استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۷۲۱-۷۳۵ از ۱۷٬۰۳۰ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Edward Hirschowitz, MD is an Associate Professor in the field of Internal Medicine with significant affiliations including the Markey Cancer Center , the Cancer Research Priority Initiative , and the Translational Oncology Research Program . His academic and clinical work focuses on advancing cancer research and treatment through innovative methodologies.
دانشیار
Dr. Ulas Bagci is an Associate Professor at Northwestern University's Feinberg School of Medicine, Department of Radiology. He holds courtesy appointments in Biomedical Engineering (BME), Electrical and Computer Engineering (ECE) at Northwestern, and Computer Science at the University of Central Florida. As the director of the Machine and Hybrid Intelligence Lab, his research focuses on AI and machine learning applications in biomedical and clinical imaging. Education: BS: Bilkent University (2003) MS: Koç University (2005) Fellow: University of Pennsylvania (2009) PhD: University of Nottingham (2010) ISTP Fellow: NIH (2012) Research Interests: Dr. Bagci’s work spans artificial intelligence, machine learning, and their integration into medical imaging workflows. His lab develops algorithms for tumor segmentation, radiomics analysis, and ethical AI frameworks in healthcare. Notable projects include large-scale MRI segmentation of cirrhotic livers and predictive models for clinical outcomes in oncology and cardiology. Publications: His recent work emphasizes AI-driven solutions for challenges in radiology, including lung disease detection, pulmonary embolism mortality prediction, and ethical considerations in foundational AI models. His articles reflect a focus on bridging clinical needs with advanced computational methods. Lab & Affiliations: The Machine and Hybrid Intelligence Lab collaborates with the Robert H. Lurie Comprehensive Cancer Center. Research themes include federated learning, medical image synthesis, and AI ethics in clinical decision-making.
استادیار
Kejun Huang is an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida's Herbert Wertheim College of Engineering. His primary research area is Machine Learning, with additional interests in algorithms, computer vision, and data science. He received his Ph.D. in Electrical Engineering from the University of Minnesota in 2016. His research focuses on machine learning, signal processing, optimization, and statistics. Recent work tackles unsupervised learning challenges and AI-powered medical research through NIH-funded projects. Dr. Huang's publications demonstrate consistent focus on optimization techniques for tensor decomposition, dictionary learning identifiability, and nonnegative matrix factorization. Key themes include algorithmic efficiency and theoretical guarantees in machine learning models.
استادیار
Jeremy Hoskins serves as an Assistant Professor in the Department of Statistics at the University of Chicago and is affiliated with the Committee on Computational and Applied Mathematics (CCAM). His office is located in room 120A with contact number 773-834-3863. He received his PhD in applied mathematics from the University of Michigan and previously held a Gibbs assistant professorship in mathematics at Yale University. His research bridges physics, computation, and mathematics with emphasis on mathematical foundations of imaging in highly-scattering and quantum environments. Dr. Hoskins develops efficient algorithms for large-scale optical system simulations, yielding applications across signal processing, genomics, acoustics, and medical imaging. Professional recognition includes: 2025 Sloan Research Fellow (announced February 18, 2025) No details were provided regarding student advising, research grants, or laboratory collaborations in the source material.
Professor Yue Rong is a Full Professor at Curtin University's Department of Electrical and Computer Engineering, within the School of Electrical Engineering, Computing and Mathematical Sciences. He holds editorial roles at IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters. His research focuses on signal processing for communications, underwater acoustic systems, wireless networks, and healthcare IoT. Rong has authored over 140 journal and conference papers and received multiple awards, including the 2010 Young Researcher of the Year Award. Education: B.E. (Electrical Engineering), Shanghai Jiao Tong University (1999) M.Sc. (Electrical Engineering), University of Duisburg-Essen (2002) Ph.D. (Electrical Engineering), Darmstadt University of Technology (2005) Research Interests: Rong's work spans cooperative MIMO communications, underwater acoustic systems, OFDM modulation, radar-based healthcare monitoring, and secure wireless protocols. His innovations include adaptive modulation schemes for underwater environments and radar-based vital signs detection. Recent trends in his publications emphasize AI-driven signal processing for healthcare IoT and underwater optical communication systems. Awards: Best Paper Awards (WCSP 2011, APCOMM 2010) Chinese Government Award (2004) DAAD/ABB Fellowship (2001-2002) Grants & Labs: His research is supported by grants focusing on UAV-enabled data collection and underwater network optimization. He leads projects in the Distributed Data Fusion and Emerging Technologies (DDFE) lab, advancing radar-cardiography and wearable health monitoring systems.
