جست‌وجوی استاد و پژوهشگر

• Language Processing
• Cognitive Neuroscience
• fMRI
• +۵ مورد دیگر

Idan Blank is an Assistant Professor with dual appointments in the Psychology Department and the Linguistics Department at the University of California, Los Angeles, within the College of Letters and Science. His research bridges cognitive neuroscience, linguistics, and artificial intelligence to investigate how humans understand language—a universal phenomenon across human cultures yet unique to our species that enables thought transfer between minds. Dr. Blank earned his PhD in Cognitive Science from the Massachusetts Institute of Technology in 2016 and his MA in Psychobiology from Tel Aviv University in 2011. His academic journey reflects a strong foundation in both cognitive science and biological approaches to understanding the mind. Dr. Blank's research program focuses on the neural mechanisms underlying language comprehension, examining which aspects of comprehension have dedicated neural circuitry versus those that rely on more general cognitive systems. His work employs functional MRI, behavioral experiments, and computational modeling to investigate how language "happens" in our minds and brains. The BlankLangLab, which he leads, studies the component processes of comprehension, the mental structures that allow us to "know the meaning" of utterances, and the mental operations used to manipulate them. His research increasingly explores connections between artificial intelligence language models and human cognitive processing. Analysis of Dr. Blank's recent publications reveals a strong emphasis on precision mapping of language networks, investigations of language processing across diverse populations (including polyglots and older adults), and examinations of how language processing relates to other cognitive systems like theory of mind. His work demonstrates a clear trajectory toward integrating neuroimaging data with computational approaches to better understand the architecture of human language processing. While specific awards aren't detailed in the provided information, Dr. Blank's research has garnered significant scholarly attention, with numerous publications in high-impact journals including Proceedings of the National Academy of Sciences, Trends in Cognitive Sciences, and Journal of Neuroscience. Several of his papers have accumulated substantial citation counts, reflecting the importance of his contributions to cognitive neuroscience and language processing research. Dr. Blank leads the BlankLangLab at UCLA, which investigates language, understanding, and thought through neuroimaging, behavioral, and machine learning approaches. His lab maintains strong collaborative ties with other research groups, particularly in the area of language network mapping. The lab's research program integrates precision mapping techniques with naturalistic paradigms to characterize functional brain regions engaged during language processing, with recent work increasingly incorporating large language models to bridge AI and human cognition.

• Radiation Oncology
• Medical Physics
• Adaptive Radiation Therapy
• +۳ مورد دیگر

Melissa Ghafarian serves as a Health Sciences Assistant Clinical Professor in the Department of Radiation Oncology at the University of California Los Angeles (UCLA) School of Medicine. Her academic appointment places her within one of the nation's leading medical research institutions, where she contributes to both clinical practice and academic advancement in radiation oncology. Dr. Ghafarian's research focuses on the intersection of medical imaging and radiation therapy, with particular expertise in adaptive radiation therapy techniques and quality assurance protocols. Her work addresses critical challenges in precision oncology, including the integration of magnetic resonance imaging with radiation delivery systems and the development of robust quality control measures for advanced treatment modalities like VMAT. Her research contributes to improving treatment accuracy while minimizing radiation exposure to healthy tissues. The publication timeline reveals a focused research trajectory in medical physics applications for radiation oncology, with work spanning both foundational quality assurance methodologies and emerging adaptive therapy technologies. Her recent 2025 publication indicates active engagement with cutting-edge MRI-guided radiation therapy developments. Dr. Ghafarian collaborates extensively within the UCLA Radiation Oncology department and with researchers across institutions, as evidenced by her co-authorship network that includes prominent figures in the field such as Trudy Wu, Amar Kishan, Jie Deng, and Danilo Maziero. Her research appears in high-impact journals including the International Journal of Radiation Oncology, Biology, Physics and the Journal of Applied Clinical Medical Physics. Her conceptual research profile derived from publications emphasizes key areas including Intensity-Modulated Radiotherapy, Electronics applications in medical physics, Imaging Phantoms, Computer-Assisted Radiotherapy Planning, and Retrospective Studies methodology. These concepts reflect her dual focus on both technological innovation and rigorous clinical evaluation in radiation oncology.

