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نمایش ۳۱-۴۵ از ۶٬۹۱۰ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
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.
Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.
دانشیار
Angela Pitenis is an Associate Professor in the Department of Materials at the University of California, Santa Barbara (UCSB), within the College of Engineering. Her research focuses on interfacial phenomena in soft materials, particularly friction, adhesion, wear, and deformation of complex surfaces ranging from living cells to polymer nanocomposites. She employs advanced experimental techniques such as microscopy, spectroscopy, and interferometry to study these interfaces under extreme conditions and within buried environments. Her work has direct applications in healthcare, energy sustainability, and engineering design. Prof. Pitenis holds a Ph.D., M.Sc., and B.S. in Mechanical Engineering from the University of Florida. Her research group investigates biomaterials, hydrogel lubrication, and bioinspired materials, with recent studies addressing implant-associated inflammation, tumor cell dynamics in 3D microgels, and pH-responsive hydrogel friction. She is affiliated with the Materials Research Lab at UCSB and contributes to interdisciplinary projects at the intersection of materials science and biology. Notable research trends in her work include the development of biocompatible lubricious surfaces, understanding friction-induced biological responses, and designing smart materials with tunable mechanical properties. Her studies on photoresponsive hydrogels and superlubricious materials highlight innovations in responsive and adaptive material systems. Pitenis emphasizes in situ experimental methods and has pioneered techniques for analyzing dynamically evolving material interfaces. Her research also extends to marine biomaterials, such as the mechanical resilience of sessile tunicates, and explores applications in medical implants, bioreactors, and energy systems. While specific awards are not listed here, her contributions reflect a commitment to advancing soft matter tribology and biomaterials science.
Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.
استادیار
Amy E. Fraley is an Assistant Professor at ETH Zürich, Department of Chemistry and Applied Biosciences, and leads the Medicinal Chemistry Research Group. Her cross-disciplinary work bridges natural products biosynthesis, pharmaceutical sciences, and environmental sustainability through enzymatic chemistry and biotechnology. BSc in Chemistry, Millersville University of Pennsylvania (2014) PhD in Medicinal Chemistry, University of Michigan College of Pharmacy (2019) Postdoctoral work at ETH Zürich Institute of Microbiology under Prof. Jörn Piel Her research focuses on harnessing biosynthetic enzymes (e.g., halogenases, monooxygenases, Diels-Alderases) to create sustainable bioactive metabolites. She explores natural product biosynthesis in fungi and marine bacteria, targeting disease mechanisms and green chemistry applications. Recent work includes metagenomic studies of Lake Chilika microbial mats and polyketide synthase engineering. Notable scientific awards include the 2024 JSP Fellow at Bürgenstock Conference, 2018 Rackham Predoctoral Fellowship, and multiple University of Michigan honors. Her group secured funding from the ETH4D Doctoral Mentorship Grant and Messerli Foundation . Collaborations span structural biology, synthetic chemistry, and microbiology, with key contributions to flavoenzyme characterization and bioactive compound libraries . The lab emphasizes interdisciplinary training and innovation in biocatalytic tools for organic synthesis.
مدرس ارشد
Dr Nga Wun (Doris) Li is a Senior Lecturer at the University of Technology Sydney (UTS) within the Faculty of Design, Architecture and Building, Department of Fashion and Textiles. She leads research in seamless knitting technology, smart textiles, and sustainable fashion innovation. PhD in Fashion & Textile Design (HK PolyU, 2021) BA (Hons) in Fashion & Textiles (HK PolyU, 2012) Certifications in Wholegarment Machine (Shima Seiki, Japan) and Higher Education Pedagogy (UTS) Her research focuses on functional garments, knitting technology, and bio-informed textile design. Key projects include smart socks for DVT prevention , one-size sports bras , and buoyant swimwear for children . She combines knitting with machine learning and 3D printing for material innovation. Recent publications highlight her work on compression textiles (2025), wearable glucose sensors (2025), and knitted metasurfaces for acoustic comfort (2024). She has secured AU$52,000+ in grants across 5 funded projects. Fashion Design Award (Jeanswest, China) Outstanding Presentation & Research Paper Awards (2024) Dr Li supervises PhD and MPhil students in smart textiles and sustainable design while coordinating machine knitting courses. She collaborates with AiDLab (Hong Kong) and Powerhouse Museum (Australia).
