Neven Saleh | Healthcare Engineering Systems | Women Researcher Award

Published on 15/09/202515/09/2025 by Computer Vision Awards

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

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.
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.
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–…).
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.
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

Published on 15/09/202515/09/2025 by Computer Vision Awards

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

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.
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.
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.
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.
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

Published on 15/09/202515/09/2025 by Computer Vision Awards

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

Published on 12/09/202513/09/2025 by Computer Vision Awards

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.

Osman Yildirim | Deep Learning | Best Researcher Award

Published on 12/09/202513/09/2025 by Computer Vision Awards

Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

Published on 11/09/202513/09/2025 by Computer Vision Awards

Mr. Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

University of Crete Medical School | Greece

Dimitrios Theodoropoulos is a researcher and AI specialist in medical imaging with expertise in machine learning, deep learning, computer vision, and artificial intelligence applications in healthcare, particularly in radiology and diabetic retinopathy analysis. He is currently pursuing a PhD in Artificial Intelligence in Medical Imaging at the University of Crete, Medical School, building on a master’s degree in computer engineering from Hellenic Mediterranean University and a bachelor’s degree in physics with specialization in microelectronics from the University of Crete, complemented by training as a radiology assistant. Alongside his academic path, he has worked extensively as a radiographer in MRI, CT, X-ray, mammography, DEXA, and EEG imaging, effectively integrating research with clinical practice. He has served as a visiting research fellow at FORTH-CBML and collaborated with institutions such as the Athens Neurotraining Center and Alexandra Hospital, bridging advanced AI research with healthcare innovation. His research focuses on the development of machine learning and deep learning algorithms for classification, segmentation, and detection tasks in medical imaging, with emphasis on diabetic retinopathy, intensive care monitoring, and noninvasive intracranial pressure assessment, while also extending to areas such as pollen analysis. He has published widely in Scopus-indexed journals and conferences, presented at international congresses and academic symposiums, and delivered guest lectures at the National and Kapodistrian University of Athens. Proficient in Python, MATLAB, TensorFlow, PyTorch, Scikit-learn, Linux, and Docker, he has advanced expertise in data preprocessing, model optimization, and AI-driven biomedical solutions. With certifications in Python programming, machine learning, and deep learning, combined with memberships in the Hellenic Artificial Intelligence Society and the Union of Greek Physicists, he demonstrates a rare integration of technical, clinical, and analytical skills, enabling him to advance scientific progress while contributing to patient-centered healthcare innovation.

Profile: Google Scholar | Scopus Profile

Featured Publications

Tsiknakis N., Theodoropoulos D., Manikis G., Ktistakis E., Boutsora O., et al., Deep learning for diabetic retinopathy detection and classification based on fundus images: A review, Comput. Biol. Med., 135, 104599.

Chatziadam P., Dimitriadis A., Gikas S., Logothetis I., Michalodimitrakis M., Theodoropoulos D., et al., TwiFly: A data analysis framework for Twitter, Information, 11(5), 247.

Theodoropoulos D., Karabetsos D.A., Antonios V., Efrosini P., Karantanas A., et al., The current status of noninvasive intracranial pressure monitoring: A literature review, Clin. Neurol. Neurosurg., 108209.

Theodoropoulos D., Sifakis N., Manikis G., Papadourakis G., Armyras K., et al., Semantic segmentation of diabetic retinopathy lesions using deep learning, SN Comput. Sci., 6(7), 782.

Theodoropoulos D., Trivizakis E., Marias K., Xirouchaki N., Vakis A., et al., Predicting intracranial pressure levels: A deep learning approach using computed tomography brain scans, Neurosurgery, 10.1227.

