Ewert Bengtsson | Quantitative Microscopy | Best Researcher Award

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Prof. Ewert Bengtsson | Quantitative Microscopy | Best Researcher Award

Professor Emeritus | Uppsala University | Sweden

Prof. Ewert Bengtsson is a distinguished researcher in computerized image analysis and medical imaging, with current work on AI-based diagnostic tools for cancer detection. He earned his PhD in Physics from Uppsala University, where he developed pioneering methods for computer-aided analysis of microscopic images applied to early cancer screening. His professional experience spans research leadership, including Director of the Centre for Image Analysis, Vice Rector for IT at Uppsala University, and project leadership in both academic and industry settings. He has contributed to numerous international collaborations and led projects in medical imaging and IT-driven healthcare solutions. His research interests include AI-based medical diagnostics, computer vision, image processing, and automated cancer detection systems. He has a strong record of mentorship, guiding over 40 doctoral students, and has contributed to global research communities through program committees, editorial boards, and invited talks. His work has been recognized with fellowships, academy memberships, and distinguished awards for contributions to science, engineering, and medical imaging. He possesses advanced research skills in medical image analysis, AI, machine learning, microscopy, and software development for diagnostic tools. 3,627 citations by 2,993 documents, 137 documents, 31 h-index, view h-index button is disabled in preview mode, further highlight his global impact and recognition.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Wählby, C., Sintorn, I. M., Erlandsson, F., Borgefors, G., & Bengtsson, E. (2004). Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy, 215(1), 67–76.

  2. Rodenacker, K., & Bengtsson, E. (2003). A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology, 25(1), 1–36.

  3. Bengtsson, E., & Malm, P. (2014). Screening for cervical cancer using automated analysis of PAP‐smears. Computational and Mathematical Methods in Medicine, 2014, 842037.

  4. Wählby, C., Lindblad, J., Vondrus, M., Bengtsson, E., & Björkesten, L. (2002). Algorithms for cytoplasm segmentation of fluorescence labelled cells. Analytical Cellular Pathology: The Journal of the European Society for Analytical Cellular Pathology.

  5. Stenkvist, B., Bengtsson, E., Eriksson, O., Holmquist, J., Nordin, B., & others. (1979). Cardiac glycosides and breast cancer. The Lancet, 313(8115), 563.

Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

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Assoc. Prof. Dr. Abdulwahid Al Abdulwahid | Cyber Security | Best Researcher Award

Associate Professor | Jubail Industrial College | Saudi Arabia

Dr. Abdulwahid Al Abdulwahid is an Associate Professor of Cybersecurity and Program Director at Jubail Industrial College, Royal Commission for Jubail and Yanbu, with extensive expertise in artificial intelligence for cybersecurity, IoT and Industry 4.0 security, cloud computing privacy, biometrics, and the human aspects of cybersecurity. He holds a PhD in Computing (Cyber Security) from the University of Plymouth, UK, an MSc in Management of Information Technology from the University of Nottingham, and a BSc in Computer and Information Systems from King Faisal University, along with a Graduate Teaching Associate certification from Plymouth University. Over two decades of professional academic and administrative experience, he has served in roles such as Deputy Director for Planning and Development, College Deputy for Student Affairs, and Department Chairperson, alongside delivering specialized lectures, workshops, and training programs locally, regionally, and internationally. His research interests focus on advancing secure and usable authentication systems, AI-driven cybersecurity solutions, and quality-driven approaches in computing education. He is highly skilled in academic accreditation, governance, quality management, and strategic leadership, in addition to contributing as a reviewer, auditor, and public speaker. As an active member of professional and community organizations including ACM, the Saudi Scientific Society for Cybersecurity, Hemaya, and the Saudi Society for Quality, he continues to foster collaboration between academia, industry, and society. His research impact is reflected through 118 citations by 113 documents, 14 publications, and an h-index of 6.

Profiles: Google Scholar | Scopus | ORCID

Featured Publications

  1. Al Abdulwahid, A., Clarke, N., Stengel, I., Furnell, S., & Reich, C. (2016). Continuous and transparent multimodal authentication: Reviewing the state of the art. Cluster Computing, 19(1), 455–474.

  2. Guo, Y., Wang, Y., Khan, F., Al-Atawi, A. A., Abdulwahid, A. A., Lee, Y., & Marapelli, B. (2023). Traffic management in IoT backbone networks using GNN and MAB with SDN orchestration. Sensors, 23(16), 7091.

  3. Al Abdulwahid, A. (2022). Detection of middlebox-based attacks in healthcare Internet of Things using multiple machine learning models. Computational Intelligence and Neuroscience, 2022, 2037954.

  4. Alassafi, M. O., AlGhamdi, R., Alshdadi, A. A., Abdulwahid, A. A., & Bakhsh, S. T. (2019). Determining factors pertaining to cloud security adoption framework in government organisations: An exploratory study. IEEE Access, 7, 136822–136835.

