Melania Ruggiero | Neuroinflammation | Best Researcher Award

Posted on by Cognitive Scientist Awards

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.

✅ 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

Posted on by Cognitive Scientist Awards

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.

✅ 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

Posted on by Cognitive Scientist Awards

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.

✅ 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.

Milena Živković | Artificial Intelligence in Medicine | Best Researcher Award

Posted on by Cognitive Scientist Awards

Ms. Milena Živković | Artificial Intelligence in Medicine | Best Researcher Award

Research Associate| University of Kragujevac, Faculty of Science, Serbia

Milena Živković is a Research Associate at the University of Kragujevac, Faculty of Science, Serbia, with a background in physics and a research focus on the integration of artificial intelligence into medical physics and science education. Her expertise lies in AI-supported educational systems, Monte Carlo simulations in radiotherapy, and environmental radioactivity. With over 38 published papers, her work bridges physics, machine learning, and curriculum innovation. Milena is recognized for her mentorship of gifted students, contribution to interdisciplinary AI-based learning models, and international collaborations with researchers in Europe and the Middle East. She has co-authored dosimetric simulation software for cancer treatment optimization and earned accolades such as Best Oral Presentation Awards at international conferences. As an active member of the Serbian and German Physical Societies, she fosters science communication through national outreach projects and educational initiatives. Her contributions span both academic excellence and impactful community-based science promotion efforts.

Profile

🎓 Education

Milena Živković earned her formal education in physics, culminating in specialized research focused on medical physics, radiation dosimetry, and educational technology. She has completed advanced academic training in English for Academic Communication and Python programming, including Stanford’s “Code in Place.” She holds a Cambridge English Certificate and multiple certificates from the University of Kragujevac in academic writing and pedagogy. Her achievements during her student years include receiving the Annual Award for Best Student from 2015 to 2019, reflecting both academic excellence and extracurricular engagement. Additionally, she has participated in numerous interdisciplinary workshops, competitions, and science communication events, contributing to both her intellectual and pedagogical growth. With a strong foundation in applied physics, her academic journey has been characterized by the seamless integration of theoretical knowledge and practical research, which she continues to expand through post-academic training, conference participation, and interdisciplinary research collaboration with clinical and educational institutions.

🧪 Experience

Milena Živković has significant experience as a Research Associate at the University of Kragujevac, where she combines artificial intelligence with physics education and medical applications. Her research includes machine learning models for radiation dosimetry, classification systems in physics education, and anomaly detection in environmental radioactivity. She serves as a section editor and reviewer for journals like Imaging and Radiation Research and Radiation Science and Technology. Milena is also a contributor to national gifted education programs, curriculum development initiatives, and AI-assisted learning models. She has collaborated with international institutions, including projects with the Clinical Center Kragujevac and partners from Iraq, enhancing the practical application of her research. She has guided STEM projects for youth and mentored students in high school competitions. Her book on Monte Carlo simulations is used in academic and clinical contexts. Her scientific outreach projects further amplify her impact across the academic, educational, and public spheres.

🏅 Awards and Honors

Milena Živković has been the recipient of numerous awards recognizing both academic and community contributions. She received the Best Researcher Award at the University of Kragujevac in 2023 and multiple Best Oral Presentation Awards at international conferences in gynecology, women’s health, and ophthalmology. She also won the Bridge of Mathematics First Place Projects (2023, 2024), highlighting innovative physics education. From 2015 to 2019, she was honored with the Annual Best Student Award and continues to receive high praise for promoting science through projects funded by Serbia’s Center for the Promotion of Science. These include thematic campaigns like Brian May and the Queen of Physics, Our Air = Our Health, and Work + Active = Radioactive. Additionally, she holds advanced training certifications in pedagogy, communication, academic writing, and programming. Her dedication to science communication, youth mentorship, and educational innovation has made her a strong contender for the Young Scientist or Best Researcher Award.

🔬 Research Focus

Milena Živković’s research sits at the intersection of artificial intelligence, medical physics, and education technology. She focuses on developing machine learning-based models for radiation dose analysis, anomaly detection in environmental radioactivity, and AI-assisted problem classification in physics education. Her contributions to the FOTELP-VOX Monte Carlo platform enable precision 3D dose distribution modeling, now applied in clinical settings. She also investigates the ecological effects of radionuclide transfer and food safety. Milena’s interdisciplinary work includes collaborations with philosophers, clinicians, educators, and AI developers to improve curriculum delivery and treatment outcomes. She actively integrates AI into educational strategies to support gifted students and has co-authored software tools used in radiotherapy optimization. Her studies are not only technical but are aimed at real-world impact—ensuring safer radiation practices, informed public health strategies, and accessible science education. Her research promotes knowledge translation, making complex physics applicable to both education and healthcare.

✅ Conclusion

Milena Živković exemplifies a new generation of researchers merging artificial intelligence with applied physics to transform education, healthcare, and science communication. Through interdisciplinary projects, academic excellence, and outreach initiatives, she continues to redefine how science serves society while mentoring future innovators and advancing clinical safety and educational equity.

Publications

  • FOTELP-VOX-OA: Enhancing radiotherapy planning precision with particle transport simulations and Optimization Algorithms

    Computer Methods and Programs in Biomedicine
    2025-08 | Journal article
    DOI: 10.1016/j.cmpb.2025.108838
    CONTRIBUTORS: Milena Zivkovic; Filip Andric; Marina Svicevic; Dragana Krstic; Lazar Krstic; Bogdan Pirkovic; Tatjana Miladinovic; Mohamed El Amin Aichouche

  • FOTELP-VOX 2024: Comprehensive overview of its capabilities and applications

    Nuclear Technology and Radiation Protection
    2024 | Journal article
    DOI: 10.2298/NTRP2403212Z
    CONTRIBUTORS: Milena Zivkovic, P.; Tatjana Miladinovic, B.; Zeljko Cimbaljevic, M.; Mohamed Aichouche, E.A.; Bogdan Pirkovic, A.; Dragana Krstic, Z.

  • Radionuclide contamination in agricultural and urban ecosystems: A study of soil, plant, and milk samples

    Kragujevac Journal of Science
    2024 | Journal article
    DOI: 10.5937/KgJSci2400011A
    CONTRIBUTORS: Mohamed Aichouche, E.A.; Mihajlo Petrović, V.; Milena Živković, P.; Dragana Krstić, Ž.; Snežana Branković, R.

  • Development of DynamicMC for PHITS Monte Carlo package

    Radiation Protection Dosimetry
    2023-11-13 | Journal article
    DOI: 10.1093/rpd/ncad278
    Part of ISSN: 0144-8420
    Part of ISSN: 1742-3406
    CONTRIBUTORS: Hiroshi Watabe; Tatsuhiko Sato; Kwan Ngok Yu; Milena Zivkovic; Dragana Krstic; Dragoslav Nikezic; Kyeong Min Kim; Taiga Yamaya; Naoki Kawachi; Hiroki Tanaka et al.

Manijeh Beigi | Medical Physics | Best Researcher Award

Posted on by Cognitive Scientist Awards

Dr. Manijeh Beigi | Medical Physics | Best Researcher Award

Dr. Manijeh Beigi is an Assistant Professor in the Radiation Oncology Department at Iran University of Medical Sciences. She specializes in dosimetry, radiotherapy treatment planning, and quality audits, with a focus on using machine learning for radiomics and dosiomics analysis. Dr. Beigi earned her Ph.D. in Medical Physics from Tehran University of Medical Sciences in 2018, where she researched the application of Diffusion Tensor Imaging (DTI) in radiotherapy planning. With over a decade of experience in radiotherapy physics, she has worked in multiple hospitals, including Imam Hosein, Haft-e-Tir, and Pardis Niloo Cancer Center. She is actively involved in research on predicting radiotherapy toxicity and advanced MR imaging applications. Dr. Beigi has mentored numerous students, contributed to high-impact journals, and presented at international conferences. Her research aims to enhance radiotherapy precision and patient safety through cutting-edge imaging and AI-driven models. 🎓🔬