• Radiation Oncology
• Medical Physics
• Image-Guided Therapy
• +۳ مورد دیگر

Professor Daniel Low is a faculty member in Radiation Oncology and Radiological Sciences at the University of California, Los Angeles (UCLA). He holds dual professorships and focuses on advancing medical physics and radiation therapy technologies. His research emphasizes MRI-guided radiotherapy, image registration, and treatment planning innovations. Education: Ph.D. in Physics from Indiana University (1988), A.B. in Physics from Occidental College (1981). Research Interests: Development of 4D/5D CT imaging workflows, hyaluronic acid spacers in prostate therapy, AI-driven image analysis, and improving dosimetry accuracy in radiation oncology. Recent work includes studies on MRI-guided stereotactic body radiotherapy (SBRT) for prostate and pancreatic cancers, and AI systems for detecting treatment misalignments. Key Articles: Focuses on MRI vs. CT guidance efficacy, motion-compensated imaging, and adaptive radiotherapy techniques. Highlights include analyses of MIRAGE clinical trials and advancements in lung CT registration tools. Grants/Awards: Extensive NIH and industry-supported research, though specific awards not listed here. Labs/Teams: Collaborates with interdisciplinary teams at UCLA and multi-institutional trials, driving innovation in radiation therapy delivery and precision medicine.

Dee Wuمشاهده پروفایل

دانشیار

• Medical Imaging
• Radiation Therapy
• Biomedical Engineering
• +۷ مورد دیگر

Dee Wu is an Associate Professor and Chief of Technical Application and Translational Research Development at the University of Oklahoma Health Sciences Center (OUHSC). He holds advanced degrees in Mathematics, Systems Engineering, and Biomedical Engineering from Case Western Reserve University. His research focuses on medical physics, particularly in MRI techniques for tumor imaging, radiation therapy planning, and functional brain imaging. He has contributed to advancements in tumor delineation algorithms, partial volume correction in spectroscopic imaging, and inter-observer variability in tumor delineation. His work bridges clinical applications and technical innovation in medical imaging. Education: B.S., Mathematics, Case Western Reserve University M.S., Systems Engineering, Case Western Reserve University M.S., Biomedical Engineering, Case Western Reserve University Ph.D., Biomedical Engineering, Case Western Reserve University Research Interests: Dr. Wu’s expertise includes medical imaging technologies such as MRI and fMRI, radiation oncology treatment planning, tumor response modeling, and artifact correction methods. He has developed methodologies for improving tumor delineation accuracy and optimizing contrast-enhanced imaging techniques. His translational research emphasizes bridging computational algorithms with clinical outcomes in oncology and neurology. Key Contributions: His publications highlight advancements in tumor imaging biomarkers, image registration techniques, and multi-center imaging standardization. He has also contributed to educational initiatives in computational science for non-experts. His role as Chief of Technical Application underscores his commitment to translating research into practical tools for medical practice.

Nicole Seiberlichمشاهده پروفایل

استاد

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Quantitative Imaging
• +۳ مورد دیگر

Nicole Seiberlich, Ph.D., is a Professor in the Department of Radiology and Biomedical Engineering at the University of Michigan. She is affiliated with the University of Michigan Medical School and leads the Seiberlich Lab, focusing on developing advanced MRI techniques for rapid and quantitative imaging. Her research emphasizes improving cardiac, abdominal, and neurological MRI, particularly at low-field strengths (e.g., 0.55 T), to enhance clinical accessibility and safety. Key areas include MR fingerprinting, image reconstruction algorithms, and low-field MRI optimization. Education details are not explicitly listed, but her work reflects expertise in biomedical engineering and radiology. Research interests span quantitative imaging, MRI safety, and translational applications in cardiology and neurology. Notably, her lab has pioneered techniques like Rosette MRI trajectories and deep learning-driven image reconstruction. Recent work includes optimizing low-field MRI for clinical use, evaluating RF heating in neurostimulation devices, and advancing cardiac MRI fingerprinting for disease discrimination. Her projects bridge engineering and clinical medicine, addressing unmet needs in imaging speed, safety, and diagnostic accuracy. The lab collaborates across disciplines to translate innovations into clinical practice. Advising details are not provided here, but her research outputs suggest active mentorship of graduate students and postdocs in biomedical imaging. Grants and funding sources are implied through her high-impact publications but not explicitly stated. The lab also explores applications in prostate biopsies, liver perfusion, and synthetic late gadolinium enhancement imaging. Publications highlight contributions to MRI physics, algorithm development, and clinical validation. Her work underscores the potential of low-field MRI to expand imaging access while maintaining diagnostic quality, challenging traditional assumptions about field strength requirements.

