جستجوی استاد راهنمای دانشگاهی

Mo Rastgaarمشاهده پروفایل

استاد

• Assistive Robotics
• Rehabilitation Robotics
• Cyber-Physical Systems
• +۳ مورد دیگر

Dr. Mo Rastgaar is a Professor at Purdue Polytechnic Institute, Purdue University. He holds a PhD in Mechanical Engineering from Virginia Tech (2008) and completed a postdoctoral fellowship at MIT's Newman Laboratory for Biomechanics and Human Rehabilitation. He leads the Human-Interactive Robotics Lab (HIRoLab), focused on assistive and rehabilitation robots for enhanced mobility, particularly lower-extremity devices. His research emphasizes understanding agile gait dynamics through human experiments and modeling. Research interests include assistive robotics, cyber-physical systems, dynamics, and control systems. Notable awards include the 2014 NSF CAREER Award. He has secured grants such as the 2019 NRI Collaborative Grant on robotic ankle prosthetics and 2020 grants for undersea infrastructure. Dr. Rastgaar's work bridges biomechanics, robotics, and clinical applications, advancing prosthetic designs and human-robot interaction. Key contributions include developing steerable powered ankle-foot prostheses and exploring multi-robot systems for underwater exploration. His labs integrate interdisciplinary approaches to solve complex mobility challenges, emphasizing both technical innovation and real-world clinical impact.

Jean-Philippe Thiranمشاهده پروفایل

استاد

• Computational Imaging
• Medical Image Analysis
• Diffusion MRI
• +۵ مورد دیگر

Prof. Jean-Philippe Thiran is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Signal Processing Laboratory (LTS5) and Director of the Institute of Electrical and Micro Engineering. He also maintains a part-time Associate Professor position with the Department of Radiology of the University Hospital Center (CHUV) and University of Lausanne (UNIL). Born in Namur, Belgium in 1970, he received his Electrical Engineering degree and PhD from the Université catholique de Louvain (UCL), Belgium, in 1993 and 1997 respectively. He joined EPFL in 1998 and has established himself as a leading researcher in computational imaging. His research focuses on computational imaging , with significant contributions to medical image analysis (particularly diffusion MRI, ultrasound imaging, and digital pathology) and computer vision . His recent work integrates advanced modeling, simulation, and machine learning techniques to extract microscopic tissue information from macroscopic MRI signals. This approach combines hyper-realistic synthetic tissue models, advanced Monte-Carlo simulations, and ML-based estimation techniques for brain microstructure analysis with potential applications to other tissues. Senior Member of IEEE Fellow of the European Association for Signal Processing (EURASIP) Prof. Thiran has authored or co-authored 1 book, 9 book chapters, 250 journal papers and over 270 peer-reviewed conference papers, and holds 12 international patents. He previously served as Co-Editor-in-Chief of the Signal Processing journal (2001-2005) and associate editor of IEEE Transactions on Image Processing. He has chaired major conferences including EUSIPCO 2008 and IEEE ICIP 2015. His laboratory at EPFL brings together interdisciplinary researchers to develop innovative imaging techniques that bridge macroscopic measurements and microscopic tissue properties, with significant potential for medical diagnostics and treatment planning applications.

Dennis Säringمشاهده پروفایل

استاد

• Deep Learning
• Medical Image Analysis
• Artificial Intelligence
• +۵ مورد دیگر

Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.

