جست‌وجوی استاد و پژوهشگر

Hassan Rivazمشاهده پروفایل

استاد

• Medical image analysis
• Deep learning
• Machine learning
• +۵ مورد دیگر

Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.

Massimo Mischiمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Diagnostic Imaging
• Ultrasound Technology
• +۴ مورد دیگر

Massimo Mischi is a Full Professor at the Faculty of Electrical Engineering of the Eindhoven University of Technology (TU/e) and chairs the Signal Processing Systems (SPS) Division , the largest division at TU/e with over 250 researchers. He founded the Biomedical Diagnostics (BM/d) Lab in 2012, which now includes 180 researchers and clinical/industrial advisors, focusing on biomedical signal processing for diagnostics and monitoring.

Maciej A Mazurowskiمشاهده پروفایل

دانشیار

• Medical Imaging
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Maciej A Mazurowski is an Associate Professor at Duke University School of Medicine, with dual appointments in the Department of Biostatistics & Bioinformatics and Radiology. He is also affiliated with the Department of Electrical and Computer Engineering and is a member of the Duke Cancer Institute. His research focuses on applying machine learning to medical imaging for improved diagnosis and treatment. Ph.D. in Computer Science from the University of Louisville (2008) Dr. Mazurowski's research emphasizes medical imaging , machine learning , and computer vision applications in radiology. His work includes automated segmentation , domain adaptation , prognostic modeling , and foundation models for MRI/CT analysis. His recent publications highlight trends in universal segmentation models (SegmentAnyBone, SegmentAnyMuscle), foundation models for MRI (MRI-CORE), and AI-driven diagnostic tools for breast cancer, glioblastoma, and thyroid nodules. Key challenges addressed include domain generalization , image harmonization , and ethical considerations in clinical AI. Incubation Award for innovative research commercialization Dr. Mazurowski has secured significant research funding from agencies including the National Institutes of Health , National Institute of Biomedical Imaging and Bioengineering , and American Roentgen Ray Society . His work spans CT segmentation , MRI analysis , and AI-based quality assessment across multiple imaging modalities.

• Medical Image Analysis
• Machine Learning in Medical Imaging
• Neuroimaging
• +۵ مورد دیگر

Lawrence Staib is Professor of Radiology and Biomedical Imaging, Biomedical Engineering, and Electrical Engineering at Yale University. He serves as Director of Undergraduate Studies in Biomedical Engineering and is a member of Yale's Bioimaging Sciences division, Image Processing & Analysis Group, Yale Biomedical Imaging Institute, and Yale-BI Biomedical Data Science Fellowship program. Dr. Staib earned his A.B. in Physics from Cornell University (1982), followed by a Ph.D. in Engineering and Applied Science from Yale University (1990), and completed a postdoctoral fellowship at Yale School of Medicine (1991). His research focuses on developing advanced medical image analysis methods using machine learning and model-based approaches. Key research areas include neuroimaging applications for autism spectrum disorder classification, cardiac imaging analysis for strain and motion assessment, prostate cancer diagnosis and risk mapping, and innovative techniques for medical image segmentation with limited labeled data. Dr. Staib's work emphasizes uncertainty estimation in deep learning models, multi-modal image registration, and domain adaptation techniques to improve clinical decision support systems. His recent publications demonstrate a strong trend toward developing interpretable AI models for clinical applications, with particular emphasis on fMRI analysis for neurological conditions, cardiac motion analysis, and prostate cancer diagnosis. His work frequently addresses the challenge of limited labeled data in medical imaging through innovative self-supervised, semi-supervised, and few-shot learning approaches. Fellow of the American Institute for Medical and Biological Engineering (AIMBE) (2015) Distinguished Investigator Award from the Academy for Radiology & Biomedical Imaging Research (2017) MICCAI Fellow (2022) Medical Image Analysis Second Best MICCAI Paper Award (2005) ASNR Cum Laude Scientific Exhibit Award (2003) Dr. Staib serves on the editorial board of Medical Image Analysis and as Associate Editor of IEEE Transactions on Biomedical Engineering. His research is supported by NIH grants including the Autism Center of Excellence program. He leads the Image Processing & Analysis Group within Yale's Bioimaging Sciences division, collaborating extensively with James Duncan, John Onofrey, Xenophon Papademetris, and other Yale researchers on applications spanning neuroimaging, cardiology, and oncology. Current projects focus on developing robust AI models for clinical decision support with emphasis on uncertainty quantification and interpretability.

Zhi-Pei Liangمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Medical Imaging
• Biomedical Engineering
• +۵ مورد دیگر

Zhi-Pei Liang is the Franklin W. Woeltge Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, with joint appointments in the Department of Bioengineering, Beckman Institute for Advanced Science and Technology, and Coordinated Science Laboratory. His research spans biomedical engineering, medical imaging, and signal processing with a focus on advancing magnetic resonance imaging and spectroscopy technologies. His educational background includes a Ph.D. in Biomedical Engineering from Case Western Reserve University (1989) and a B.S. in Electrical Engineering from South-China University of Technology (1982), followed by postdoctoral training at UIUC (1989-1991). Professor Liang's research interests center on magnetic resonance imaging and spectroscopy , with particular emphasis on ultrafast imaging techniques , model-based reconstruction methods , and the integration of physics-based modeling with machine learning . His pioneering work on SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) has revolutionized high-resolution metabolic brain imaging by enabling label-free molecular imaging through the marriage of spin physics and machine learning. His research spans pattern recognition, parameter estimation, image formation theory, and algorithms for medical imaging applications. Analysis of his recent publications reveals a strong focus on high-resolution metabolic imaging , particularly using SPICE methodology to map brain metabolism with unprecedented detail. His work bridges fundamental physics of magnetic resonance with advanced computational methods to overcome traditional limitations in imaging speed and resolution. Current research directions include J-resolved spectroscopic imaging, deuterium-based metabolic mapping, and multimodal integration of PET and MRSI for studying neurological disorders. Elected to International Academy of Medical and Biological Engineering (2012) Gold Medal, International Society for Magnetic Resonance in Medicine (2022) Technical Achievement Award, IEEE Engineering in Medicine and Biology Society (2014) Fellow, National Academy of Inventors (2021) Author of influential book 'Principles of Magnetic Resonance Imaging' (1999) President of IEEE Engineering in Medicine and Biology Society (2011-2012) Professor Liang has advised numerous students and postdocs in biomedical imaging research and has received multiple teaching honors including the Ronald W. Pratt Outstanding Teaching Award (2005) and multiple listings among UIUC's Excellent Teachers. His research has been supported by various grants from NIH, NSF, and other funding agencies. He leads the SPICE (Spectroscopic Imaging by exploiting spatiospectral Correlation) research group which focuses on developing novel imaging techniques that combine physics-based modeling with machine learning for ultrafast metabolic imaging. His laboratory, part of the Beckman Institute's Integrative Imaging Theme, collaborates extensively with clinical researchers at Carle Illinois College of Medicine and other institutions to translate advanced imaging techniques into clinical applications for neurological disorders, cancer, and metabolic diseases. Current projects focus on high-resolution mapping of brain metabolism in Alzheimer's disease, stroke, and brain tumors using novel MR spectroscopic imaging techniques.

• Inverse Problems
• Nonlinear Acoustics
• Medical Imaging
• +۵ مورد دیگر

Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.

Travis B. Thompsonمشاهده پروفایل

استادیار

• Mathematical Modeling
• Scientific Computing
• Machine Learning in Medicine
• +۵ مورد دیگر

Travis B. Thompson, Ph.D. is an Assistant Professor in the Department of Mathematics and Statistics at Texas Tech University, leading the TM4 (Texas Tech Translational and Theoretical Mathematical Modeling and Machine Learning in Medicine) research group. His academic journey includes postdoctoral work at Rice University, Simula Research Laboratory, and the University of Oxford, focusing on mathematics applied to neurodegenerative diseases. Education: Ph.D. in Mathematics from Texas A&M University (2013) Dr. Thompson develops theoretical mathematical models and applies scientific computing and machine learning to study neurological pathologies, particularly Alzheimer’s disease. His work explores complex biological processes on networks, translational healthcare applications, and nutritional security implications. Current research trends integrate neuroimaging data with finite element simulations to model tau progression , amyloid beta dynamics , and glymphatic clearance in age-related diseases. Scientific awards and honors were not explicitly mentioned in the provided materials. Dr. Thompson’s interdisciplinary approach connects computational neuroscience with biomedical engineering , utilizing techniques like diffusion tensor imaging and level set methods to analyze pathological protein spread and brain tissue mechanics . The TM4 research group focuses on network neurodegeneration , personalized medicine , and machine learning diagnostics . Their work spans from microfluidic cancer detection to computational modeling of brain clearance mechanisms , addressing challenges in both neurodegenerative diseases and biomedical engineering through rigorous mathematical frameworks.

Dana Cobzasمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Imaging
• Image Segmentation
• +۷ مورد دیگر

Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .

• Machine Learning
• Biomedical Engineering
• Medical Imaging
• +۵ مورد دیگر

Professor Anil Bharath holds the role of Professor of Biologically-Inspired Computation & Inference in the Department of Bioengineering at Imperial College London. He serves as Academic Director of Imperial Global: Singapore and co-leads the IN-CYPHER research program on AI-driven healthcare security. His research focuses on machine learning, deep networks, and biomedical applications. He earned his BEng from UCL and PhD from Imperial College, followed by roles including President of the City and Guilds College Association (2022-2024). Research Interests: Machine learning, neural networks, medical imaging, and biologically-inspired computation. Notable contributions include 2D steerable filters for shape detection (1998), Bayesian marginalisation in early computer vision, and deep learning for cardiac MRI analysis. He co-founded Cortexica Vision Systems (acquired by Zebra Technologies in 2019), applying biological neuron models to visual search technology. Labs & Affiliations: Director of the BICI Lab, affiliated with the Data Science Institute, Centre for Neurotechnology, and multiple healthcare networks. His work spans interdisciplinary projects in AI for healthcare, cardiovascular engineering, and medical device innovation. Publications: Recent work emphasizes AI in cardiology (e.g., MRI analysis for mitral regurgitation, aortic stenosis detection) and synthetic data privacy. His research bridges computational models with clinical interpretability, addressing challenges in medical imaging efficiency and diagnostic accuracy.

• Medical Physics
• Radiation Oncology
• Artificial Intelligence in Medical Imaging
• +۳ مورد دیگر

Professor Annette Haworth is a distinguished academic at the University of Sydney, serving as Director of the Institute of Medical Physics and Course Director for the Master of Medical Physics program. She holds conjoint roles at Westmead and Blacktown Hospitals in radiation oncology medical physics. With over 25 years of clinical experience, her work bridges medical physics, radiation oncology, and AI-driven treatment optimization. Her research focuses on biologically targeted radiation therapy, particularly prostate cancer, using advanced imaging biomarkers and AI to customize dose delivery. She leads the BiRT project ( https://birt.sydney.edu.au/ ), which develops imaging biomarkers for tumor characterization and biological treatment planning across multiple cancer sites. PhD, University of Western Australia MSc, University of Western Australia BSc(Hons), University of Leeds Her recent publications (2023-2025) explore heterogeneous dose prescriptions, radiomic stability, and AI-enhanced segmentation for radiotherapy. Key trends include integrating quantitative MRI, PET imaging, and machine learning to personalize radiation doses while minimizing healthy tissue exposure. Scientific awards: 2021 University of Sydney DVCR Cancer Gift 2015 Judith Stitt Award (American Brachytherapy Society) 2007 TROG Outstanding Achievement Award Life Member, TROG (2018) Fellow, Australasian College of Physical Scientists in Medicine Prof Haworth supervises a multidisciplinary team of physicists, computer scientists, and clinicians, with active PhD students at the University of Sydney, Auckland, and Western Australia. She has secured competitive grants, including an NHMRC project grant (2016) for BiRT development and PdCCRS funding (2010-2014) for prostate cancer bioeffect models.

• Medical Image Analysis
• Artificial Intelligence
• Healthcare
• +۹ مورد دیگر

Cherise Chen is an Assistant Professor in Computer Vision at the Department of Computer Science, University of Sheffield. She also holds positions as a Visiting Researcher in the Oxford BioMedIA Group at the University of Oxford and an Honorary Research Fellow at Imperial College London. As a core member of the Insigeno Institute and Shef.AI community, Dr. Chen leads research at the intersection of artificial intelligence and healthcare, focusing on translating cutting-edge AI techniques into practical medical applications. Dr. Chen's research program centers on developing robust, data-efficient machine learning algorithms for medical image analysis. Her work spans adversarial data augmentation, robust machine learning frameworks, and data-efficient learning techniques including self-supervised, few-shot, and semi-supervised approaches. She has made significant contributions to multi-task and multi-modal learning, adaptive machine learning systems, and algorithms with built-in considerations for fairness, privacy, robustness, and interpretability. Her research specifically targets clinical applications in cardiac image analysis (including segmentation, registration, and shape remodeling with quality control), prostate image analysis integrated with pathological image analysis, and brain image segmentation for clinical use cases. Analysis of Dr. Chen's recent publications reveals a strong focus on addressing the practical challenges of deploying AI in real-world medical settings. Her work on test-time adaptation methods (2023), adversarial style composition (2022), and cooperative training frameworks (2021) demonstrates her commitment to creating AI systems that maintain performance despite domain shifts and limited labeled data. She has consistently contributed to top-tier medical imaging conferences, with multiple papers accepted to MICCAI from 2018-2023, reflecting her standing in the medical imaging research community. IEEE TMI Gold-level Distinguished Reviewer Award (2022-2023) MICCAI 2023 Outstanding Reviewer Award Winner of the Fetal Tissue Annotation and Segmentation Challenge (FeTA) 2022 Winner of the Multi-sequence Cardiac MR Segmentation Challenge 2019 China National Scholarships (twice, top 0.2%) Dr. Chen actively mentors students and has delivered invited talks at prestigious institutions including Johns Hopkins University, Technical University of Munich, and the German Cancer Research Center. Her laboratory at Sheffield focuses on advancing deep medical image segmentation with particular attention to robustness, reliability, and real-world applicability in clinical workflows.

Elizabeth Neumannمشاهده پروفایل

استادیار

• Neurological diseases
• Molecular imaging
• Mass spectrometry
• +۴ مورد دیگر

Elizabeth Neumann is an Assistant Professor in the Department of Chemistry at the University of California, Davis, where her lab focuses on the molecular and cellular architecture of neurological diseases using advanced analytical tools. Her research integrates matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) with multimodal technologies like immunofluorescence, spectroscopy, and transcriptomics to decode complex biological systems. Education: BS in Chemistry from Baylor University, PhD from University of Illinois at Urbana-Champaign under Jonathan Sweedler Postdoctoral training: NIH Postdoctoral Fellow at Vanderbilt University under Richard Caprioli and Jeffrey Spraggins The Neumann lab specializes in developing high-resolution imaging methods for spatial lipidomics and metabolomics, with applications spanning neuroscience, nephrology, and electrochemistry. Their work enables non-destructive molecular profiling of tissues while preserving spatial context. Recent publications emphasize MALDI TIMS IMS for lipid distribution analysis, multimodal integration with CODEX immunofluorescence, and innovative approaches in single-cell chemical analysis. Collaborative efforts include contributions to the NIH Human Biomolecular Atlas Program. Scientific awards: NIH/NIDDK F32, NSF Graduate Research Fellowship, multiple institutional scholarships Leadership: Active in developing community guidelines for antibody-based imaging and FAIR data stewardship Research collaborations span Vanderbilt, University of Illinois, and cross-institutional consortia, with a focus on advancing analytical methods for biomedical discovery.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

G. Allan Johnsonمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Biomedical Technology
• Neuroimaging
• +۵ مورد دیگر

G. Allan Johnson is the Charles E. Putman University Distinguished Professor of Radiology at Duke University, with concurrent appointments in Physics (Trinity College of Arts & Sciences) and Biomedical Engineering (Pratt School of Engineering). He serves as Director of the Duke Center for In Vivo Microscopy , an NIH/NIBIB national Biomedical Technology Resource Center focused on developing preclinical imaging technologies and applying them to critical biomedical questions. Ph.D. in Radiology from Duke University (1974) Current leadership in preclinical MRI and connectomics His research spans high-resolution magnetic resonance imaging , diffusion MRI , and structural connectomics , with applications to neurodegenerative diseases , brain development , and biomedical engineering . Recent work includes creating multicontrast MR atlases for rodent brains and pioneering ex vivo MRI-histology fusion techniques . Selected publications reveal expertise in diffusion tensor imaging , tractography , and multimodal imaging workflows for both preclinical and clinical applications . Grants include leadership in projects funded by the CHDI Foundation (Huntington's disease), University of Pittsburgh (connectome atlases), and University of Tennessee Health Science Center (Alzheimer's imaging genetics). Scientific recognition includes NIH/NIBIB Resource Center Leadership Charles E. Putman University Distinguished Professorship His team at the Duke Center for In Vivo Microscopy develops multiparametric imaging platforms , 4D cardiac micro-CT atlases , and quantitative susceptibility mapping methods for applications ranging from neurotoxicology to plant root imaging .

• Biomedical Engineering
• Biomedical Image Processing
• Medical Physics
• +۵ مورد دیگر

Dr. PELİN ÇİRİŞ is an Assistant Professor in the Department of Biomedical Engineering at the Faculty of Engineering, Akdeniz University, Antalya, Turkey. She also serves as the Director of the Biomedical Technologies Application and Research Center and Deputy Director of the Science and Technology Application and Research Center at Akdeniz University. Previously, she served as Head of Department for Biomedical Engineering from 2014-2020 and continues as Deputy Head since 2016. Her academic career includes significant postdoctoral training at Harvard Medical School and Yale University. Education: Post Doctorate, Harvard University, Harvard Medical School, Brigham And Women's Hospital, Radiology (2013-2015) Post Doctorate, Yale University, School Of Medicine, Diagnostic Radiology (2012-2013) Doctorate, Yale University, School Of Engineering And Applied Science, Biomedical Engineering (2008-2012) Masters (Non-Thesis), Yale University, School Of Engineering & Applied Science, Engineering And Applied Science (2008-2010) Postgraduate, Johns Hopkins University, School Of Engineering, Biomedical Engineering (1998-2000) Undergraduate, Purdue University, School Of Electrical And Computer Engineering, Electrical Engineering (1994-1998) Dr. Çiriş's research spans multiple domains of biomedical imaging and engineering, with a strong focus on magnetic resonance imaging (MRI) techniques and applications. Her work encompasses advanced MRI physics, quantitative imaging methods, and clinical applications across various medical fields. She has made significant contributions to signal modeling in MRI, particularly in cancer imaging contexts including prostate and cervical cancers. Her research also extends to neuroimaging for brain tumor visualization and white matter tractography, as well as cardiac imaging applications for tissue characterization and functional assessment. She has developed innovative approaches like the GAME (Gaussian Augmentation of the Monoexponential) model for analyzing MRI signal decays in cancer tissues. The trajectory of Dr. Çiriş's publication record reveals a progression from fundamental MRI techniques to sophisticated clinical applications. Her early work focused on image reconstruction, parallel imaging, and real-time interventional guidance. Over time, her research evolved to emphasize quantitative approaches for tissue characterization in oncology, particularly in gynecologic and prostate cancers. Recent publications demonstrate expertise in advanced modeling techniques for MRI signal decays, thermometry applications, and image processing methods for clinical decision support. This progression reflects a career dedicated to bridging engineering innovation with practical clinical applications across multiple medical specialties. Scientific Awards: Magna Cum Laude Award (Gynecological cancers), International Society for Magnetic Resonance in Medicine (2015) Magna Cum Laude Award (Prostate cancer), International Society for Magnetic Resonance in Medicine (2015) PhD Candidacy Awarded With Distinction, Yale University (2012) Travel Grant for Top 25 Papers, IEEE International Symposium on Biomedical Imaging (2007) Melvin Judkins Young Investigator Award, American Heart Association (2007) Academic Honors and Dean's Lists, Purdue University (1998) Dr. Çiriş has secured research funding including the project 'Assessing validity of MRI T2* hypoxia measurement in cervical cancer' (2014-2015). She serves as an Editor for the Journal of Magnetic Resonance Imaging since 2015, demonstrating her standing in the MRI research community. She has contributed to academic governance as a jury member for Assistant Professorship Appointment Committee at Akdeniz University. Her leadership extends to directing research centers focused on biomedical technologies at Akdeniz University, where she oversees interdisciplinary teams working on medical imaging technologies. As Director of the Biomedical Technologies Application and Research Center and Deputy Director of the Science and Technology Application and Research Center, Dr. Çiriş leads interdisciplinary teams focused on advancing medical imaging technologies and their clinical translation. Her centers foster collaboration between engineers, physicists, clinicians, and computer scientists to develop innovative solutions for diagnostic and therapeutic challenges in medicine, particularly in the areas of cancer imaging, neuroimaging, and cardiac applications.