Barat Barati | Artificial Intelligence | Research Impact Award

Published on 10/09/202513/09/2025 by Computer Vision Awards

Assist. Prof. Dr. Barat Barati | Artificial Intelligence | Research Impact Award

Medical Physics | Shoushtar Faculty of Medical Sciences | Iran

Assist. Prof. Dr. Barat Barati is a distinguished academician and researcher specializing in radiotherapy, artificial intelligence (AI), and computational simulation, with a career dedicated to advancing healthcare diagnostics and treatment through innovative research and teaching. Currently serving as a faculty member at Shoushtar Faculty of Medical Sciences, he integrates deep learning models with biomedical signal processing to address challenges in medical sciences, particularly brain tumor diagnosis and classification. He earned his doctoral degree with a specialization in artificial intelligence and simulation methods, where his PhD research introduced novel approaches by combining machine learning algorithms with Monte Carlo simulation tools such as MCNP, significantly advancing medical physics and diagnostic imaging. With a strong foundation in physics, mathematics, computer science, and biomedical technologies, Dr. Barati bridges engineering and medicine while enhancing his expertise through specialized training in programming, data analysis, and AI-driven healthcare applications. His research focuses on applying AI and computational simulations in radiotherapy and medical imaging, emphasizing brain tumor detection, classification, and radiation treatment modeling, while also extending to biomedical signal processing and machine learning applications for improved diagnostic accuracy and treatment planning. As a faculty member, he contributes to teaching, mentoring, research supervision, and interdisciplinary collaborations, publishing impactful work in Scopus-indexed journals. Recognized for his ability to mentor young researchers and his vision to advance precision medicine, Dr. Barati demonstrates leadership, innovation, and commitment to improving patient outcomes, making him a deserving candidate for the Best Researcher Award and an influential figure in the global scientific community.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Noorimotlagh, Z., Mirzaee, S. A., Kalantar, M., Barati, B., Fard, M. E., & Fard, N. K. (2021). The SARS-CoV-2 (COVID-19) pandemic in hospital: An insight into environmental surfaces contamination, disinfectants’ efficiency, and estimation of plastic waste production. Environmental Research, 202, 111809.
Mohseni, H., Amini, S., Abiri, B., Kalantar, M., Kaydani, M., Barati, B., Pirabbasi, E., & … (2021). Are history of dietary intake and food habits of patients with clinical symptoms of COVID-19 different from healthy controls? A case–control study. Clinical Nutrition ESPEN, 42, 280–285.
Moghiseh, Z., Xiao, Y., Kalantar, M., Barati, B., & Ghahrchi, M. (2023). Role of bio-electrochemical technology for enzyme activity stimulation in high-consumption pharmaceuticals biodegradation. 3 Biotech, 13(5), 119.
Barati, B., Erfaninejad, M., & Khanbabaei, H. (2025). Evaluation of effect of optimizers and loss functions on prediction accuracy of brain tumor type using a light neural network. Biomedical Signal Processing and Control, 103, 107409.
Akbari, G., Mard, S. A., Savari, F., Barati, B., & Sameri, M. J. (2022). Characterization of diet based nonalcoholic fatty liver disease/nonalcoholic steatohepatitis in rodent models: Histological and biochemical outcomes. Universidad de Murcia, Departamento de Biología Celular e Histología.

Vera Yuk Ying Chung | Light Field Image Processing | Best Researcher Award

Published on 09/09/202509/09/2025 by Computer Vision Awards

Dr. Vera Yuk Ying Chung | Light Field Image Processing | Best Researcher Award

Senior Lecturer at The University of Sydney | Australia

Dr. Vera Yuk Ying Chung is a renowned researcher in computer science specializing in light field image processing, machine learning, event-based vision, and multimedia processing. She has dedicated her career to advancing computational methods with practical applications in healthcare, virtual reality, prosthetic vision, agriculture, and multimedia technologies. As a faculty member at the University of Sydney, she has made significant contributions through her extensive publications in high-impact journals and conferences, where her research has gained strong recognition and citations. Her work bridges theory and practice, providing solutions that impact both academia and industry. Dr. Chung has also played a key role in mentoring PhD candidates, securing competitive grants, and fostering international collaborations. With her leadership, interdisciplinary expertise, and long-term dedication, she continues to influence the global research community while shaping innovative technologies for the future.

Professional Profiles

Google Scholar | Scopus Profile | ORCID Profile

Education

Dr. Vera Yuk Ying Chung completed her doctoral studies in computer science at the School of Information Technology, University of Sydney, where she focused on advanced areas of computing and image processing. Her PhD training provided her with a strong foundation in computational techniques, algorithmic design, and data-driven research methodologies. Over the years, she has continued to expand her academic knowledge through active engagement in interdisciplinary studies, including artificial intelligence, deep learning, biomedical computing, and multimedia systems. Her education not only equipped her with technical expertise but also strengthened her ability to address complex real-world challenges through research. In addition, her continuous involvement with students and research projects reflects her dedication to education and knowledge dissemination. By combining her academic background with practical research initiatives, she has established herself as a leader in computer vision and multimedia studies, making significant contributions to both academia and industry.

Professional Experience

Dr. Vera Yuk Ying Chung has built an extensive professional career as a researcher and academic at the University of Sydney, where she has been actively engaged in teaching, mentoring, and advancing cutting-edge research. Her experience includes supervising PhD candidates supported by industry and international grants, coordinating collaborative projects, and publishing widely in prestigious venues such as IEEE Transactions on Image Processing, IEEE Transactions on Visualization and Computer Graphics, and AAAI Conference on Artificial Intelligence. She has also collaborated with diverse teams across different domains, including biomedical imaging, virtual reality, and smart agriculture, showing her adaptability and interdisciplinary reach. Her role has not only been limited to academic research but also extended to project leadership, where she has guided large-scale initiatives and ensured impactful outcomes. With her ability to combine academic rigor with real-world applications, she has earned recognition as a respected leader within the global computer science community.

Research Interest

Dr. Vera Yuk Ying Chung’s research interests span a wide range of areas in computer science, with particular focus on light field image processing, event-based vision, machine learning, and multimedia technologies. She has contributed to developing methods for image quality assessment, super-resolution, 3D reconstruction, and vision systems for visually impaired individuals, reflecting her interest in creating solutions with real societal impact. Her research also extends into biomedical applications, including medical imaging, radiology report generation, and prosthetic vision, which highlight her commitment to health-focused innovation. Additionally, she has explored applications of artificial intelligence in fields such as virtual reality, haptic feedback, smart agriculture, and data-driven environmental monitoring. By bridging computational theory with practical challenges, her research addresses both technical advancements and human-centered needs. Her diverse interests demonstrate a forward-looking approach that continues to push the boundaries of machine learning, computer vision, and multimedia processing.

Research Skill

Dr. Vera Yuk Ying Chung possesses a wide range of research skills that enable her to excel in interdisciplinary areas of computer science. She is highly proficient in machine learning techniques, deep neural networks, and event-based vision processing, which she applies to solve complex challenges in multimedia and image analysis. Her expertise in light field image processing and image quality assessment demonstrates her technical strength in developing models for high-resolution imaging, super-resolution, and 3D reconstruction. She also brings skills in biomedical imaging, virtual reality applications, and smart agricultural solutions, reflecting her versatility and adaptability. Dr. Chung has strong abilities in experimental design, data analysis, algorithm development, and cross-domain integration, which allow her to bridge theory with practical implementations. Furthermore, her experience in supervising research students, managing grants, and coordinating collaborative projects highlights her leadership and organizational skills, making her a well-rounded and impactful researcher.

Publications Top Notes

Title: Light field spatial super-resolution using deep efficient spatial-angular separable convolution
Year: 2018
Citation: 232

Title: A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Year: 2012
Citation: 221

Title: Deep learning in generating radiology reports: A survey
Year: 2020
Citation: 220

Title: Learning implicit credit assignment for cooperative multi-agent reinforcement learning
Year: 2020
Citation: 186

Title: Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues
Year: 2018
Citation: 181

Title: A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method
Year: 2009
Citation: 179

Title: A particle swarm optimization approach based on Monte Carlo simulation for solving the complex network reliability problem
Year: 2010
Citation: 168

Title: CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR
Year: 2021
Citation: 162

Title: Feature selection with intelligent dynamic swarm and rough set
Year: 2010
Citation: 132

Title: Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization
Year: 2014
Citation: 104

Title: Artificial bee colony based data mining algorithms for classification tasks
Year: 2011
Citation: 88

Title: Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability
Year: 2010
Citation: 78

Title: Using transfer learning with convolutional neural networks to diagnose breast cancer from histopathological images
Year: 2017
Citation: 75

Title: A new simplified swarm optimization (SSO) using exchange local search scheme
Year: 2012
Citation: 50

Title: NTIRE 2025 challenge on light field image super-resolution: Methods and results
Year: 2025
Citation: 49

Title: A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization
Year: 2018
Citation: 45

Title: Light field image quality assessment with auxiliary learning based on depthwise and anglewise separable convolutions
Year: 2021
Citation: 44

Title: Human-Computer Interaction. Interaction Design and Usability: 12th International Conference, HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings, Part I
Year: 2007
Citation: 43

Title: Stochastic dual simplex algorithm: A novel heuristic optimization algorithm
Year: 2019
Citation: 35

Title: Fast search block-matching motion estimation algorithm using FPGA
Year: 2000
Citation: 34

Conclusion

Dr. Vera Yuk Ying Chung is a deserving candidate for the Best Researcher Award due to her extensive contributions to light field image processing, machine learning, event-based vision, and multimedia research, along with her impactful publications in prestigious journals and conferences. Her work has advanced both theoretical and applied knowledge, with applications ranging from healthcare and prosthetic vision to virtual reality and smart agriculture, demonstrating meaningful contributions to society. With her strong record of research funding, mentorship, and academic leadership, she continues to inspire and guide future researchers. Her potential for expanding global collaborations, taking on greater leadership roles, and driving innovative interdisciplinary research further underscores her suitability for this recognition.

Naga Nithin Katta | Image Processing | Best Researcher Award

Published on 09/09/202509/09/2025 by Computer Vision Awards

Mr. Naga Nithin Katta | Image Processing | Best Researcher Award

Employee at Oppo | India

Naga Nithin Katta is a highly motivated computer science and engineering professional with a strong focus on innovation, research, and problem-solving. His expertise spans artificial intelligence, machine learning, computer vision, and full stack development, areas in which he has applied his skills to impactful projects. He has gained industrial exposure as a software engineer at OPPO, where he contributed to projects involving video stream analysis, automation of testing frameworks, and mobile AI deployment. Alongside his industry experience, he has been an active mentor in data structures and algorithms, helping students strengthen their problem-solving abilities. His leadership has been recognized through international competitions, including selection among the Top 100 teams globally in the Google Solution Challenge and multiple hackathon victories. With a balance of technical knowledge, practical implementation, and a passion for community contribution, he is steadily building a strong foundation as an emerging researcher with promising leadership potential.

Professional Profile

Scopus Profile

Education

Naga Nithin Katta is pursuing a Bachelor of Technology in Computer Science and Engineering at VNR Vignana Jyothi Institute of Engineering and Technology, where he has been developing a strong academic background in computing principles, software engineering, and applied technologies. Prior to this, he successfully completed a diploma in computer science from the Government Institute of Electronics, which provided him with a solid technical base in programming, database management, and system design. His educational journey has been complemented by active participation in research-oriented projects, hackathons, and collaborative learning platforms that encouraged innovation and problem-solving. He has consistently demonstrated academic excellence by integrating classroom knowledge with practical applications, which is evident in his project work and international recognition through competitive platforms. This strong educational foundation has equipped him with both theoretical and applied perspectives, allowing him to bridge the gap between academia and industry while nurturing his passion for research and development.

Professional Experience

Naga Nithin Katta has gained valuable professional experience as a software engineer at OPPO, where he contributed to significant projects aimed at improving efficiency and automation in mobile technologies. His work involved developing web applications using Vue.js and MySQL for managing project statuses, implementing video stream analysis through OpenCV and Python, and deploying AI models on mobile devices using ONNX and Beeware. He played a key role in creating a UI automation system powered by large language models, reducing manual testing efforts and enhancing accuracy. Additionally, he contributed to building a network operator testing automation tool, streamlining processes and reducing workforce requirements. Alongside his industry work, he served as a student mentor at SmartInterviews, guiding learners in data structures and algorithms and preparing them for technical challenges. This blend of industrial expertise and teaching experience reflects his versatility, ability to collaborate across teams, and passion for applying research in practical contexts.

Research Interest

Naga Nithin Katta’s research interests lie primarily in the fields of artificial intelligence, computer vision, natural language processing, and software engineering, with a particular focus on developing innovative solutions that bridge academic research and real-world applications. He has worked on projects such as sign language converters that integrate computer vision with generative AI and cloud technologies, reflecting his interest in human-computer interaction and accessibility-focused applications. His engagement with large language models and UI automation tools demonstrates his curiosity in advancing human-machine interaction and automated testing frameworks. Additionally, his focus on video stream analysis and frame detection highlights his inclination towards multimedia research and visual computing. He is also keen on exploring areas such as deep learning optimization, mobile AI deployment, and cloud-integrated intelligent systems. His vision is to contribute to impactful solutions that enhance everyday technologies while simultaneously pursuing scholarly outputs that advance scientific knowledge.

Research Skill

Naga Nithin Katta has developed strong research skills that enable him to design, implement, and evaluate innovative solutions across different domains of computer science. He is proficient in programming languages such as C, C++, Python, and Java, and demonstrates advanced knowledge in full stack development with tools like ReactJs, Vue.js, and MySQL. His expertise in AI and machine learning is reflected in projects involving computer vision, natural language processing, and model deployment on mobile devices. He has practical experience in research-driven software development, having implemented algorithms for video frame detection, gesture recognition, and UI automation powered by large language models. His familiarity with tools like OpenCV, ONNX, Flask, and cloud-based APIs allows him to conduct applied research efficiently. He also possesses strong problem-solving abilities, demonstrated by his role as a mentor in data structures and algorithms. His skills in bridging theoretical concepts with industrial applications showcase his potential as a future research leader.

Publications Top Notes

Title: Optical Motion Detection Language Generator: A Survey

Year: 2025

Conclusion

Naga Nithin Katta is a deserving candidate for the Best Researcher Award as he has consistently demonstrated innovation, technical expertise, and leadership in both academic and industrial settings. His impactful projects, including advancements in computer vision, automation, and AI-driven solutions, showcase contributions that address real-world challenges and benefit society. With proven recognition in global competitions, mentorship roles, and industry research experience, he has already made meaningful strides as an emerging researcher. With a continued focus on publishing in reputed venues and building stronger international collaborations, he holds significant potential to become a future leader in the research and technology community.

Sa Zhou | Human Machine Interface | Best Researcher Award

Published on 09/09/202509/09/2025 by Computer Vision Awards

Dr. Sa Zhou | Human Machine Interface | Best Researcher Award

Postdoc at Stanford University | United States

Dr. Sa Zhou is a dedicated researcher in the fields of biomedical engineering, neuroscience, and psychiatry, currently working as a postdoctoral scholar at Stanford University. His research emphasizes multimodal neuroimaging, brain-machine interfaces, stroke rehabilitation, cognitive enhancement, and neuromodulation, bridging engineering and medicine to improve human health outcomes. He has published extensively in internationally recognized journals and contributed to conferences with global visibility. His innovative contributions extend beyond academic research into patents, translational projects, and clinical applications, demonstrating his ability to turn theory into practice. Through his involvement in teaching, mentoring, and editorial activities, he has shown leadership and commitment to advancing science and supporting the next generation of researchers. His global collaborations across Asia and the United States reflect his adaptability and international impact. With a strong foundation and innovative approach, he continues to make meaningful contributions with high potential for future leadership in research and society.

Professional Profiles

Google Scholar | Scopus Profile | ORCID Profile

Education

Dr. Sa Zhou pursued his higher education with a strong focus on engineering and biomedical sciences, which provided him with a multidisciplinary foundation for his research career. He earned his Bachelor and Master of Philosophy degrees in Electrical Engineering from Yanshan University, where he gained in-depth knowledge of signal processing, system development, and computational approaches to neural data. He then advanced his academic journey by completing his PhD in Biomedical Engineering at The Hong Kong Polytechnic University, where he developed expertise in neuroengineering, multimodal neuroimaging, and stroke rehabilitation. His doctoral research explored neural reorganization in sensorimotor impairments and recovery, involving systematic neurological evaluations, electrophysiological analyses, and clinical trials. This educational background not only honed his analytical and technical skills but also laid the groundwork for his interdisciplinary approach, bridging engineering principles with neuroscience and clinical applications. His academic training has shaped his ability to conduct impactful research at the interface of technology and medicine.

Professional Experience

Dr. Sa Zhou’s professional experience reflects a blend of academic research, teaching, and applied innovation in biomedical engineering and neuroscience. He is currently a postdoctoral scholar at Stanford University in the Department of Psychiatry and Behavioral Sciences, contributing to projects focused on personalized cognitive enhancement and digital interventions for aging-related disorders. Prior to this role, he worked extensively at The Hong Kong Polytechnic University, where he participated in pioneering projects on stroke rehabilitation, neuromodulation, and brain-machine interfaces. His experience also includes collaboration on international research initiatives that integrate engineering, neuroscience, and clinical practice, leading to high-impact publications and translational applications. Alongside research, he has actively contributed to education as a teaching assistant in neuroengineering, applied electrophysiology, and digital signal processing, mentoring undergraduate and postgraduate students. His diverse professional background demonstrates his ability to conduct innovative research, translate findings into practical solutions, and inspire future researchers through academic leadership.

Research Interest

Dr. Sa Zhou’s research interests span a wide spectrum of neuroscience, engineering, and clinical applications, with a particular emphasis on developing innovative technologies for human health and rehabilitation. His work focuses on multimodal neuroimaging techniques, including structural and functional MRI, DTI, and EEG, combined with advanced signal processing and machine learning approaches to understand brain networks. He is also deeply engaged in brain-machine interfaces, stroke rehabilitation, neuromotor interfaces, and robotic systems that enhance motor recovery and cognitive function. His interests extend to non-pharmacological interventions for preclinical Alzheimer’s disease and mild cognitive impairments, reflecting his commitment to addressing aging-related neurological disorders. He also explores neuromodulation methods, including electrical and ultrasound stimulation, to optimize therapeutic outcomes. These diverse interests demonstrate his interdisciplinary approach, integrating engineering innovations with clinical neuroscience to create personalized solutions. His research aims not only to advance scientific knowledge but also to deliver real-world impact in improving patient care and well-being.

Award and Honor

Dr. Sa Zhou has been recognized with numerous awards and honors that highlight his academic excellence, research achievements, and leadership potential. He has received prestigious fellowships, including support from international neuroscience and brain aging associations, acknowledging his contributions to advancing cognitive enhancement research. During his doctoral studies, he was awarded the PolyU Research Postgraduate Scholarship for outstanding performance, along with national-level scholarships that placed him among the top-performing postgraduates in China. He has also earned multiple competitive awards in research and innovation competitions, such as the Hong Kong Medical and Healthcare Device Industries Association Student Research Award and the Champion Award in the Three-Minute Thesis Competition. His teaching excellence was recognized with Best Teaching Assistant Awards, demonstrating his impact in both research and education. These accolades reflect his consistent pursuit of excellence, his ability to compete at international levels, and his dedication to advancing science while inspiring peers and students.

Research Skill

Dr. Sa Zhou possesses a wide range of research skills that integrate advanced engineering techniques with clinical neuroscience applications. His expertise includes real-time robotic control, rehabilitation system design, and multimodal neuroimaging analysis, enabling him to develop and test innovative technologies for stroke rehabilitation and cognitive enhancement. He is proficient in conducting clinical trials with stroke patients, performing neuroimaging scans such as fMRI, DTI, and structural MRI, and analyzing electrophysiological signals including EEG, EMG, and LFP. His skillset also extends to neuromodulation experiments using transcranial ultrasound stimulation and neuromuscular electrical stimulation, combined with advanced kinematic signal recording systems. In addition, he has strong programming and analytical abilities in machine learning, Matlab, Python, and C/C++, which support his work in neural decoding and brain network analyses. These skills, coupled with experience in mentoring, peer review, and system development, demonstrate his ability to design, implement, and translate research into impactful clinical and technological outcomes.

Publications Top Notes

Title: Pathway-specific cortico-muscular coherence in proximal-to-distal compensation during fine motor control of finger extension after stroke
Year: 2021
Citation: 32

Title: Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke
Year: 2022
Citation: 24

Title: Effect of pulsed transcranial ultrasound stimulation at different number of tone-burst on cortico-muscular coupling
Year: 2018
Citation: 20

Title: Optimization of relative parameters in transfer entropy estimation and application to corticomuscular coupling in humans
Year: 2018
Citation: 18

Title: Low-intensity pulsed ultrasound modulates multi-frequency band phase synchronization between LFPs and EMG in mice
Year: 2019
Citation: 17

Title: Impairments of cortico-cortical connectivity in fine tactile sensation after stroke
Year: 2021
Citation: 15

Title: Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Year: 2022
Citation: 5

Title: Automatic theranostics for long-term neurorehabilitation after stroke
Year: 2023
Citation: 4

Title: Estimation of corticomuscular coherence following stroke patients
Year: 2017
Citation: 4

Title: Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models
Year: 2024
Citation: 1

Title: Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework
Year: 2025

Title: Relationships between neuropsychiatric symptoms, subtypes of astrocyte activities, and brain pathologies in Alzheimer’s disease and Parkinson’s disease
Year: 2025

Title: Neural Correlates of Dual‐Functional Local Dynamic Stability in Older Adults
Year: 2024

Title: Profiles of brain topology for dual-functional stability in old age
Year: 2024

Title: Neuromuscular networking connectivity in sensorimotor impairments after stroke
Year: 2023

Conclusion

Dr. Sa Zhou is highly deserving of the Best Researcher Award for his outstanding contributions at the intersection of biomedical engineering, neuroscience, and psychiatry, with impactful research in neuroimaging, brain-machine interfaces, stroke rehabilitation, and cognitive enhancement for aging populations. His work has advanced both theoretical understanding and practical applications, supported by high-quality publications, patents, and international collaborations that bridge engineering and medicine. Beyond research, his leadership in teaching, mentoring, and reviewing reflects a strong commitment to the scientific community and knowledge dissemination. With his growing expertise, innovative approaches, and dedication to addressing critical health challenges, Dr. Zhou shows great promise for future research breakthroughs and leadership in shaping the fields of neuroengineering and translational neuroscience.