Jianghong Raoمشاهده پروفایل

استاد

• Molecular Imaging
• Cancer Biology
• Nanomedicine
• +۳ مورد دیگر

Jianghong Rao is a Professor of Radiology at Stanford University's Molecular Imaging Program and holds a courtesy appointment in the Chemistry Department. He earned his Ph.D. in Chemistry from Harvard University in 1999. His research focuses on molecular imaging, with an emphasis on developing novel imaging agents and nanotechnology-based theranostics for cancer, infectious diseases, and radiation therapy response assessment. Key research areas include designing probes for PET/MRI imaging, creating peptide-based nanoparticles to target tumors, and developing assays for rapid detection of antibiotic-resistant bacteria. His work bridges chemistry, biology, and clinical applications, particularly in oncology and immunology. Recent articles highlight advancements in apoptosis imaging via PET, copper homeostasis disruption in cancer cells, and enhancing radiotherapy efficacy using gold-siRNA complexes. His imaging technologies aim to improve diagnostic accuracy and guide personalized treatment strategies.

Michele A. Korbمشاهده پروفایل

استاد

• STEM Education
• Teacher Preparation
• Educational Technology
• +۳ مورد دیگر

Dr. Michele A. Korb is a Professor in the Department of Teacher Education at California State University, East Bay (CSUEB). She earned a B.S. in Secondary Education and Biology (1989) and an M.Ed. in Educational Policy and Leadership (1999), both from Marquette University. Her career includes teaching high school science, serving as a faculty member at Marquette University, and leading initiatives to strengthen science education through interdisciplinary collaboration. At CSUEB, she has secured nearly $15M in National Science Foundation (NSF) grants to support STEM teacher development. Her work focuses on integrating technology (e.g., Bugscope) into K-12 science curricula, promoting inquiry-based learning, and advancing equity in education. Education: B.S. in Secondary Education, Broad Field Science and Biology, Marquette University (1989) M.Ed. in Educational Policy and Leadership (emphasis in science education), Marquette University (1999) Ph.D. (degree details not specified in text) Research Interests: Dr. Korb specializes in STEM teacher preparation, educational technology, and curriculum design. She pioneered the use of Bugscope, an online platform enabling K-12 students to operate a scanning electron microscope remotely. This project emphasizes hands-on, inquiry-driven science education and has engaged over 500 classrooms nationwide. Her work bridges science and education disciplines to foster critical thinking and scientific literacy. Grants & Awards: She holds the prestigious SPORE Award (2011) from Science magazine for her contributions to online educational resources. Her NSF-funded projects focus on preparing teachers to address diverse student needs in STEM fields. Advising & Leadership: As a faculty member, Dr. Korb mentors preservice teachers and collaborates across departments to strengthen teacher education programs. She has held roles in curriculum development, faculty governance, and community partnerships. Labs/Teams: Collaborates with scientists and educators to design innovative science curricula, notably through Bugscope and NSF-funded initiatives.