Profile

Education 🎓

Dr. Manijeh Beigi holds a Ph.D. in Medical Physics (2018) from Tehran University of Medical Sciences, where she developed automated clinical target volume determination for glioma treatment using multiparametric MRI. She completed her M.Sc. in Medical Physics (2010) at Tarbiat Modares University, focusing on radiotherapy dosimetry and quality audits in Varian linear accelerators. Her coursework covered key topics such as radiotherapy physics, radiobiology, MRI, CT, and PET physics, statistical methods, and treatment planning. Throughout her academic journey, she has specialized in advanced imaging techniques, radiomics, and AI-based predictive modeling for radiotherapy applications. Dr. Beigi’s education has provided her with strong expertise in medical physics, treatment planning optimization, and quality assurance, positioning her as a leader in radiation oncology research and innovation. 📚

Experience 👨‍🏫

Dr. Manijeh Beigi has been an Assistant Professor at Iran University of Medical Sciences since 2020, where she focuses on radiotherapy physics, treatment planning, and quality assurance. Previously, she worked as a Radiotherapy Physicist at Imam Hosein Hospital (2010-2016), Haft-e-Tir Hospital (2016-Present), and Pardis Niloo Cancer Center (2019-2021), specializing in 3D conformal radiotherapy, IMRT planning, machine QA, and dosimetry. She was also a Research Assistant (2012-2018) at Tehran University of Medical Sciences, collaborating on quantitative MRI and spectroscopy research. Dr. Beigi has significant experience in mentoring students, managing research projects, and implementing AI-driven radiotherapy solutions. Her work integrates imaging and machine learning to optimize treatment efficacy and minimize patient toxicity. 💼🔬

Research Interests 🔬

Dr. Manijeh Beigi’s research centers on dosimetry, radiotherapy quality audits, and the application of machine learning in radiomics and dosiomics. She explores AI-driven models to predict radiotherapy toxicity and optimize treatment planning. Her work integrates advanced MRI techniques, such as Diffusion Tensor Imaging (DTI), to enhance clinical target volume delineation for gliomas and other cancers. She investigates imaging biomarkers to assess radiation-induced damage and improve treatment precision. Additionally, Dr. Beigi is actively involved in multi-disciplinary research collaborations, utilizing deep learning for medical image analysis. Her contributions aim to advance radiation oncology by improving accuracy, reducing side effects, and personalizing treatment plans. 🧬📡

Dr. Manijeh Beigi has received several accolades for her contributions to medical physics and radiotherapy research. She has been recognized for her work in AI-driven radiomics and dosiomics at international conferences, including ESTRO and AAPM. Her research on glioma segmentation using DTI and radiotherapy toxicity prediction has been published in top-tier journals. She has also been awarded grants for her studies on advanced MRI applications in radiotherapy planning. Additionally, Dr. Beigi has played a key role in multi-institutional research collaborations, earning recognition for her leadership in medical imaging and quality assurance. 🏆🎖️

Publications 📚

Pedram Fadavi | Radiation oncology | Best Researcher Award

Posted on by Cognitive Scientist Awards

Dr. Pedram Fadavi | Radiation oncology | Best Researcher Award

Dr. Pedram Fadavi, M.D., is a distinguished radiation oncologist and associate professor at Iran University of Medical Sciences (IUMS). Born in Tehran, Iran (1974), he specializes in cancer treatment, radiotherapy, and oncological research. With over two decades of experience, he has contributed extensively to academia and clinical practice. His expertise spans breast, head, neck, and gynecologic cancers. He has published influential research in radiomics, chemotherapy delays, and treatment-induced complications. A dedicated educator, he mentors medical students and residents while advancing oncology research. 📚💡

