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

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.

Raveendra Pilli | Image Processing | Best Researcher Award

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Mr. Raveendra Pilli | Image Processing | Best Researcher Award

He mentored B.Tech. projects focused on the early detection of Alzheimer’s Disease. One project involved utilizing multi-modality neuroimaging techniques, where MRI and PET images were collected from the OASIS database, preprocessed, and robust features were extracted for classification. MATLAB and the SPM-12 toolbox were used for this task. Another project focused on the early detection of Alzheimer’s Disease using deep learning networks, where an MRI dataset from the ADNI database was collected, preprocessed, and the performance was compared with baseline algorithms. For this project, he used MATLAB and Python.

NIT-Silchar, India

Profile

Education

A dedicated research scholar with a Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar (Thesis Submitted, CGPA 9.0), specializing in brain age prediction and early detection of neurological disorders using neuroimaging modalities. With extensive teaching experience, a strong passion for research, and a proven ability to develop engaging curricula, deliver effective lectures, and guide students toward academic success, I am committed to contributing to the field through research, publications, and presentations. My academic journey includes an M.Tech. from JNTU Kakinada (76.00%, 2011) and a B.Tech. from JNTU Hyderabad (65.00%, 2007), along with a strong foundational background in science, having completed 10+2 (MPC) with 89.00% in 2003 and SSC with 78.00% in 2001.

Work experience

He worked as a Junior Research Fellow at the National Institute of Technology, Silchar, Assam, from July 2021 to June 2023, where he assisted professors with course delivery for Basic Electronics, conducted laboratory sessions, graded assignments, and provided office hours for student support. From July 2023 to December 2024, he served as a Senior Research Fellow at the same institute, taking on additional responsibilities, including mentoring B.Tech. projects and assisting with Digital Signal Processing laboratory duties. Prior to his research roles, he was an Assistant Professor at SRK College of Engineering and Technology, Vijayawada, Andhra Pradesh, where he taught courses such as Networks Theory, Digital Signal Processing, RVSP, SS, and LICA. He utilized innovative teaching methods, including active learning techniques, to enhance student engagement and learning outcomes. He also mentored undergraduate research projects in image processing and received positive student evaluations for his teaching effectiveness.

Publication