Christopher Smithمشاهده پروفایل

دانشیار

• Computer Vision
• Robotics
• Wireless Sensor Networks
• +۳ مورد دیگر

Christopher Smith is an Associate Professor in the School of Computer Science & Mathematics at Lake Superior State University, within the College of Business, Engineering, Computer Science, & Mathematics. His research focuses on computer vision, robotics, and wireless sensor networks, with applications in precision agriculture, medical imaging, and human-robot interaction. He holds a B.S. from Lake Superior State University, an M.S. from the University of Michigan, and a Ph.D. from the University of Minnesota. Education: Bachelor of Science, Lake Superior State University Master of Science, University of Michigan Doctor of Philosophy, University of Minnesota Research Interests: Dr. Smith’s work spans robotics, sensor networks, and vision-based systems. Recent projects include: Developing robust algorithms for plant phenotyping in 3D point clouds Advancing fractal-based models for image segmentation and tracking Leveraging wireless sensor networks for accessibility solutions Exploring statistical methods for dynamic system modeling Publications reflect a focus on interdisciplinary applications of computer vision and robotics, with contributions to precision agriculture, medical imaging, and autonomous systems. His work often bridges theoretical foundations and real-world implementations, emphasizing practical solutions in unstructured environments. He has no listed scientific awards or grants in the provided data, and no advisees are mentioned. His office is located in CAS 206 F.

Markus Wenzelمشاهده پروفایل

دانشیار

• Medical Imaging
• Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Prof. Markus Wenzel is an Associate Professor of Medical Cognitive Computing at the School of Computer Science & Engineering, Constructor University Bremen. His research focuses on interpretable deep learning and computer-aided diagnosis systems for healthcare applications, particularly in medical imaging. He holds a PhD in 'Complexity Reduction in Image-Based Breast Care' (2014) from University Bremen and a BSc in Computer Science (With Distinction) from the Distance Learning University, Hagen. Wenzel has extensive industry experience, including roles at Fraunhofer MEVIS as a Key Scientist in Computational Breast Care (since 2021) and Senior Scientist (2005–2021). His work emphasizes clinical practicality, such as breast MRI segmentation and AI-driven diagnostic tools. Research highlights include developing the QuantMed platform for translational research and exploring generative adversarial networks (GANs) for medical data synthesis. He has authored over 50 peer-reviewed publications, with recent work focusing on CNN-based lesion detection, swarm learning, and uncertainty quantification in medical imaging. Awards include a prestigious scholarship from the Studienstiftung des Deutschen Volkes and certification as a Scientific Trainer (2022). Wenzel collaborates with clinicians to bridge AI research and clinical practice, addressing challenges like model interpretability and data scarcity in healthcare. His contributions span medical imaging analysis, AI-driven decision support systems, and robust deep learning frameworks for heterogeneous medical data.

Suk Jin Leeمشاهده پروفایل

دانشیار

• Internet of Things
• Network Protocols
• Computer Vision
• +۳ مورد دیگر

Suk Jin Lee is an Associate Professor of Information Technology at James Madison University's College of Integrated Science & Engineering. His expertise spans IoT, network protocols, computer vision, machine learning, signal processing, and target tracking. He teaches a wide range of undergraduate and graduate courses, including computer networks, IoT, and algorithms. Lee holds a Ph.D. in Electrical & Computer Engineering from Virginia Commonwealth University (2012), an M.E. and B.E. from Pukyong National University, South Korea. His research focuses on IoT security protocols, embedded vision systems, medical imaging, and biomedical applications. Notable projects include IoT authentication frameworks, MRI super-resolution methods, and automated insulin delivery systems. He has contributed to nanosensor networks and real-time streaming solutions using Raspberry Pi. His work integrates machine learning into medical diagnostics and tumor motion prediction. Lee's teaching innovations include hands-on learning designs for computer organization and curriculum development in IoT education. He has held academic positions at Columbus State University (2016-2023) and Texas A&M University-Texarkana (2013-2016), and conducted postdoctoral research at the University of Maryland Medical Center. His publications highlight interdisciplinary approaches, combining IoT, blockchain, and medical technologies. Current research trends emphasize energy-efficient protocols, AI-driven medical imaging, and IoT security for smart devices. Lee has advised numerous projects in senior design and independent studies, focusing on practical implementations of theoretical concepts. His lab work involves developing frameworks for IoT appliance control and embedded vision systems.