Profile

Education 🎓

Dr. Fadavi earned his M.D. (2000) and board certification in Radiation Oncology (2006) from Shahid Beheshti University, Tehran. He completed a seven-year medical program (1993-2000) and specialized in radiation oncology during his residency (2002-2006). He holds an Iran Medical Council license (No. 76602) and actively contributes to medical education and research at IUMS. His training provided a strong foundation in oncologic treatments and innovative radiotherapy techniques. 🔬📖

Experience 👨‍🏫

Since 2006, Dr. Fadavi has served as a radiation oncologist at Haftome Tir Hospital (IUMS). He became an associate professor in 2008, teaching at IUMS and Tehran University of Medical Sciences (2011-2013). His clinical expertise includes radiotherapy advancements, cancer management, and interdisciplinary oncology research. He has led multiple projects addressing radiation-induced complications and patient outcomes. His leadership in academic and clinical oncology has shaped the future of radiation therapy in Iran. 🌍🔬

Research Interests 🔬

Dr. Fadavi’s research centers on radiomics, predictive modeling, and improving radiotherapy outcomes. His recent studies explore machine learning applications in radiation toxicity prediction, chemotherapy delays, and novel treatment strategies for breast, cervical, and head-and-neck cancers. He investigates biomarkers for cancer prognosis and response to therapy, with a strong emphasis on precision oncology. His work in computational oncology and artificial intelligence-driven diagnostics is shaping the future of personalized cancer treatment. 💻🧬Awards & Recognitions 🏅

Dr. Fadavi has received numerous accolades for his contributions to radiation oncology and medical research. His work on radiomics, treatment toxicity, and innovative oncological therapies has been recognized in national and international forums. His publications in leading journals and collaborations with top researchers underscore his impact on global cancer research. He has also been honored for his excellence in medical education, research innovation, and commitment to improving patient care. 📜🏅

Publications 📚

Nuo Yu | Radiomics | Best Researcher Award

Posted on by Cognitive Scientist Awards

Ms. Nuo Yu | Radiomics | Best Researcher Award

Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College ,China

Nuo Yu is a Ph.D. candidate at the Cancer Institute and Hospital of the Chinese Academy of Medical Sciences, specializing in radiation oncology with a focus on esophageal squamous cell carcinoma (ESCC). His research primarily explores innovative chemoradiotherapy regimens to improve treatment outcomes for patients with locally advanced ESCC.

Yu has contributed to several peer-reviewed publications in SCI-indexed journals. Notably, he co-authored a study titled “Conversion Chemoradiotherapy Combined with Nab-Paclitaxel Plus Cisplatin in Patients with Locally Advanced Borderline-Resectable or Unresectable Esophageal Squamous Cell Carcinoma: A Phase I/II Prospective Cohort Study,” published in Strahlentherapie und Onkologie in August 2024. This research evaluated the efficacy and safety of a novel chemoradiotherapy regimen, demonstrating promising results in locoregional control and overall survival rates.

In March 2023, Yu co-authored another significant study, “Efficacy and Safety of Concurrent Chemoradiotherapy Combined with Nimotuzumab in Elderly Patients with Esophageal Squamous Cell Carcinoma: A Prospective Real-world Pragmatic Study,” published in Current Cancer Drug Targets. This research focused on treatment strategies for elderly patients with ESCC, highlighting the potential benefits of combining chemoradiotherapy with nimotuzumab.

Yu’s work has been recognized at international conferences, including presentations at the American Society for Radiation Oncology (ASTRO), the Federation of Asian Organizations for Radiation Oncology (FARO), and the Korean Society for Radiation Oncology (KOSRO). These engagements underscore his active participation in the global radiation oncology community and his commitment to advancing cancer treatment research.

While still in the early stages of his career, Yu’s focused research on ESCC and his contributions to the field of radiation oncology position him as a promising candidate for the Best Researcher Award. Continued efforts to expand his research scope, increase publication impact, and assume leadership roles in larger-scale studies will further strengthen his candidacy.

Profile

Scientific Publications