جست‌وجوی استاد و پژوهشگر

Janet Huangمشاهده پروفایل

استادیار

• Human-AI Interaction
• Creative AI
• Design of AI Systems
• +۳ مورد دیگر

Janet Huang serves as Assistant Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), where she contributes to the Future Everyday group. Her research pioneers human-AI co-learning frameworks that empower designers to harness AI as a creative material for solving complex real-world problems across diverse contexts. Academic Background: PhD in Computer Science, National Taiwan University (2018) MSc in Computer Science, National Taiwan University (2008) Research Focus: Dr. Huang designs novel toolkits bridging data sensemaking and decision-making support, with emphasis on creative AI applications and ethical human-AI collaboration . Her work spans healthcare data visualization, urban play systems, and memory augmentation tools, consistently exploring how AI can enhance human creativity while addressing societal implications. She employs participatory design methods to develop systems that balance technical innovation with user-centered values. Publication Trends: Recent articles (2023-2025) reveal three dominant trajectories: (1) generative AI for domain-specific problem-solving in nursing and biodesign, (2) playful urban exploration systems examining citizen-AI tensions, and (3) memory and decision-making support tools analyzing anthropomorphism effects. These works demonstrate her signature approach of embedding technical AI research within rich human contexts. Awards & Recognition: Ph.D. Thesis Honorable Mention, TAAI 2018 MOST Postdoctoral Scholarship (Taiwan) Mingler Scholarship 2023 Best Poster Honorable Mention, CSCW 2023 Academic Contributions: Dr. Huang teaches specialized courses including Creativity and Aesthetics of Data & AI and Digital Craftsmanship , while supervising 19 student projects. Her research receives support from competitive grants including the MOST scholarship, with applications spanning healthcare innovation and sustainable community development. She actively contributes to UN Sustainable Development Goals through human-centered AI solutions. Research Environment: As core member of TU/e's Future Everyday group, she collaborates with interdisciplinary teams spanning computer science, design, and domain experts. Her lab environment emphasizes iterative prototyping and real-world deployment, particularly in healthcare settings and urban contexts where AI systems interface with complex human practices.

Luca Caucciمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Optical Sciences
• Biomedical Imaging
• +۶ مورد دیگر

Luca Caucci is an Assistant Research Professor in Medical Imaging at the University of Arizona, affiliated with the Arizona Health Sciences Center (AHSC). His research integrates optical sciences and computational imaging to advance medical imaging technologies, particularly in nuclear medicine and 3D imaging . He has pioneered methods for photon-processing detectors and stochastic modeling in oncology and virology. Research Trends: His publications focus on positron emission tomography (PET) , machine learning for image reconstruction , and radiance sensor development . Key themes include GPU acceleration , list-mode data processing , and statistical decision theory in biomedical imaging. His work addresses challenges in 3D imaging , noise reduction , and task performance optimization . Collaborative Efforts: While the text does not explicitly mention lab teams or grants, his contributions to FastSPECT III calibration and alpha/beta emission tomography suggest active involvement in interdisciplinary projects bridging optical engineering and medical diagnostics .

Naomi Cheslerمشاهده پروفایل

استاد

• Cardiovascular Engineering
• Pulmonary Hypertension
• Vascular Biology
• +۵ مورد دیگر

Naomi Chesler, Ph.D., is the Chancellor’s Inclusive Excellence and Edwards Lifesciences Foundation Professor in the Department of Biomedical Engineering at the University of California, Irvine (UCI). She also directs the UCI Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center (CIRC). Her research integrates mechanical engineering, vascular biology, and imaging tools to study cardiovascular physiology, particularly focusing on right ventricular function, pulmonary hypertension, and sex differences in heart disease. Chesler’s work emphasizes translational applications, including stem cell-engineered vascular grafts and computational models predicting vascular dynamics. She has pioneered studies on pulmonary vascular distensibility as a clinical biomarker and has contributed to understanding how estrogen influences pulmonary vascular health. Chesler actively promotes diversity in academia, co-authoring papers on equitable hiring strategies and serving on national panels for cardiovascular research guidelines. Her research spans preclinical studies in rodents and macaques, human clinical trials involving veterans and preterm-born adults, and computational modeling of hemodynamics. Key innovations include defining phenotypic subgroups in heart failure with preserved ejection fraction (HFpEF) using RV-pulmonary artery coupling metrics and developing non-invasive methods to assess pulmonary vascular stiffness during exercise. Chesler’s lab collaborates across disciplines, combining biomechanical experiments with advanced imaging (MRI, PET) and machine learning for predictive analytics. Professional contributions include co-authoring NIH-funded grants, serving on editorial boards for Comprehensive Physiology and Frontiers in Bioengineering , and leading workshops on systemic approaches to health disparities. Her educational initiatives include a novel course integrating computational modeling with cardiovascular physiology instruction.

• Medical Imaging
• Positron Emission Tomography (PET)
• Time-of-Flight PET
• +۱۲ مورد دیگر

Katrin Herweg is a Research Scientist at the Institute of Imaging and Computer Vision (Chair of Imaging and Image Processing), RWTH Aachen University, Germany. She has been actively contributing to the advancement of high-resolution, time-of-flight positron emission tomography (PET) detector technologies since 2019. Her work is deeply rooted in the Department of Physics of Molecular Imaging Systems at the Institute for Experimental Molecular Imaging, and she continues her research in detector development with a strong focus on scintillator physics and photodetection systems. Master of Science in Physics, RWTH Aachen University (2017–2020), Focus: Experimental Particle Physics Bachelor of Science in Physics, RWTH Aachen University (2014–2017) Katrin's research centers on the development of PET detectors with ultra-high timing resolution. She investigates fast and ultra-fast light emission mechanisms in scintillator materials such as CsZnCl₃, LYSO:Ce,Ca, and BaF₂ through experimental measurements and simulations. Her work aims to push the boundaries of time-of-flight PET and CT by enabling sub-100 ps timing precision, which enhances image quality and quantification in nuclear medicine. She also explores novel photodetector technologies like μSiPMs and digital-analog SiPMs (DIGILOG) to overcome limitations of traditional materials like BGO. The recent publications highlight a consistent trend in advancing PET detector performance through innovations in scintillator characterization, ASIC-based readouts (e.g., TOFPET2c), high-frequency SiPM electronics, and modular, scalable detector designs with depth-of-interaction capability. Her work bridges fundamental physics with practical medical imaging applications, particularly in scatter rejection, organ-dedicated imaging, and axial field-of-view expansion. Scientific Awards: 3rd Place, IEEE Nuclear Science Symposium Student Paper Award (November 2023) Katrin has supervised multiple bachelor's theses on topics including deep learning-based position estimation in PET detectors, optical coupling materials, and scintillator coatings. She has served as an exercise and laboratory course instructor in nuclear medicine physics for both M.Sc. Biomedical Engineering and B.Sc. Electrical Engineering students. While no direct grant information is listed, her extensive publication record in high-impact journals and conferences suggests active participation in funded research projects. She collaborates closely with Prof. Volkmar Schulz and Dr. Stefan Gundacker, key figures in molecular imaging at RWTH Aachen. Katrin is a member of the research team at the Chair of Imaging and Image Processing (LFB), which focuses on advanced imaging systems, detector development, and image reconstruction algorithms. The team is part of a larger ecosystem at RWTH Aachen dedicated to experimental molecular imaging and biomedical engineering, contributing to platforms like Hyperion III for PET/MRI research.

Zhimei Renمشاهده پروفایل

استادیار

• Distribution-free inference
• Multiple hypothesis testing
• Data-driven decision making
• +۵ مورد دیگر

Zhimei Ren is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her Ph.D. in Statistics from Stanford University in 2021 under the supervision of Professor Emmanuel Candès and completed a postdoctoral fellowship at the University of Chicago from 2021-2023, advised by Professor Rina Foygel Barber. Prior to her doctoral studies, she earned a B.S. in Statistics from Peking University in 2016. Her educational background includes: Ph.D. in Statistics, Stanford University, 2021 (Advisor: Emmanuel Candès) B.S. in Statistics, Peking University, 2016 Dr. Ren's research focuses on developing distribution-free statistical methods with applications across various domains. Her work centers on distribution-free inference, multiple hypothesis testing, data-driven decision making, causal inference, and survival analysis. She has made significant contributions to conformal prediction frameworks, developing novel approaches for selection-conditional coverage, interactive framework for conformal selection, and applications in survival analysis. Her research bridges theoretical statistics with practical applications in healthcare, education technology, and machine learning model validation. Analysis of her publication record reveals a strong focus on conformal prediction methods and their extensions. Over the past two years, she has published extensively on topics including conformal selection frameworks, distribution-free inference for survival analysis, and applications of conformal methods to foundation models and time series data. Her work demonstrates a consistent theme of developing theoretically sound statistical methods that provide rigorous guarantees without distributional assumptions, making them broadly applicable across scientific domains. Her notable scientific achievements include: Rising Star in Data Science, University of Chicago, 2022 Waterloo Student Conference Presentation Award, University of Waterloo, 2021 First Prize of the 2018 Citadel Datathon, Stanford University, 2018 Dr. Ren is actively engaged in academic service, currently co-organizing the International Seminar on Selective Inference with prominent researchers in the field. She teaches advanced statistical courses including Statistical Inference (STAT4310) and Seminar in Advanced Applications of Statistics (STAT9910) at the University of Pennsylvania. Her teaching focuses on statistical methodology and inference, preparing students for both theoretical and applied work in statistics and data science. Dr. Ren has also developed several R packages including cfsurvival for conformalized survival analysis and derandomKnock for implementing derandomized knockoffs procedures, demonstrating her commitment to making her research accessible and usable by the broader scientific community.

Yuni Dewarajaمشاهده پروفایل

استاد پژوهشی

• Radiation absorbed dose estimation
• Quantitative SPECT imaging
• Image reconstruction
• +۶ مورد دیگر

Yuni Dewaraja, PhD is the William Martel Research Professor in the Department of Radiology at the University of Michigan Medical School. She is affiliated with the Center for Positron Emission Tomography and has made significant contributions to the field of nuclear medicine physics and radiopharmaceutical therapy dosimetry. Dr. Dewaraja earned her PhD in 1994 from the University of Michigan, following an MS from Kansas State University in 1990 and a BS from the University of Western Australia in 1986. Her academic journey has positioned her as a leading expert in quantitative imaging and radiation dosimetry. Dr. Dewaraja's research focuses on estimating radiation absorbed dose in radionuclide therapy , quantitative SPECT imaging , image reconstruction techniques , and correction methods for scatter, attenuation and partial volume effects . She extensively employs Monte Carlo methods for image reconstruction and patient-specific absorbed dose estimation. Her work bridges physics, engineering, and clinical applications to improve the precision of targeted radionuclide therapies. Her recent publications demonstrate a strong emphasis on Lutetium-177 and Yttrium-90 therapies , with research spanning dosimetry standardization , predictive modeling of treatment response , optimization of imaging protocols , and integration of multiple therapeutic modalities . She has been instrumental in international efforts to reduce variability in dosimetry calculations and establish best practices in the field. Can 177Lu-DOTATATE Kidney Absorbed Doses be Predicted from Pretherapy SSTR PET? Multicycle Dosimetric Behavior and Dose–Effect Relationships in [177Lu]Lu-DOTATATE Therapy Shorter SPECT scans using self-supervised coordinate learning techniques Sequential 90Y SIRT and SBRT using PET-based Absorbed Dose Maps 177Lu-PSMA-617 SPECT/CT Dosimetry and Radiobiological Models Dr. Dewaraja has received recognition through numerous citations of her work, with over 3,000 total citations according to the Michigan Research Experts profile. Her research has been featured in leading journals including the Journal of Nuclear Medicine (93 publications), European Journal of Nuclear Medicine and Molecular Imaging (30 publications), and Medical Physics (12 publications). As a dedicated educator and mentor, Dr. Dewaraja collaborates extensively with colleagues across multiple institutions on research projects focused on advancing precision in nuclear medicine therapy. Her work has significant implications for improving the safety and efficacy of targeted radionuclide treatments for cancer patients.

Marlies Goordenمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Image Reconstruction
• SPECT/PET/CT System Optimization
• +۳ مورد دیگر

Marlies Goorden is a researcher at the Faculty of Applied Sciences , Delft University of Technology , specializing in Medical Physics & Technology . Her work focuses on improving imaging geometries for SPECT, PET, and CT systems through analytical modeling, Monte Carlo simulations, and ray tracing methods. She also develops advanced image reconstruction algorithms and optimizes gamma ray detectors using novel scintillating materials and sensor combinations. Education: MSc in Applied Physics, TU Delft (2002) PhD in Theoretical Solid State Physics, Leiden University (2005) Research Interests center on enhancing spatial resolution and sensitivity in preclinical and clinical imaging systems, particularly through clustered pinhole collimation and photon transport models. Her recent work includes spectral CT thermometry, proton stopping power ratio prediction, and pile-up evaluation frameworks for photon-counting detectors. Publications highlight her contributions to simultaneous SPECT/PET systems (VECTor), accelerated reconstruction algorithms, and detector optimization. Her collaborations span institutions like University Medical Center Utrecht and University of Geneva.

Daniela Hernandez Muguiroمشاهده پروفایل

استادیار بالینی

• Clinical Pathology
• Veterinary Hematology
• Tick-borne Diseases
• +۳ مورد دیگر

Daniela Hernandez Muguiro serves as a Clinical Assistant Professor in the Department of Diagnostic Medicine/Pathobiology at Kansas State University's College of Veterinary Medicine. Her clinical expertise spans hematology, cytology, urinalysis, chemistry, and serum protein electrophoresis within veterinary diagnostic pathology. Her academic foundation includes: BVSc (2008) from Universidad de Guadalajara Master in Veterinary Medicine: Veterinary Clinical Pathology (2011-2013) from Universidad Nacional Autonoma de Mexico Clinical Pathology residency (2015-2018) at Cornell University Diplomate of the American College of Veterinary Pathologists (Clinical Pathology, 2018) Dr. Hernandez Muguiro's research program centers on clinical pathology with specialized focus on canine and feline hematopoietic neoplasia and tick-borne diseases. Her work bridges diagnostic laboratory medicine with collaborative clinical studies, particularly examining vector-borne pathogens in Hong Kong canine and feline populations. Recent publications reveal expanding interests in machine learning applications for immunocytochemistry image analysis and the epidemiology of emerging pathogens in exotic species. Her scientific contributions demonstrate consistent productivity across parasitology, hematology, and diagnostic methodology. The publication timeline shows progressive specialization in tick-borne disease epidemiology (2023-2024), hematopoietic neoplasia in exotic species (2020), and innovative diagnostic approaches (2023-2025), with increasing international collaboration evident in Hong Kong-based studies. Her professional recognition includes: Diplomate, American College of Veterinary Pathologists (Clinical Pathology, 2018) As an educator, she directs fourth-year veterinary student rotations in VDMP902 Diagnostic Medicine while training diagnostic medicine interns and anatomic pathology residents. Her collaborative research model emphasizes clinical pathology support for multidisciplinary studies, though specific grant details remain unpublicized. Current work shows strong international engagement through Hong Kong-based investigations of vector-borne diseases and fungal infections in companion animals, with growing integration of computational methods in diagnostic pathology.

Richard Ansorgeمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Biophysics
• High-Performance Computing
• +۵ مورد دیگر

Dr. Richard Ansorge is a Retired Senior Lecturer at the University of Cambridge and Fellow of Fitzwilliam College. He specializes in interdisciplinary research at the intersection of physics and life sciences, particularly in medical imaging technologies. His work focuses on advanced imaging modalities such as MRI/PET integration, dose calculation algorithms for proton therapy, and high-performance computing applications in medical imaging. Ansorge’s research interests include the physics of life, computational methods for medical imaging, and the development of novel imaging systems. He has contributed to advancements in PET/MRI co-registration, GPU-accelerated image processing, and radiation therapy optimization. His academic career has involved pioneering work in MRI signal theory, attenuation correction techniques, and small-animal imaging systems. Notable research trends in his publications emphasize the fusion of medical imaging modalities (e.g., PET/MRI), optimization of radiation delivery systems, and leveraging parallel computing (CUDA/GPU) for real-time medical applications. His work bridges fundamental physics principles with practical biomedical engineering solutions. Awards: No scientific awards explicitly mentioned in the provided texts. However, his contributions to medical imaging and particle therapy reflect significant impact in the field. Advising and grants: No specific student advisees or grant details provided. His research has likely been supported by institutional funding and collaborative projects at Cavendish Laboratory. Labs/Teams: Affiliated with the Cavendish Laboratory at the University of Cambridge, focusing on interdisciplinary physics and life sciences research.

• Biomedical Engineering
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Dr. Mirosław Socha is a Lecturer at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków. His office is located in building B-1, room 206, where he conducts research and academic activities. Contact is available via phone (+48 12 617 28 28) or institutional email (socha@agh.edu.pl). His research encompasses several interconnected domains: Biomedical engineering with focus on medical imaging reconstruction and visualization techniques Computer vision applications for surgical navigation and diagnostics Deep learning methodologies for medical implant design and cranial defect solutions Metrology systems including flow calibration and friction measurement Signal processing for EEG and neural interface systems Analysis of his 15 most recent publications reveals strong emphasis on computational methods in healthcare, particularly deep learning-based medical reconstruction (2022-2023), measurement instrumentation (2019-2021), and medical visualization systems (2012-2015). His work consistently bridges theoretical concepts with clinical and industrial applications. No scientific awards, prizes, or fellowships are documented in available sources. Information regarding research grants, laboratory leadership, supervised students, or collaborative teams is unavailable in the provided materials.

Jeffrey Yapمشاهده پروفایل

استاد پژوهشی

• Quantitative Imaging
• Cancer Therapy
• Multimodality Imaging
• +۶ مورد دیگر

Jeffrey Yap is a Research Professor in the Department of Radiology and Imaging Sciences at the University of Utah . He serves as the Director of the Center for Quantitative Cancer Imaging and Theranostics (CQCIT) , a multidisciplinary hub integrating radiopharmaceutical development, preclinical and clinical imaging, and theranostic technologies. His research focuses on quantitative multimodality imaging to evaluate novel cancer therapies in murine tumor models and clinical trials , with expertise in PET/CT, SPECT/CT, and theranostic radiopharmaceuticals . He holds a PhD in Medical Physics from the University of Chicago and a BS in Physics from the University of Dallas. Education PhD in Medical Physics, University of Chicago BS in Physics, University of Dallas Research Interests Quantitative imaging for cancer therapy evaluation Theranostics (diagnostic and therapeutic radiopharmaceuticals) 4D PET/CT in radiation treatment planning Body composition analysis in oncology Preclinical and clinical trial imaging Hypoxia and metabolic PET biomarkers Scientific Contributions Developed standardized imaging protocols for clinical trials (e.g., UPICT) Characterized autophagy resistance mechanisms in RAS-driven cancers Validated FDG-PET as a pharmacodynamic biomarker for targeted therapies Integrated body composition metrics into cancer survival analysis Laboratory & Facilities Director of CQCIT , operating two cGMP cyclotron labs North American Mediso Preclinical Imaging Center of Excellence Advanced digital PET/CT/SPECT/CT for clinical trials Image Analysis Core with Yunu software for tumor measurement

Jan Liuمشاهده پروفایل

پژوهشگر

• Biomedical Engineering
• Medical Device Technology
• Electrical Impedance Imaging
• +۷ مورد دیگر

Jan Liu is a Research Associate at the Institute for Medical Device Technology at the University of Stuttgart. With a background in Electrical Engineering and Biomedical Engineering, Liu focuses on medical device innovation using impedance measurements, sensor fabrication, and needle navigation systems. Their work bridges theoretical simulation with experimental validation in clinical contexts. Education: M.Sc. in Electrical Engineering (2015), B.Sc. in Business Administration (2016), B.Sc. in Electrical Engineering (2014) Teaching: Coordinated courses in Medical Measurement Methods and Practical Medical Device Development (2019-2023) Liu’s research spans biomedical device development, including needle navigation systems using electrical impedance, 3D vein reconstruction , and low-cost vein detection methods. Their work emphasizes simulation (COMSOL, FEM) , tissue phantom fabrication , and soft robotic actuation for medical applications. Recent publications highlight trends in impedance-based tissue identification , multi-local needle sensors , and microscale electrode design . Liu has supervised over 20 theses on topics ranging from venous collapse prevention to speaker vibration actuators and monopolar impedance sensitivity . Key collaborations: Korea Advanced Institute of Science and Technology, University of Stuttgart Research grants: Not explicitly mentioned but implied through conference publications

• Biomedical Imaging
• Radiology
• Molecular Imaging
• +۵ مورد دیگر

Feng Li serves as an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His work bridges the gap between advanced materials science and biomedical imaging, with a particular focus on PET imaging and molecular structure analysis. He maintains affiliation with the PET Core facility at Yale, indicating his specialized expertise in positron emission tomography techniques. His research interests span Biomedical Imaging, Radiology, Molecular Imaging, Polymer Science, and Nanotechnology. Dr. Li's work demonstrates exceptional interdisciplinary integration, applying polymer chemistry principles to develop novel imaging agents and drug delivery systems. His publications reveal expertise in cyclic polymers, nanoparticle interactions, and biosensor development, with applications ranging from cancer diagnostics to neurological imaging. Analysis of his recent publications (2024-2025) shows a strong trend toward interdisciplinary research combining materials science with medical applications. His work frequently involves molecular design of polymers for specific biomedical functions, development of novel imaging techniques, and creation of biosensors for disease detection. The publications demonstrate methodological diversity, incorporating computational modeling, chemical synthesis, and clinical validation approaches. Conjugated Polymer Nanoparticles research exploring polymer topology effects Unique Complex Structure studies of cyclic poly(ethylene glycol) on gold nanoparticles Molecular Design work on oligosaccharide-block-oligodimethylsiloxane systems Supramolecular Biosensor development for disease-associated metabolite quantification Advanced imaging techniques for cancer and neurological applications While specific grant information isn't detailed in the provided text, Dr. Li's extensive publication record across high-impact journals suggests successful funding for his research programs. His collaborative work across chemistry, radiology, and biomedical engineering departments indicates leadership in interdisciplinary research initiatives. Dr. Li's laboratory likely focuses on developing novel imaging agents and materials for medical applications, with emphasis on molecular-level design and characterization.

Tong Xuمشاهده پروفایل

استاد

• Medical Physics
• Radiation Therapy
• Medical Imaging
• +۳ مورد دیگر

Dr. Tong Xu is a Professor in the Department of Physics at Carleton University, affiliated with the Faculty of Science. His research focuses on real-time motion tracking for medical procedures, including tumor tracking during radiation therapy and image-guided surgery. He has pioneered the PeTrack technique using positron emission markers and developed the dynamic dual-energy x-ray imaging (dDEXI) method for lung tissue motion assessment. His work aims to improve radiation therapy accuracy, reduce patient radiation exposure, and enhance diagnostic tools. Dr. Xu holds a U.S. Patent (#8447387) for real-time tumor tracking technology and has contributed to advancements in medical imaging and radiation oncology. His research interests span medical physics, radiation therapy, and biomedical engineering, with applications in oncology and cardiology. Key projects include respiratory motion management in PET/CT imaging and optimizing 4D-VMAT radiotherapy frameworks. Dr. Xu collaborates on interdisciplinary projects to translate imaging innovations into clinical practice. Awards : U.S. Patent #8447387 (2013) Key Technologies : PeTrack, dDEXI, motion-compensated imaging Labs/Groups : Medical Imaging and Radiation Physics Research Group at Carleton University

Amir H. Goldanمشاهده پروفایل

استادیار

• Medical Imaging
• PET Imaging
• X-Ray Imaging
• +۵ مورد دیگر

Amir H. Goldan is an Assistant Professor in the Department of Electrical and Computer Engineering at Stony Brook University, with an affiliation in Radiology at Stony Brook Medicine. His research focuses on advancing medical imaging technologies, particularly in PET and X-ray imaging, emphasizing detector design, avalanche photodetectors, and novel imaging systems. He leads the Goldan Lab, which collaborates with biomedical engineers, computer scientists, and medical professionals to develop high-resolution, cost-effective imaging solutions. Education: Ph.D. in Biomedical Engineering (implied from lab collaborations). Research interests include Prism-PET detector modules for high-resolution brain imaging, single-photon counting techniques, and quantum mechanical modeling of charge transport. The lab’s translational projects include conformal PET scanners and motion-tracking systems for improved neuroimaging accuracy. Goldan mentors graduate and undergraduate students in interdisciplinary research, emphasizing career development in medical imaging. Notable projects include the Prism-PET II scanner with tapered crystals and electromagnetic motion tracking. The lab’s work addresses clinical needs in oncology, neuroimaging, and cardiology through innovative detector fabrication and machine learning integration. Grants & Funding: Implied through lab activities but not explicitly stated. Labs/Teams: Goldan Lab at Stony Brook University, focusing on medical imaging innovations.