  5. Abdulwahid, A. A. (2023). Classification of ethnicity using efficient CNN models on MORPH and FERET datasets based on face biometrics. Applied Sciences, 13(12), 7288.

Mona Maze | Land Classification | Best Researcher Award

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Assoc. Prof. Dr. Mona Maze | Land Classification | Best Researcher Award

Senior Researcher | Agricultural Research Center | Egypt

Dr. Mona Maze is a dedicated researcher specializing in agricultural climate, plant nutrition, and digital agriculture, with a strong focus on developing climate change adaptation strategies, precision farming approaches, and the use of remote sensing and machine learning in agriculture. She earned her PhD in Plant Nutrition from the Technical University of Munich, where her doctoral work addressed crop growth and yield modeling under water scarcity and changing climatic conditions. Over her professional career, she has actively contributed to national and international research projects in collaboration with institutions such as the European Commission, USAID, UNDP, and FAO, while also leading initiatives like the Digital Dynamic Agricultural Map of Egypt and Early Warning Systems for farmers. Her teaching experience and supervision of graduate students reflect her commitment to academic development, while her publication record in reputed journals such as Scientific Reports, ISPRS Journal of Photogrammetry and Remote Sensing, Agronomy, and Energies highlights her strong scientific contributions. Her research interests span climate-smart agriculture, soil fertility, plant nutrition, digital transformation in agriculture, and data-driven solutions for food security. She possesses advanced research skills in machine learning, deep learning, geospatial data analysis, crop modeling, and experimental design, complemented by professional certifications in business management, spatial data science, and AI-based systems. She has 54 citations by 54 documents, 11 publications, and an h-index of 4, reflecting her growing impact in the scientific community.

Profiles: Scopus | ORCID

Featured Publications

  1. Maze, M., Attaher, S., Taqi, M. O., Elsawy, R., Gad El-Moula, M. M. H., Hashem, F. A., & Moussa, A. S. (2025). Enhanced agricultural land use/land cover classification in the Nile Delta using Sentinel-1 and Sentinel-2 data and machine learning. ISPRS Journal of Photogrammetry and Remote Sensing.

  2. Salah, M., Maze, M., & Tonbol, K. (2024). Intersecting vulnerabilities: Climate justice, gender inequality, and COVID-19’s impact on rural women in Egypt. Multidisciplinary Adaptive Climate Insights.

  3. Maze, M., Taqi, M. O., Tolba, R., Abdel-Wareth, A. A. A., & Lohakare, J. (2024). Estimation of methane greenhouse gas emissions from livestock in Egypt during 1989 to 2021. Scientific Reports.

  4. El-Beltagi, H. S., Hashem, F. A., Maze, M., Shalaby, T. A., Shehata, W. F., & Taha, N. M. (2022). Control of gas emissions (N₂O and CO₂) associated with applied different rates of nitrogen and their influences on growth, productivity, and physio-biochemical attributes of green bean plants grown under different irrigation methods. Agronomy, 12(2), 249.

  5. Abd El-Fattah, D. A., Maze, M., Ali, B. A. A., & Awed, N. M. (2022). Role of mycorrhizae in enhancing the economic revenue of water and phosphorus use efficiency in sweet corn (Zea mays L. var. saccharata) plants. Journal of the Saudi Society of Agricultural Sciences.

Omid Hajipoor | Text Generation | Best Researcher Award

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Mr. Omid Hajipoor | Text Generation | Best Researcher Award

Omid Hajipoor | Amirkabir University of Technology (Tehran Polytechnic) | Iran

Omid Hajipoor is a researcher in artificial intelligence with a strong focus on natural language processing, generative adversarial networks, and large language models. He is currently pursuing his PhD in Computer Engineering at Amirkabir University of Technology, Tehran, building on earlier academic training with a master’s in artificial intelligence and robotics from Malekashtar University and a bachelor’s in software engineering from Birjand University. His professional experience spans roles such as technical product manager, project manager, NLP team leader, and engineer, where he has contributed to the design and development of advanced NLP solutions, chatbots, social media text generation systems, error detection models, and sentiment lexicons. His research interests lie in text generation, adversarial learning, transformers, diffusion models, and applied AI systems for social media and multilingual contexts. He has been involved in impactful projects including railway optimization software, abusive language detection, and generative Persian text applications, and he has published in respected venues such as Neurocomputing and Scopus-indexed journals. In addition to his academic and industrial contributions, he has served as a teaching assistant and lecturer for undergraduate and postgraduate students, and he has mentored teams in innovation events that won recognition. His research skills include programming in Python, MATLAB, and C++, expertise in PyTorch, TensorFlow, and other machine learning frameworks, and strong experience in project management tools like Git and Docker. He has demonstrated leadership, creativity, and technical proficiency throughout his career. His research record shows citations by 2 documents from 1 publication with an h-index of 1.