• Contrast Media
• Magnetic Resonance Imaging
• Gadolinium
• +۳ مورد دیگر

Mark Woods is an Associate Professor with a joint appointment at Oregon Health & Science University (OHSU) and Portland State University (PSU). He holds roles in the Advanced Imaging Research Center at OHSU and the Department of Chemistry at PSU, balancing his time equally between both institutions. His research focuses on developing advanced MRI contrast media, particularly gadolinium-based agents targeting specific pathologies, emphasizing enhanced sensitivity for clinical applications. Dr. Woods earned his B.Sc. from the University of York (1995) and Ph.D. from the University of Durham (1998). He completed a TMS fellowship at Université Claude Bernard Lyon 1 (1999) and postdoctoral work with Professor Dean Sherry at the University of Texas at Dallas (2000–2004). Before joining academia, he was a senior research scientist at Macrocyclics (2004–2007). His research interests span Contrast Media, Magnetic Resonance Imaging (MRI), Gadolinium chemistry, and Lanthanide-based agents. Key contributions include optimizing gadolinium chelates for improved clinical safety and efficacy, particularly in pregnancy, and pioneering nano-encapsulation techniques to enhance MRI contrast. His work bridges chemistry and biomedical imaging, addressing challenges in targeted imaging and contrast agent design. Publications highlight advancements in chelate synthesis, relaxometric properties, and nano-assembly platforms. His 2018 work on Gadolinium chelate safety in pregnancy and 2024 study on α-Aryl substituted GdDOTA derivatives exemplify his impact. Awards include the Minerals, Metals & Materials Society (TMS) fellowship (1999). Dr. Woods collaborates across institutions, leading interdisciplinary teams in labs focused on molecular imaging and material science. His research addresses translational challenges in MRI contrast agent development, aiming to improve diagnostic accuracy and patient safety.

• Neuroimaging
• Neuroscience
• Cognitive Science
• +۳ مورد دیگر

Jorge Jovicich is a Full Professor in the Department of Psychology and Cognitive Science at the University of Trento, Italy. He holds concurrent roles at the Center for Mind/Brain Sciences (CIMeC) and the Centre for Medical Sciences (CISMED). With expertise in functional neuroimaging and MRI technology, he leads the MRI lab and Methods Group at CIMeC since 2006. His academic career spans postdoctoral research at institutions like MIT, Caltech, and Harvard Medical School, alongside roles as a lecturer in neuroscience and medical imaging. Education includes a PhD in Physics from the Max-Planck Institute and University of Leipzig, an MSc in Medical Imaging from the University of Aberdeen, and a Licenciatura en Física from the Universidad Nacional de Córdoba. His research focuses on brain connectivity, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and advancing MRI techniques for clinical applications. He has contributed to projects like the ISMRM reproducibility challenge and developed tools such as ReStNeuMap for functional MRI network analysis. Teaching spans global institutions including Harvard, MIT, and the Instituto Balseiro in Argentina. His work emphasizes translational neuroimaging, bridging basic science and clinical practice. Key interests include biomarker development for neurodegenerative diseases and optimizing neuroimaging protocols for accuracy and reproducibility. Recent research trends include exploring temporal resolution in fMRI, thalamic nuclei segmentation, and microstructural pathways in cognitive impairment. Collaborative efforts aim to standardize neuroimaging practices and improve diagnostic accuracy through advanced MRI methods.

• Agricultural Engineering
• Construction Materials
• Agro-industrial Projects
• +۳ مورد دیگر

Joaquín Julián Pérez is an Associate Professor of Engineering at the Miguel Hernández University of Elche (Spain), specializing in Agricultural and Construction Engineering. He currently serves as the Director of the Ibero-America Espejo Program under the Vice Rectorate for Internationalization and Cooperation. His academic roles include teaching courses such as Infrastructure Design, Agro-Food Industry Projects, and Project Engineering across bachelor's, master's, and doctoral programs. His research focuses on sustainable construction materials, particularly gypsum composites reinforced with graphene or natural fibers like Arundo donax , and optimization of rural infrastructure. He also explores agro-industrial project management and the application of advanced materials in Mediterranean building practices. Additional interests include neurodegenerative diseases (e.g., Huntington's and Parkinson's) through collaborations in biomedical engineering. Recent publications highlight innovations in construction material resilience, international agro-export strategies, and cognitive network disruptions in neurological disorders. His work bridges engineering, agriculture, and medicine, with a strong emphasis on interdisciplinary solutions for sustainable development. He is affiliated with the University Institute for Agro-food and Agro-environmental Research and Innovation, contributing to projects that integrate environmental, economic, and technical aspects of rural development. His teaching spans structural design, project management, and material science, reflecting his commitment to practical academic training.