Aditi Das is a Full Professor in the School of Chemistry and Biochemistry at the Georgia Institute of Technology, College of Sciences. She leads the Das Laboratory, which focuses on the biochemistry and chemical biology of lipids, particularly studying cytochrome P450 enzymes and their role in lipid metabolism, endocannabinoid systems, and inflammatory pathways. Her educational background includes: B.Sc. in Chemistry from St. Stephen's College M.Sc. in Chemistry from Indian Institute of Technology, Kanpur (I.I.T) Ph.D. in Chemistry from Princeton University Postdoctoral research at Northwestern University (NSF-NSEC fellow) and Beckman Institute for Advanced Science and Technology, University of Illinois UC Professor Das's research interests center around understanding the physiological role of lipids in sustaining homeostasis and their implications in disease states such as neurodegenerative disorders, cancer, and cardiovascular diseases. Her laboratory specializes in: Enzymology of cytochrome P450s, particularly CYP2J2 epoxygenase Metabolism of ω-3 and ω-6 fatty acids and their derivatives Minor cannabinoid metabolism by cytochrome P450 enzymes Discovery of novel anti-inflammatory lipid metabolites and endocannabinoids Mechanistic studies of membrane proteins using nanodisc technology Her work bridges biochemistry, chemical biology, and pharmacology to uncover novel therapeutic targets related to lipid signaling pathways. Analysis of Professor Das's recent publications (2023-2025) reveals a strong focus on cannabinoid metabolism by cytochrome P450 enzymes, with particular emphasis on how these metabolic processes generate bioactive compounds that interact with the endocannabinoid system. Her research increasingly explores the therapeutic potential of omega-3 derived endocannabinoid epoxides in inflammatory and neurodegenerative conditions. The use of nanodisc technology for studying membrane proteins in near-native environments remains a consistent methodological thread throughout her work, enabling detailed mechanistic insights into enzyme function. Professor Das has received numerous prestigious awards recognizing her research excellence and teaching: 2024 NIH Outstanding Researcher Award (MIRA R35) for established investigators 2024 Vasser Woolley Faculty Fellowship 2023 Plenary Lecture at the International Society of the Study of Xenobiotics (ISSX) 2021 E.L.R. Stokstad Award 2019-2021 List of Teachers Ranked as Excellent 2019 Eicosanoid Research Foundation Young Investigator Award 2019 Zoetis Research Excellence Award 2019 Mary Swartz Rose Young Investigator Award 2015 National Scientist Development Award from the American Heart Association 2022 El Sohly Award from the American Chemical Society Professor Das actively mentors a diverse group of students and postdoctoral researchers, with several former lab members now holding faculty positions or working at prestigious institutions. Her laboratory has secured significant funding from NIH, NSF, and other sources to support research on lipid metabolism, cannabinoid pharmacology, and membrane protein biochemistry. Notable grants include an NIH R35 Outstanding Investigator Award (MIRA), an NIH R21 grant from NIDA, and multiple collaborative grants with other research groups. The Das Laboratory operates within the Petit Institute of Bioengineering and Biosciences (IBB) at Georgia Tech, utilizing state-of-the-art facilities for biochemical and biophysical studies. The lab specializes in nanodisc technology to study membrane proteins in near-native environments, with particular expertise in cytochrome P450 enzymes and their interactions with lipid substrates. Recent work has expanded into collaborative projects involving lipidomics, structural biology, and translational applications of lipid signaling research.
Chi Liu is a Professor of Radiology & Biomedical Imaging at Yale School of Medicine . He serves as Associate Director of Biomedical Imaging Technology at the Yale Biomedical Imaging Institute and Director for Research Faculty Affairs in the Radiology & Biomedical Imaging department. Education : PhD from Johns Hopkins University (2008) Postdoctoral Training : University of Washington (2010) Certification : American Board of Science in Nuclear Medicine (Nuclear Medicine Physics and Instrumentation) His research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging , emphasizing deep learning algorithms , reconstruction algorithms , data correction , and dynamic imaging . Key clinical applications include early detection of chemotherapy-induced cardiotoxicity , multimodality imaging of heart failure , and motion variability elimination in therapy response assessment . The 15 most recent publications reveal a strong emphasis on deep learning techniques for low-dose imaging , motion correction , and cross-tracer generalizability in PET/SPECT systems. These works span applications in cardiac imaging , neuroscience , oncology , and theranostics . Scientific Award : Bruce Hasegawa Young Investigator Medical Imaging Science Award (2012) Contact: chi.liu@yale.edu | ORCID 0000-0002-7007-1037
Professor Shaun Gregory is the Director of the Centre for Biomedical Technologies at Queensland University of Technology (QUT), where he also serves as Co-Director of the Artificial Heart Frontiers Program, Founder and Director of the Heart Hackathon student team competition, and Director of the CardioRespiratory Engineering and Technology Laboratory. He holds appointments in the Faculty of Engineering, School of Mechanical, Medical & Process Engineering. His educational background includes Bachelor, Masters (research), and PhD degrees, all awarded by QUT. He also holds both NHMRC and Heart Foundation fellowships, demonstrating his significant contributions to cardiovascular research. Professor Gregory's research applies a translational approach to cardiovascular engineering with a particular focus on devices used to support or replace the heart. His work brings together multidisciplinary teams of engineering, biomedical science, design, and medicine to develop novel technical solutions for clinically relevant problems. His research has changed clinical practice on numerous occasions and assisted with the regulatory approval of medical devices. His areas of interest include mechanical circulatory support, artificial heart development, cardiovascular device engineering, and hemodynamics. His publication portfolio demonstrates a strong focus on extracorporeal membrane oxygenation (ECMO), ventricular assist devices, and cardiovascular device testing. His recent work has explored computational fluid dynamics in blood flow analysis, novel cannula design for circulatory support, and the hemodynamic effects of various cardiovascular devices. His research often bridges engineering principles with clinical applications, resulting in practical innovations in cardiac support technologies. NHMRC Fellowship Heart Foundation Fellowship President-Elect of the International Society for Mechanical Circulatory Support Professor Gregory has successfully secured more than $65 million in research funding and has published over 100 research articles in his field. He is actively involved in mentoring the next generation of researchers, currently accepting Honours, Masters, and PhD students. His CardioRespiratory Engineering and Technology Laboratory serves as a hub for interdisciplinary research that brings together engineering, biomedical science, and clinical expertise to address critical challenges in cardiovascular medicine.
Gaetano Valenza is an Associate Professor of Bioengineering at the University of Pisa, Italy, where he leads the Neuro-Cardiovascular Intelligence Lab at the Enrico Piaggio Research Centre. He holds affiliations with the Neuroscience Statistics Research Laboratory at MIT and has served as a Research Fellow at Harvard Medical School and Massachusetts General Hospital. His academic work spans bioengineering, computational physiology, and affective computing. His research focuses on statistical and nonlinear biomedical signal and image processing , cardiovascular and neural modeling , and physiologically interpretable artificial intelligence . He develops wearable systems for physiological monitoring, with applications in autonomic nervous system assessment, brain-heart interactions, and mental health. His work has led to novel metrics such as the Sympathetic and Parasympathetic activity indices derived from ECG. The 15 most recent publications reflect a consistent trend in brain-heart interplay , complexity analysis of physiological signals , explainable AI in healthcare , and virtual reality applications in mental health . His work integrates advanced signal processing, nonlinear dynamics, and machine learning to decode emotional and cognitive states from physiological data. Dr. Valenza is a Senior Member of IEEE and serves on several technical committees. He is an active editorial leader, currently serving as Associate Editor for IEEE-EMBC , Plos One , Complexity , and Scientific Reports , and has guest-edited special issues in Philosophical Transactions of the Royal Society A and IEEE Journal of Biomedical and Health Informatics . He has led or participated in numerous international research projects, including FP7 and H2020 initiatives such as NEVERMIND and EXPERIENCE. He teaches courses in Biostatistics, Probability & Biostatistics, and Advanced Image Processing at the University of Pisa. As lab head and project coordinator, he leads a multidisciplinary team working on neuro-cardiovascular intelligence, wearable systems, and AI-driven mental health interventions.
Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.
دانشیار
Mathias Funk is an Associate Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), leading the Things Ecology lab. His research focuses on designing with data and systems behavior, particularly in IoT and medical technology contexts. He co-founded UXsuite GmbH and developed the OOCSI communication framework. With a PhD in Electrical Engineering from TU/e (2011), Funk has held academic roles since 2013, including postdoctoral positions in Electrical Engineering and Industrial Design. He teaches courses like 'Data-enabled design' and supervises projects on AI, data foundry, and OOCSI applications. His work bridges engineering and design, addressing sustainability and human values through systemic design approaches. Education: PhD in Electrical Engineering, TU/e (2011) Computer Science background from RWTH Aachen Postdoc at TU/e's Electrical Engineering and Industrial Design departments Research Interests: Data-driven design methodologies, IoT systems, medical technology innovation, sustainable design practices, and interactive systems for musical expression. Recent work emphasizes telemonitoring efficacy in heart failure care and federated learning applications. Awards: Best Paper Award (2014) Honorable Mention (2021) Grants/Projects: Leading initiatives like MEDICAID (medtech solutions), ACACIA (interpretable AI), and Data-Driven Servitizing in Professional Printing. Active in clinical workflow prototyping and cardiovascular disease detection projects. Lab Activities: Manages the Things Ecology lab, focusing on systemic design challenges in smart ecosystems. Collaborates internationally through spin-offs and visiting lectureships.
Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford’s Institute of Biomedical Engineering. He leads the Computational Health Informatics (CHI) Lab, focusing on AI-driven healthcare solutions with a strong emphasis on translational research in low- and middle-income countries (LMICs). His work spans digital health technologies, medical imaging analysis, and AI ethics. Clifton holds multiple fellowships, including from the Alan Turing Institute and Fudan University. Key affiliations include co-directorship of the Oxford-CityU Centre for Cardiovascular Engineering and involvement in the Wellcome Trust’s Flagship Centre in Vietnam. His research has been commercialized through spinouts like OBS Medical and Oxehealth. Notable projects include AI tools for non-invasive vital sign monitoring and pandemic response strategies using audio-based health data. Clifton’s awards include the IEEE Early Career Award (2022) and the Vice-Chancellor’s Innovation Prize. His lab’s Suzhou branch focuses on open-source digital health research using public datasets. Current research themes include multimodal data integration, generative AI in healthcare, and equitable AI deployment across global health systems.
Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.
W. Hong Yeo is a Professor in the Woodruff School of Mechanical Engineering and Program Faculty in Bioengineering at the Georgia Institute of Technology, where he also directs the WISH Center. He holds adjunct appointments in the Wallace H. Coulter Department of Biomedical Engineering. Previously, he was an Assistant Professor at Virginia Commonwealth University (2014-2016) and a postdoctoral fellow at the University of Illinois Urbana-Champaign's Beckman Institute. Dr. Yeo's research integrates nanomechanics, soft materials, and nano-microfabrication to develop bio-interfaced systems. Key areas include: Flexible Bioelectronics : Wearable/implantable sensors for health monitoring Human-Machine Interfaces : Neural prosthetics and soft robotics Translational Nanoengineering : Nanoparticle biosensing and diagnostics His publications (2023-2025) demonstrate strong focus on wireless health technologies, including multi-modal wearable systems, implantable sensors for cardiovascular/neurological monitoring, and AI-integrated diagnostics. Trends show increasing emphasis on closed-loop therapeutic systems and scalable manufacturing. Awards & Recognition : BMES Innovation and Career Development Award Virginia Commercialization Award Blavatnik Award Nominee NSF Summer Institute Fellowship Research funding sources include MEDARVA Foundation, NIH, DARPA, and industry partners like CooperVision. He leads the Center for Human-Centric Interfaces & Engineering , developing next-generation bio-interfaced systems.
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