Michael McAlpineمشاهده پروفایل

استاد

• 3D Printing
• Nanotechnology
• Biomedical Engineering
• +۴ مورد دیگر

Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award

• Microbial Genomics
• Gut Microbiome
• Aging and Healthy Aging
• +۴ مورد دیگر

Paul O'Toole is a Professor of Microbial Genomics and Principal Investigator at the APC Microbiome Ireland, University College Cork. His research focuses on the gut microbiome's role in health, aging, and disease, particularly in the context of diet and probiotics. He leads projects like the ELDERMET study on elderly nutrition and the NU-AGE project exploring Mediterranean diets' anti-aging effects. He holds a BA (Mod.) from Trinity College Dublin and a PhD from Lund University, with postdoctoral training in Canada and New Zealand. Key grants include studies on dairy-derived microbiota, probiotic strain improvement, and microbiome analysis in aging populations. He has published extensively on Lactobacillus genomics, gut-brain interactions, and microbiome-driven health outcomes. His work bridges fundamental microbiology with clinical applications, emphasizing translational research. Scientific highlights include discovering microbiome links to cognitive decline, demonstrating dietary modulation of gut microbes to combat obesity, and identifying keystone species in healthy aging. He advocates for sustainability in conservation and food systems, reflecting his interdisciplinary approach to global health challenges.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Professor Daniel Rueckert is a leading academic in Artificial Intelligence and Medical Imaging, holding dual positions at Imperial College London (as Professor of Visual Information Processing) and Technical University of Munich (Alexander von Humboldt Professor for AI in Medicine and Healthcare). He obtained his MSc from Technical University Berlin (1993) and PhD from Imperial College London (1997), followed by postdoctoral work at King’s College London. At Imperial, he led the Department of Computing (2016–2020) and founded the Biomedical Image Analysis group. His research focuses on AI-driven medical image analysis, including algorithms for image reconstruction, registration, and clinical decision support. His research interests span AI applications in healthcare, machine learning for medical imaging, and computational methods for clinical diagnostics. Notable contributions include over 500 publications and 60+ PhD graduates, with key works in federated learning, cardiac motion analysis, and biomarker development. Awards include the Leibniz Prize (2025), Royal Academy of Engineering Fellowship (2015), and multiple ERC grants. He leads the BioMedIA research group and is an editorial board member of Medical Image Analysis . Recent publications highlight advancements in AI-driven medical imaging, such as secure federated learning frameworks and deep learning models for disease prediction. His work bridges academic and industrial sectors through initiatives like IXICO, an Imperial spin-out. Current affiliations include roles at both Imperial and TUM, emphasizing interdisciplinary collaboration in healthcare technology. Advising and grants: Supervised over 60 PhD students and 40 post-docs. Secured grants including ERC Synergy (2013) and ERC Advanced (2020). Active in labs focused on biomedical image computing and AI in healthcare systems. Collaborative efforts include the BioMedIA group and TUM’s AI initiatives. Labs/teams: Leads the Biomedical Image Analysis group at Imperial and the TUM AI in Medicine team. Collaborates extensively on projects like cardiac imaging analysis and federated learning for healthcare.

Edoardo Charbonمشاهده پروفایل

استاد

• Ultra High-Speed Optical Sensors
• 3D Optical Sensors
• LiDAR
• +۱۰ مورد دیگر

Edoardo Charbon is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering, where he leads the Advanced Quantum Architecture Lab (AQUA). He also serves on the School Council STI and is Co-Director of STI-SSIQ Administration. Previously, he was a full professor and chair at Delft University of Technology from 2008 to 2016. Charbon received his Elektrotechnik Diploma from ETH Zurich, M.S. from UC San Diego, and Ph.D. from UC Berkeley, all in electrical engineering. His career spans industry experience at Cadence Design Systems and Canesta Inc. before joining EPFL in 2002. His research focuses on ultra high-speed and 3D optical sensors, with applications in LiDAR, FLIM (Fluorescence Lifetime Imaging Microscopy), PET (Positron Emission Tomography), FCS (Fluorescence Correlation Spectroscopy), and NIROT (Near-Infrared Optical Tomography). He has pioneered deep-submicron CMOS SPAD technology, which is now mass-produced and used in smartphones, telemeters, and medical diagnostics. His recent work bridges cryo-CMOS circuits for quantum computing with advanced optical sensing techniques. Analysis of his recent publications reveals a strong trend toward integrating quantum technologies with practical imaging applications. His work spans from fundamental device development (SPAD sensors, cryo-CMOS circuits) to applied systems (LiDAR engines, medical imaging devices), with increasing integration of machine learning techniques for real-time processing. 2023 IISS Pioneering Achievement Award Fellow of the IEEE Distinguished visiting scholar, W. M. Keck Institute for Space at Caltech Fellow, Kavli Institute of Nanoscience Delft Distinguished lecturer, IEEE Photonics Society Professor Charbon has authored or co-authored over 500 papers and two books, and holds 27 patents. His research has been supported by collaborations with organizations including Bosch, X-Fab, Texas Instruments, Maxim, Sony, Agilent, and the Carlyle Group. He has driven significant innovation in CMOS SPAD technology, which is now commercially deployed in various applications. He leads the Advanced Quantum Architecture Lab (AQUA) at EPFL, which focuses on the development of advanced sensor systems combining quantum technologies with conventional electronics. The lab has been instrumental in creating SPAD-based imaging systems that push the boundaries of time-resolved optical detection.

Tim Q. Duongمشاهده پروفایل

استاد

• Medical Imaging
• MRI
• Image Analysis
• +۹ مورد دیگر

Tim Q. Duong, Ph.D., is a Professor at Albert Einstein College of Medicine, affiliated with the Departments of Radiology, Biochemistry, Ophthalmology & Visual Sciences, and Neuroscience. His research focuses on medical imaging, MRI, image analysis, machine learning, and predictive modeling for studying diseases like COVID-19 , neurodegeneration (Alzheimer's, multiple sclerosis), brain injuries , and breast cancer . Develops AI-driven MRI techniques for early disease detection Investigates neuroplasticity in glaucoma and diabetic retinopathy Leads grants from NIH and National Eye Institute Research Trends : Recent publications emphasize AI integration in medical imaging, long-term effects of SARS-CoV-2, and advanced MRI applications for ocular and neurological disorders. Grants include multiple R01 awards for diabetic retinopathy and glaucoma studies. Training Opportunities : Actively recruits postdocs, research coordinators, and faculty. Offers research positions for graduate, medical, and high school students, including Regeneron Scholar programs. Labs & Teams : Leads the Duong Lab at Montefiore Medical Center, focusing on translational research for clinical imaging solutions.

• Biomedical AI
• Medical Image Analysis
• Biomedical Imaging
• +۷ مورد دیگر

Professor Liyue Shen is a faculty member in the Department of Biomedical Engineering within the College of Engineering at the University of Michigan. Her research program focuses on cutting-edge applications of artificial intelligence in biomedical imaging and healthcare, with particular expertise in diffusion models and inverse problem solving for medical image reconstruction. Dr. Shen's research interests span biomedical AI, medical image analysis, biomedical imaging, machine learning, computer vision, signal and image processing, AI for precision health, and bioinformatics. Her work bridges theoretical advances in AI with practical clinical applications, developing novel methods for medical image reconstruction, segmentation, and analysis that can improve diagnostic accuracy and treatment planning. Analysis of her recent publications reveals a strong focus on diffusion models for solving complex inverse problems in medical imaging, with particular emphasis on patch-based approaches, latent space disentanglement, and efficient sampling techniques. Her research group has made significant contributions to 3D CT reconstruction, chest X-ray analysis, holographic phase retrieval, and patient-specific imaging studies, demonstrating both theoretical innovation and practical clinical relevance. While specific scientific awards aren't mentioned in the available materials, her extensive publication record in top venues demonstrates significant scholarly impact in the field of biomedical AI. Her research program appears well-funded through grants supporting her work in medical imaging and AI development.

Jianming Liangمشاهده پروفایل

استاد

• Biomedical Informatics
• Data Science
• Medical Imaging
• +۷ مورد دیگر

Jianming Liang is a full professor at Arizona State University's College of Health Solutions, specializing in biomedical informatics, data science, and computer vision. His research focuses on self-supervised learning, foundation models, and improving transfer learning techniques for medical imaging applications. National Academy of Inventors Fellow (2021) ASU Faculty Innovation Award (2019) ASU Distinguished Faculty Award (2023) NIH R01 grant recipient Led lab producing FDA-approved medical imaging products His lab has developed multiple open-source frameworks like Ark , Foundation_X , and ModelsGenesis for medical image analysis. Team has received over 70 student research awards including NCWIT Collegiate and AMIA Ph.D. Dissertation honors. Key research contributions include: Anatomically consistent foundation models Domain-adaptive pretraining strategies Annotation-efficient deep learning Integrated classification/localization/segmentation frameworks 40+ US patents (50+ pending) Major publications demonstrate leadership in self-supervised learning for chest radiography, pulmonary embolism detection, and medical AI explainability.

Lucia Carichinoمشاهده پروفایل

استادیار

• Mathematical Biology
• Fluid Dynamics
• Numerical Methods
• +۵ مورد دیگر

Lucia Carichino is an Assistant Professor in the School of Mathematics and Statistics at Rochester Institute of Technology (RIT). She holds a PhD in Mathematics from Purdue University and a BS/MS in Mathematical Engineering from Politecnico di Milano, Italy. Her research focuses on mathematical and computational models of multiscale biological systems, particularly fluid-structure interaction in biological contexts like ocular blood flow and microswimmers. She emphasizes integrating experimental data with mathematical models to advance medical understanding. Carichino teaches courses such as Differential Equations, Linear Algebra, and oversees undergraduate research projects. In 2023, she received the National Science Foundation LEAPS-MPS award for her work on computational modeling of eye-contact lens interactions. Her research has been published in high-impact journals and presented at conferences. She actively collaborates on projects addressing glaucoma, ocular hemodynamics, and biomedical applications. Education: PhD in Mathematics, Purdue University BS and MS in Mathematical Engineering, Politecnico di Milano, Italy Research interests include fluid dynamics, numerical methods, and mathematical biology. Her work bridges theoretical models with biomedical applications, such as optimizing gene therapy delivery and analyzing ocular physiology under varying environmental conditions (e.g., altitude). She explores topics like sperm motility, computational simulations of biological systems, and the interplay between fluid dynamics and biological structures. Her recent articles highlight advancements in ocular pharmacokinetics, contact lens interactions, and altitude effects on intraocular pressure. These studies underscore her expertise in multiscale modeling and fluid-structure interaction. Carichino also contributes to educational initiatives, fostering a collaborative classroom environment. Notable awards include the NSF LEAPS-MPS award (2023). She advises student research projects and collaborates with colleagues, such as Maki, on interdisciplinary studies. Her work is supported by grants and has led to presentations at ophthalmology and mathematics conferences. Carichino’s lab focuses on computational modeling of biological systems, particularly in ophthalmology and microscale fluid dynamics. Her team develops tools to simulate complex physiological processes, aiding in medical diagnostics and treatment strategies.

Levent Burak Karaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computational Design
• +۱۲ مورد دیگر

Levent Burak Kara is a Professor in the Department of Mechanical Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Robotics Institute. He is a leading researcher in AI-driven computational design, additive manufacturing, and intelligent engineering systems, leading the Visual Design and Engineering Lab (VDEL) at CMU. Education: B.S., Mechanical Engineering, Middle East Technical University (1998) M.S., Mechanical Engineering, Carnegie Mellon University (2000) Ph.D., Mechanical Engineering, Carnegie Mellon University (2005) His research focuses on integrating machine learning, optimization, and geometric modeling to revolutionize engineering design and manufacturing. Key areas include topology optimization, CAD intelligence, digital twins, generative design, bioengineering, and electronic design automation. His work enables automation of traditionally labor-intensive design processes using deep learning and reinforcement learning. His recent publications reveal a strong trend toward physics-informed surrogate modeling, real-time simulation, manufacturability prediction, and AI-driven automation in mechanical, biomedical, and electronic systems. These works frequently appear in top journals such as Journal of Mechanical Design and Journal of Applied Mechanics , and at premier conferences like NeurIPS and DAC. Scientific Awards: National Science Foundation CAREER Award ASME Design Automation Society Young Investigator Award Google AI for Social Good Impact Scholar Kara advises several Ph.D. students and has secured significant funding from federal agencies such as the NSF and the U.S. Army Research Laboratory, as well as collaborations with industrial leaders including Cadence Design Systems and NVIDIA. His research is also supported by CMU’s NextManufacturing Center and the Critical Technology Initiative. He is actively involved in developing intelligent design systems that leverage AI to automate product design, optimize manufacturing processes, and improve medical diagnostics, particularly in oral cancer screening and organ preservation. His lab, VDEL, is a hub for innovation in AI-enabled engineering.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

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.

• Medical Machine Learning
• Data Science
• Artificial Intelligence in Healthcare
• +۳ مورد دیگر

Prof. Dr. Julia Vogt is an Assistant Professor at the Department of Computer Science at ETH Zürich, leading the Professur für Medizin. Datenwiss. Her research focuses on medical machine learning, data science, and AI applications in healthcare. She specializes in developing interpretable AI systems for clinical decision support, particularly in pediatric diabetes management, medical imaging analysis, and anomaly detection. Her work bridges translational gaps by emphasizing causal approaches and clinical validation. Her academic role includes teaching courses like the Data Science Lab (263-3300-00L/10L) and Topics in Medical Machine Learning (263-5100-00L). Her lab's research spans predictive modeling for nocturnal hypoglycemia, echocardiogram analysis for pulmonary hypertension detection, and multimodal learning in radiology. She also contributes to national pediatric data initiatives like SwissPedHealth. Key technical areas include concept bottleneck models, stochastic AI frameworks, and generative models for medical signal denoising. Her work often emphasizes model interpretability, fairness, and robustness to distribution shifts. She collaborates on projects involving wearable devices for pediatric monitoring and AI-driven rehabilitation tools for post-stroke gait analysis.