in the field of artificial intelligence and data science.

🔬 Research Focus

Muhammad Ahmad’s research focuses on deep learning, generative AI, and large language models (LLMs), particularly applied to medical image analysis. He is committed to enhancing diagnostic accuracy in complex medical conditions using AI. His notable work includes developing a hybrid deep learning architecture with adaptive feature fusion for multi-stage Alzheimer’s disease classification, published in Brain Sciences. Another study, submitted to the International Journal of Machine Learning and Cybernetics, proposes a dynamic fusion model for brain tumor diagnosis. His academic pursuits aim to integrate LLMs and computer vision for robust, intelligent medical systems. Ahmad’s goal is to bridge gaps between artificial intelligence and clinical practice, focusing on real-time, explainable, and scalable AI systems for healthcare. His research embodies a combination of theoretical rigor and practical implementation, striving to deliver solutions that are both impactful and clinically relevant.

âś… Conclusion

Muhammad Ahmad is a driven AI researcher and engineer specializing in generative AI, LLMs, and deep learning for medical imaging, with proven academic, research, and industry experience, recognized through prestigious awards and impactful publications, currently contributing to advanced healthcare technologies at UESTC

Publications

  • Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification

    Brain Sciences
    2025-06-06 | Journal article
    DOI: 10.3390/brainsci15060612
    CONTRIBUTORS: Ahmad Muhammad; Qi Jin; Osman Elwasila; Yonis Gulzar
  • Dynamic Fusion of Local and Global Features for Superior Brain Tumor Diagnosis Submitted as First Author
    to International Journal of Machine Learning and Cybernetics. Submission ID: 6a1c905c-080c-44f9-98e8-
    4f23727a5dc7.

Abdeldjalil Ouahabi | Face recognition | Best Paper Award

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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. Cédric 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

🎓 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 exposure—highlighted by academic appointments in the United States (Bucknell University) and the Middle East (Qatar University)—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.

🧪 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. CĂ©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.

🏅 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. CĂ©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.

🔬 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. Cédric 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.

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

  • Advanced genetic image encryption algorithms for intelligent transport systems

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

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

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

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

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

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.

âś… 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