Profile: Google Scholar | Scopus 

Featured Publications

Hajipoor, O., Nickabadi, A., & Homayounpour, M. M. (2025). GPTGAN: Utilizing the GPT language model and GAN to enhance adversarial text generation. Neurocomputing, 617, 128865.

Hajipoor, O., & Sadidpour, S. S. (2022). Automatic Persian text generation using rule-based models and word embedding. Electronic and Cyber Defense, 9(4), 43–54.

Hajipoor, O., & Sadidpour, S. S. (2020). Automatic keyword extraction from Persian short text using word2vec. Electronic and Cyber Defense, 8(2), 105–114.

Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

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Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.

Reymark Delena | Data Analytics | Best Researcher Award

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Assist. Prof. Dr. Reymark Delena | Data Analytics | Best Researcher Award

Assistant Professor | Mindanao State University – Iligan Institute of Technology | Philippines

Assist. Prof. Dr. Reymark Delena is a dedicated researcher and academic with expertise in machine learning, IoT, climate informatics, data analytics, and smart systems. He earned a Master of Science in Information Technology from De La Salle University and a Bachelor’s degree in Information Systems from the University of Southern Mindanao, further supported by vocational training in ICT. He has served in various academic roles including Instructor, Assistant Professor, and currently contributes as a Senior Consultant in Smart Agriculture at PhilRice, while also engaging in teaching and mentoring at MSU. His professional journey includes roles as a software engineer and developer, providing him with a strong foundation in practical system development alongside research. His research interests cover smart agriculture, early detection systems, educational technologies, and sustainable digital solutions. He has actively participated in national and international conferences, presented research papers, and collaborated on funded projects such as RiceProTek and early fungal detection systems, reflecting his commitment to applied research with societal benefits. He possesses strong skills in programming, mobile and web development, data visualization, AI modeling, and technology management, making him versatile across academic and industry domains. His contributions have been recognized through invited talks, community workshops, and research forums, highlighting his academic leadership and service. His growing research impact is evident with 4 citations by 4 documents, 6 documents, and an h-index of 1.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Ampuan, A. D., & Deleña, R. D. (2024). A quantitative evaluation of online appointment system at Mindanao State University–Main Campus: Employing the system usability scale (SUS) and technology acceptance model (TAM). Proceedings of the 3rd International Conference on Digital Transformation and Applications (ICDXA).

  2. Delena, R. D., Tangkeko, M. S., & Sieras, J. C. (2023). From climate to crop: Unveiling the impact of agro-climate dataset on rice yield in Cotabato Province. Data in Brief, 51, 109754.

  3. Delena, R. D., Tangkeko, M. S., Ampuan, A. D., & Sieras, J. C. (2023). ARP Cotabato: Exploring seasonal climate and rice production in Cotabato Province through advanced data visualization and rapid analytics. Software Impacts, 17, 100546.

  4. Dia, N. J., Sieras, J. C., Khalid, S. A., Macatotong, A. H. T., Mondejar, J. M., & Delena, R. D. (2025). EduGuard RetainX: An advanced analytical dashboard for predicting and improving student retention in tertiary education. SoftwareX, 29, 102057.

  5. Gulam, S. B., & Delena, R. D. (2024). The development of a web-based Meranaw language lexicon using a rule-based morphological analyzer for Meranaw verbs: Dindiyorobasa App. Proceedings of the 3rd International Conference on Digital Transformation and Applications (ICDXA).

Neven Saleh | Healthcare Engineering Systems | Women Researcher Award

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Assist. Prof. Dr. Neven Saleh | Healthcare Engineering Systems | Women Researcher Award

Associate Professor | Future University in Egypt | Egypt

Assist. Prof. Dr. Neven Saleh is a highly motivated and detail-oriented researcher with extensive experience in biomedical engineering, machine learning, deep learning for disease diagnosis, healthcare technology, hospital design, and assistive communication systems. She holds a Ph.D. in Biomedical Engineering from Politecnico di Torino and a master’s and bachelor’s degree in biomedical and electronics engineering from Egyptian universities. She has served as an associate professor at multiple institutions, supervised numerous Ph.D. and M.Sc. students, and contributed to international research collaborations across Italy, Egypt, and the USA. Her research interests focus on AI-driven diagnostic systems for retinal and neurological disorders, cancer detection using image processing, healthcare technology assessment, hospital workflow optimization, and assistive technologies for patients with disabilities. She has received multiple awards and honors for her innovative projects, teaching excellence, and scientific contributions, including national competitions, best thesis awards, and international conference recognitions. She possesses strong research skills in machine learning, deep learning, computer vision, biomedical signal processing, medical instrumentation, and healthcare technology management. She is a member of professional organizations such as TWAS-OWSD and IFMBE and has completed advanced certifications in machine learning, clinical engineering, and healthcare quality management. Her research impact is reflected in 277 citations by 204 documents, 41 publications, and an h-index of 9.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Saleh, N., Sharawi, A. A., Abd Elwahed, M., Petti, A., Puppato, D., & Balestra, G. (2014). Preventive maintenance prioritization index of medical equipment using quality function deployment. IEEE Journal of Biomedical and Health Informatics, 19(3), 1029–1035.