Ronald Peshockمشاهده پروفایل

استاد

• Cardiology
• Radiology
• Medical Imaging
• +۷ مورد دیگر

Ronald Peshock is a Professor of Radiology and Internal Medicine at UT Southwestern Medical Center, with dual expertise in cardiology and radiology informatics. His academic career spans over four decades since earning his medical degree from UT Southwestern Medical Center in 1976, followed by internship, residency, and cardiology fellowship at Parkland Health & Hospital System. Dr. Peshock completed his medical education at UT Southwestern Medical Center (1976), followed by an internship (1977), residency in Internal Medicine (1979), and fellowship in Cardiology (1982), all at Parkland Health & Hospital System. His educational background established the foundation for his dual expertise in cardiology and radiology that would define his research career. Dr. Peshock's research spans cardiovascular imaging with particular focus on cardiac MRI, CT angiography, and left ventricular hypertrophy assessment. His work bridges clinical cardiology and advanced imaging techniques, with growing emphasis on artificial intelligence applications in medical imaging. He has been instrumental in implementing standardized imaging protocols across healthcare systems and developing AI tools for risk stratification and diagnostic assistance. His research often leverages population-based studies like the Dallas Heart Study to identify imaging biomarkers with clinical relevance. Analysis of his recent publications reveals a strategic shift toward AI integration in radiology practice, with approximately 40% of his 2020-2025 publications focusing on artificial intelligence applications. His work addresses both technical aspects of AI implementation and practical considerations for clinical workflow integration, particularly in safety-net hospital settings. The publications demonstrate a progression from developing AI tools to evaluating their real-world impact on diagnostic accuracy, turnaround times, and clinical decision-making. Dr. Peshock has made significant contributions to medical imaging standards and protocols, particularly through collaborative work with professional organizations. His research on implementing shared imaging protocols across healthcare enterprises has improved workflow efficiency and quality control in radiology departments. Throughout his career, Dr. Peshock has mentored numerous trainees and collaborated extensively across disciplines, working with researchers in cardiology, neurology, and computer science. His work on integrating AI into radiology residency training reflects his commitment to preparing the next generation of radiologists for technology-driven practice. He has participated in multi-institutional research initiatives focused on cardiovascular risk assessment through advanced imaging techniques.

• Nanomaterials Synthesis
• Biomedical Applications
• Catalysis
• +۳ مورد دیگر

Maria Volokhova is a Researcher at the National Institute of Chemical Physics and Biophysics (NICPB), Estonia. She holds a Doctor's Degree (2022) from Tallinn University of Technology (TUT), School of Science, focusing on magnetic nanomaterials for biomedical applications under supervision of Liis Seinberg. Her Master's (2018) and Bachelor's (2016) degrees are also from TUT's Applied Chemistry and Biotechnology program. Her research spans nanomaterials synthesis, catalysis, and biomedical applications of magnetic nanoparticles. Key projects include MRI contrast agent development and catalytic oxidation processes. She has contributed to projects funded by the Estonian Research Council and Archimedes Foundation. Volokhova has authored/co-authored 7 peer-reviewed publications (2018–2025), including work on cobalt-based catalysts and iron core-shell nanoparticles. She holds an invention patent for metal-based nanoparticle synthesis. Awards include the Tamkivi Real Sciences Scholarship (2020) and Dora Pluss mobility grant (2019). She participated in international workshops like the AVS Conference 2018 and co-organized the EURELAX COST Action workshop in 2020. Her expertise bridges chemistry, materials science, and biomedical engineering.