استادیار
Aonghus Lawlor is an Assistant Professor/Lecturer in Computer Science at the School of Computer Science, University College Dublin. His roles include coordinating modules such as Software Engineering, Data Structures, Machine Learning, and Final Year Project Foundations. He holds an Orcid identifier: 0000-0002-6160-4639. His research focuses on machine learning applications in medical imaging (e.g., MRI, CT), sports science, and healthcare systems. Notable areas include AI-driven diagnostics, cybersecurity in radiology, and genomics for agricultural optimization. Recent work explores ChatGPT4-vision in MS progression, knee osteoarthritis grading via anomaly detection, and reinforcement learning in exercise prescriptions. Professional activities include committee roles in ACM Recommender Systems and Intelligent User Interfaces, grant assessments, and peer reviewing. He has published 137+ outputs, emphasizing interdisciplinary AI solutions with clinical and agricultural impact. Teaching responsibilities span foundational CS courses to advanced ML and project modules. No formal awards are listed, but his work demonstrates contributions to AI ethics, health informatics, and agricultural genomics.
دانشیار
Wooram Park is an Associate Professor in the Department of Mechanical Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He leads the Robotics and Intelligent Systems Laboratory (ROBINS Lab) and holds a PhD from Johns Hopkins University (2008), along with MS and BS degrees from Seoul National University (2003 and 1999). His research focuses on robotics, biomedical robotics, computational structural biology, and image processing. Key projects include flexible needle steering for medical applications, haptic feedback systems, and advanced algorithms for motion planning and image reconstruction. He has received notable awards such as the Creel Fellowship (2007) and Critics’ Choice Award in ArtBot Design (2004). His work spans theoretical contributions in stochastic systems and practical innovations like vibratory magnetic robots (Vimbot) and wearable haptic devices. The ROBINS Lab emphasizes interdisciplinary research at the intersection of mechanical engineering, computer science, and biomedical applications.
Dr. Esam Abdel-Raheem is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor, Faculty of Engineering. His research focuses on digital signal processing, biomedical engineering, cognitive radio networks, and VLSI design. He holds a Ph.D. from the University of Victoria (1995) and is a Professional Engineer (P.Eng.) in Ontario and a Senior Member of IEEE. Education: B.Sc. Electrical Engineering, Ain Shams University (1984) M.Sc. Electrical Engineering, Ain Shams University (1989) Ph.D. Electrical Engineering, University of Victoria (1995) Research Interests: Dr. Abdel-Raheem’s work spans signal processing for communications, biomedical signal processing, and VLSI implementations. He has pioneered algorithms for cognitive radio networks and adaptive filtering. His recent studies leverage deep learning for medical diagnostics (e.g., lung nodule detection, Parkinson’s disease voice analysis) and cognitive radio spectrum sensing. Publications Trends: Recent work emphasizes biomedical applications (e.g., CT scan analysis, diabetic retinopathy detection) and machine learning integration in communications (e.g., federated learning for traffic crowdsourcing). His articles often bridge theoretical signal processing with practical implementations in hardware (e.g., FPGA-based filters). Awards/Grants: Not explicitly listed in the text, though his senior IEEE membership and prolific publications suggest sustained professional recognition. Lab/Teams: While not detailed, his research themes imply involvement in interdisciplinary teams focusing on biomedical engineering, telecommunications, and VLSI design.
Adrian Chan is a Professor at Carleton University's Department of Systems and Computer Engineering, Faculty of Engineering and Design. He holds the title of Director of the Research and Education in Accessibility, Design, and Innovation (READi) program. His expertise spans biomedical engineering, signal processing, and accessibility technologies. Education: Ph.D. in Electrical Engineering (University of New Brunswick), M.A.Sc. in Electrical Engineering (University of Toronto), B.A.Sc. in Computer Engineering (University of Waterloo). Research focuses on non-invasive sensors, biomedical signal/image processing, machine learning, and accessibility solutions. Notable projects include the Abilities Living Laboratory and collaborations with healthcare institutions like The Ottawa Hospital. His work addresses challenges in neonatal transport safety, placental imaging for maternal health, and wearable medical devices. Publications highlight advancements in AI-driven ECG analysis, histopathology segmentation, and clinical monitoring systems. Over 150 students have been mentored, with many securing prestigious awards. Awards include the 2024 CMBES Fellowship, 2023 Carleton Research Achievement Award, and 2012 3M Teaching Fellowship. Grants include NSERC CREATE programs and CFI funding for the Abilities Living Laboratory. Leadership roles include interim Assistant Vice-President (Academic), Associate Dean (Graduate Programs), and Shad Valley Program Director. Active in community initiatives like the READi training program and accessibility advocacy.
عضو هیئت علمی
Sujan Kumar Roy is an Assistant Teaching Professor in the Department of Computer Science at Michigan Technological University. He holds a Ph.D. in Machine Learning with Computer Engineering and Signal Processing from Griffith University, Australia, and has earned multiple academic distinctions, including the 'Award of Excellence in the Research Thesis' and consideration for the 'Chancellor's Medal for Excellence in Ph.D. Thesis 2021.' Dr. Roy's teaching focuses on Computational Intelligence, Foundations of Data Science, Machine Learning, Data Mining, and Introduction to Data Science. His research explores applications of AI, ML, and Data Science in Cybersecurity, Medical Image Analysis, Healthcare Systems, and Speech Enhancement. He has contributed extensively to speech enhancement techniques, integrating Kalman filters with machine learning and deep learning approaches. Recent research trends in his publications emphasize the development of robust algorithms for speech enhancement in noisy environments, fusion datasets for hate speech detection, and adaptive filtering methods. His work bridges signal processing and AI, aiming to improve real-time system performance and noise robustness. Awards: Award of Excellence in the Research Thesis Consideration for Chancellor's Medal for Excellence in Ph.D. Thesis 2021 In teaching, Dr. Roy emphasizes foundational concepts and their practical applications. His grants and collaborations focus on advancing AI-driven solutions for healthcare and cybersecurity challenges. He is affiliated with the Department of Computer Science at Michigan Tech, contributing to both academic and research missions.
Alisa Piekny, PhD, is a Professor of Biology and Associate Dean of Research and Infrastructure at Concordia University's School of Health. Her research focuses on cytoskeleton regulation during cell division, migration, and polarity, particularly in cancer cells. She employs human cell cultures, iPSCs, and collaborative drug discovery approaches to advance anti-cancer therapies and nanotechnology-based diagnostic tools. Education: PhD (University of Calgary), Post-Doc (IMP Vienna and University of Chicago). Research interests include anillin protein dynamics, microtubule-actomyosin interactions, and nanoparticle-cell interactions. Her work bridges cell biology with translational medicine, emphasizing cytokinesis mechanisms and their hijacking in cancer. Recent publications highlight innovations in drug delivery systems, fluorescent nanoparticle imaging, and molecular tools for live-cell cytokinesis analysis. Collaborations span nanotechnology, cancer biology, and developmental biology, yielding insights into cell fate regulation and therapeutic targeting. Her research has been recognized through interdisciplinary projects at the interface of biology and materials science, with a focus on practical medical applications.