  2. Saleh, N., Abdel Wahed, M., & Salaheldin, A. M. (2022). Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images. International Journal of Imaging Systems and Technology, 32(3), 740–752.

  3. Saleh, N., Farghaly, M., Elshaaer, E., & Mousa, A. (2020). Smart glove-based gestures recognition system for Arabic sign language. In 2020 International Conference on Innovative Trends in Communication and … (pp. 37–…).

  4. Saleh, N., Hassan, M. A., & Salaheldin, A. M. (2024). Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making. Scientific Reports, 14(1), 17323.

  5. Salaheldin, A. M., Abdel Wahed, M., Talaat, M., & Saleh, N. (2024). Deep learning‐based automated detection and grading of papilledema from OCT images: A promising approach for improved clinical diagnosis and management. International Journal of Imaging Systems and Technology, 34(4), e23133.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

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Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.

Benito Farina | Spatio-Temporal CV | Best Researcher Award

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Mr. Benito Farina | Spatio-Temporal CV | Best Researcher Award

Researcher | Universidad Politecnica de Madrid | Spain

Benito Farina is a dedicated researcher in artificial intelligence, machine learning, and biomedical engineering with a strong focus on medical imaging, cancer screening, and predictive modeling. He completed his bachelor’s and master’s degrees in Biomedical Engineering with highest honors at Università degli Studi di Napoli Federico II, where his theses explored machine learning for breast cancer histopathology and deep learning models for lung nodule malignancy detection. He pursued his doctoral studies in Electrical Engineering at Universidad Politécnica de Madrid, graduating with distinction for his research on spatio-temporal image analysis methods to enhance lung cancer screening and therapy response prediction. Professionally, he gained extensive experience as a Junior Research Scientist at Universidad Politécnica de Madrid, where he developed AI-based medical imaging datasets, implemented advanced models including CNNs, RNNs, and transformers, and explored generative models and explainable AI for clinical applications. He later joined the Centro de Investigación Biomédica en Red as a Research Scientist, leading projects in medical image segmentation, classification, and interpretability, managing GPU-based deployments, and contributing to international collaborations and grant proposals. His international exposure includes visiting scientist positions at Harvard University’s Brigham and Women’s Hospital, where he worked on image harmonization techniques to improve consistency in multi-center datasets. His research interests lie in artificial intelligence for healthcare, medical image processing, radiomics, generative models, self-supervised learning, and explainable AI with a vision of translating computational tools into clinical practice. Throughout his career, he has guided undergraduate and master’s students, actively contributed to competitive AI challenges, and engaged in cultural leadership as Vice-President of a community association promoting cultural heritage and development. He has presented his research at reputed conferences, published in indexed journals, and continues to expand his academic contributions through collaborative projects. His research skills include proficiency in Python, R, MATLAB, TensorFlow, PyTorch, and Keras, expertise in GPU cluster computing, dataset development, model deployment with Docker, and technical documentation with LaTeX. Fluent in Italian, Spanish, and English, he thrives in multicultural academic environments and has demonstrated both technical excellence and leadership capabilities. Benito has earned academic distinctions for his outstanding performance in higher education and doctoral research, reflecting his commitment to excellence. With strong foundations in artificial intelligence and biomedical engineering, he aspires to drive advancements in precision medicine, foster global collaborations, and translate AI innovations into impactful healthcare solutions.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Farina, B., Guerra, A. D. R., Bermejo-Peláez, D., Miras, C. P., Peral, A. A., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., Muñoz-Barrutia, A., & others. (2021). Delta-radiomics signature for prediction of survival in advanced NSCLC patients treated with immunotherapy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 886–890). IEEE.

Farina, B., Benito, R. C., Montalvo-García, D., Bermejo-Peláez, D., Maceiras, L. S., & others. (2025). Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification. Computers in Biology and Medicine, 196, 110813.

Ramos-Guerra, A. D., Farina, B., Rubio Pérez, J., Vilalta-Lacarra, A., & others. (2025). Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data. Cancer Immunology, Immunotherapy, 74(4), 120.

Seijo, L., Bermejo-Peláez, D., Gil-Bazo, I., Farina, B., Domine, M., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Bolaños, M. C., Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., & others. (2020). Design and implementation of predictive models based on radiomics to assess response to immunotherapy in non-small-cell lung cancer. In XXXVIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica.