Assoc. Prof. Dr. Mar Jimenez de la Pena | Medicine and Health Sciences | Medical Hypothesis Award

Assoc. Prof. Dr. Mar Jimenez de la Pena is a highly accomplished radiologist and academician with a distinguished career devoted to advancing diagnostic imaging, neuroimaging, and translational radiology. She completed her Licentiate in Medicine and Surgery at the Universidad Complutense de Madrid and specialized in Radiodiagnosis at Hospital 12 de Octubre in Madrid, establishing a robust clinical and scientific foundation. Currently serving as the Associate Head of Diagnostic Imaging at Hospital Universitario QuirónSalud Madrid, she also contributes as a Professor of Medicine at the Universidad Europea de Madrid, where she mentors medical students and radiology residents. Her professional experience spans more than two decades, encompassing academic instruction, clinical excellence, and leadership in medical imaging. Assoc. Prof. Dr. Mar Jimenez de la Pena has been actively engaged in national and international research collaborations, focusing on the application of advanced MRI techniques in neurological disorders, pediatric neuroimaging, and genetic syndromes. Her research interests center on neurodevelopmental disorders, brain perfusion analysis, and the application of artificial intelligence in MRI interpretation. She has demonstrated outstanding research skills in multimodal MRI data acquisition, neuroimage analysis, and clinical study design. Her work has been published in top-tier journals such as Frontiers in Neuroscience, Neuroradiology, European Journal of Medical Genetics, and American Journal of Medical Genetics Part A, significantly contributing to the understanding of complex neuroimaging patterns and clinical correlations. With over 40 peer-reviewed articles, including 33 international papers, and multiple book chapters, she has established a strong academic footprint. Assoc. Prof. Dr. Mar Jimenez de la Pena has received over 30 prestigious awards, including 29 honors from the Spanish Society of Radiology (SERAM), recognition from the Spanish Society of Neurosurgery (SENR), and an American Society of Radiology Award for her scientific presentations. Her memberships in SERAM, SENR, and ESR, alongside her role as a reviewer for Radiología, underscore her professional dedication to the advancement of radiological science. A visionary researcher and educator, she continues to contribute to training programs for medical imaging professionals, fostering innovation and excellence in radiological education. In conclusion, Assoc. Prof. Dr. Mar Jimenez de la Pena exemplifies a perfect blend of clinical expertise, academic leadership, and research innovation, making her a highly deserving candidate for international recognition in medical imaging and radiological research. Her continued contributions promise to shape the future of neuroimaging and radiodiagnostic excellence globally.

Profile: ORCID

Featured Publications

Jimenez de la Pena, M. (2023). Tatton‐Brown–Rahman syndrome: Novel pathogenic variants and new neuroimaging findings. American Journal of Medical Genetics Part A. Citations: 12

Jimenez de la Pena, M. (2023). White-matter lesions and cortical cerebral blood flow evaluation by 3D arterial spin-labeled perfusion MRI in asymptomatic divers: Correlation with patent foramen ovale occurrence. Journal of Clinical Medicine. Citations: 18

Jimenez de la Pena, M. (2022). Development of a super-resolution scheme for pediatric magnetic resonance brain imaging through convolutional neural networks. Frontiers in Neuroscience. Citations: 25

Jimenez de la Pena, M. (2022). Mutations in the COL18A1 gene associated with Knobloch syndrome and structural brain anomalies: A novel case report and literature review of neuroimaging findings. Neurocase. Citations: 9

Jimenez de la Pena, M. (2021). Neuroimaging findings in patients with EBF3 mutations: Report of two cases. Molecular Syndromology. Citations: 7

Jimenez de la Pena, M. (2021). Abnormal frontal gyrification pattern and uncinate development in patients with KBG syndrome caused by ANKRD11 aberrations. European Journal of Paediatric Neurology. Citations: 15

Jimenez de la Pena, M. (2020). Automatic identification of atypical clinical fMRI results. Neuroradiology. Citations: 22

Mustansar Fiaz | Medical Image Segmentation | Best Researcher Award

Dr Mustansar Fiaz, IBM Research,a United Arab Emirates

Passionate about computer vision and deep learning, Mustansar Fiaz specializes in remote sensing, person search, medical image segmentation, visual object tracking, and multi-modal analysis. With a Ph.D. from Kyungpook National University, he has over three years of post-PhD experience. Mustansar currently leads innovative research at IBM Research in Abu Dhabi, developing advanced models for remote sensing applications. His prior roles include research associate at MBZUAI and senior AI software engineer at Tricubics, Seoul. His notable projects and numerous awards reflect his contributions to the field. 🌍🖥️🔬🛰️

Publication profile

google scholar

Education

Dr. Mustansar Fiaz, completed a Ph.D. in Computer Science and Engineering from Kyungpook National University, Daegu, S. Korea (Mar. 2016 – Feb. 2021) with a GPA of 4.18/4.5. Their thesis focused on “Robust Object Tracking and Segmentation Using Siamese Networks.” They earned a Master’s in Engineering from Sejong University, Seoul, S. Korea (Mar. 2014 – Feb. 2016) with a GPA of 4.39/4.5, researching the “Space Knowledge Information Process (SKIP) Tool for Multi-Dimensional Data Analysis.” They hold a BS in Computer and Information Sciences from PIEAS, Islamabad, Pakistan (Mar. 2007 – Aug. 2011) with a GPA of 3.01/4.0, where they worked on “Medical Image Segmentation using Statistical and Transform Methods.” 🎓💻📊🖼️

Experience

Currently a Staff Research Scientist at IBM Research in Abu Dhabi, UAE (Oct. 2023 – Present), where I specialize in remote sensing applications and develop Visual-Language foundation models for remote sensing. Previously, He was a Research Associate at the Intelligent Visual Analytics Lab (IVAL) at MBZUAI (Aug. 2021 – Oct. 2023), focusing on computer vision tasks, writing research proposals, supervising CV701 labs, and co-supervising MS and Ph.D. students. I also served as a Senior AI Software Engineer at Tricubics, Seoul (Mar. 2021 – Jul. 2021), leading AI and computer vision R&D for AI-based unmanned stores, and as a part-time CTO at Ujura (CATMOS), Seoul (Mar. 2021 -May. 2021), developing AI health information systems for pets, especially cats. 🛰️📊🐱

Noteable Projects

Mustansar Fiaz, a researcher and developer at MBZUAI in Abu Dhabi since August 2021, specializes in remote sensing, medical image segmentation, and person search. His work in remote sensing involves developing AI-based algorithms to detect semantic changes in man-made facilities while ignoring noisy changes. In medical image segmentation, he creates CNN and transformer-based models for 2D and 3D segmentation tasks, including cell, multi-organ, polyp, and cardiac MRI scans. For person search, he develops methods to detect and localize individuals in images from multiple cameras. Previously, he worked at KNU in Daegu, South Korea, focusing on video object tracking and segmentation using deep learning techniques. 📊📸🏥🛰️

Awards

Recipient of the Best Presentation Award and Best Paper Award at IW-FCV 2022 🏆, with additional accolades including the Best Student Paper Award at IW-FCV 2020 and the Outstanding Research CSE Thesis Award in 2021 🎓. Honored with the ACM SIGAPP Student Travel Award for ACM SAC 2019 ✈️. Served as Joint Secretary and Secretary Information for PSAK between 2017-2019 📜. Awarded the KNU International Graduate Scholarship (KINGS) for PhD studies (2016-2021) 🎓, Sejong University’s fully funded MS scholarship (2014-2016) 📚, and PIEAS Fellowship for BS studies (2007-2011) 🏅.

Research focus

Based on the publications provided, the research focus of this person centers on visual object tracking and remote sensing. They are involved in developing advanced methods for tracking objects in noisy and cluttered environments, including techniques like channel-spatial attention learning and deep Siamese networks. Their work also explores Medical image segmentation and gender identification using signal processing techniques. Recent studies highlight the use of transformers and Siamese networks for robust tracking and change detection in remote sensing. The emphasis is on improving tracking accuracy and robustness through novel algorithms and model architectures. 🌟📊🔍📷