استاد مهمان
Dr. Neal Bangerter is a Visiting Professor in the Department of Bioengineering at Imperial College London's Faculty of Engineering. He specializes in medical imaging (MRI), artificial intelligence, machine learning, and signal processing. Dr. Bangerter holds adjunct appointments at INSEAD, the University of Utah, and Brigham Young University. His research focuses on ultra-high field MRI, AI applications in healthcare, and data-driven bioscience technologies. He leads the London Collaborative Ultra-High Field Scanner (LOCUS) project and advises companies on AI and innovation strategies. Education: B.S. in Physics (UC Berkeley), M.S. and Ph.D. in Electrical Engineering (Stanford University). Career highlights include roles at McKinsey & Company, Microsoft, and Reactrix, as well as founding BYU's Medical Imaging Research Center. He has pioneered cross-faculty initiatives like the Crocker Innovation Fellowship Program. Research interests include novel MRI pulse sequences, AI in medical imaging, and large-scale health data analysis. His work spans collaborations with Stanford, Oxford, Cambridge, and Siemens Healthcare. He teaches executive education at INSEAD, focusing on bridging technical concepts with business strategies. Key awards include the David Evans Chair at Brigham Young University. His contributions to the UK Biobank Neuroimaging study and development of MRI techniques like RAFO-4 highlight his impact on advancing imaging technologies and AI applications in healthcare.
مدرس ارشد
Dr. Maxime Cordeil is a Senior Lecturer in Human Centred Computing at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on Virtual and Augmented Reality technologies for data interaction, interactive visualization systems, and AR interfaces for industry applications. He has authored over 60 publications in top-tier venues such as ACM CHI and IEEE VR, and was nominated as Australia's Field Leader in Computer Graphics in 2021 and 2022. Research Interests: Data visualization, immersive analytics, medical imaging, collaborative systems, and human-computer interaction. Current Projects: Includes embedded visualizations for sports performance, interactive machine learning in 3D environments, and mixed-reality applications in forensic science. PhD Supervision: Actively guiding students in topics like immersive gesture exploration, AR for digital health, and collaborative VR systems. His work bridges theory and practice, with tools like IATK (Immersive Analytics Toolkit) and the MADE-Axes hardware system. He collaborates with industry partners like Raytracer and CSIRO, focusing on applications in space exploration, underwater training, and remote operations. Awards: Multiple best paper recognitions at ISS and CHI, plus industry-driven research grants. Labs: Leads the Immersive Analytics research group at UQ, specializing in embodied interaction and spatial computing.
Jiebo Luo is the Albert Arendt Hopeman Professor of Engineering and Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD and has been affiliated with the Department of Computer Science since 2011, following a 15-year career at Kodak Research. His research spans computer vision, natural language processing, machine learning, data mining, computational social science, and digital health. Luo is an ACM Fellow, AAAI Fellow, IEEE Fellow, SPIE Fellow, and IAPR Fellow. Education: PhD (specific discipline not explicitly stated in text). Research interests include computer vision, machine learning, data mining, social media analysis, biomedical informatics, human-computer interaction, and ubiquitous computing. He co-authored the book Deep Neural Network for Medical Image Computing: Principles and Applications (Elsevier, 2022). His work has led to nearly 600 technical papers and 90+ U.S. patents. Research Trends: Recent work focuses on large language models (LLMs), multimodal systems, AI-driven social media analysis, and healthcare applications. Key areas include bias analysis in political simulations, video understanding, and benchmark development for AI-generated content evaluation. His research bridges theoretical advancements with practical applications in healthcare, social sciences, and multimedia systems. Awards: ACM SIGMM Technical Achievement Award (2021), IEEE Region 1 Technological Innovation Award (2018), Eastman Innovation Award (2004), and multiple best-paper recognitions at top conferences. Service & Leadership: Served as program co-chair for ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017. Currently Editor-in-Chief of IEEE Transactions on Multimedia (2020–2022). Editorial board roles include several IEEE Transactions journals and conferences. Labs & Teams: Leads research groups in computer vision and multimodal computing at the University of Rochester, collaborating on projects like UroSAM (kidney stone classification) and computational social science initiatives.
Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.
فقط ۷۵۰ نتیجه اول نمایش داده میشود. برای رسیدن به نتایج دقیقتر، جستوجو را با فیلترها محدودتر کنید.
