جستجوی استاد راهنمای دانشگاهی

• Remote Sensing
• Microwave Remote Sensing
• GNSS-Reflectometry
• +۵ مورد دیگر

Hyuk Park is an Assistant Professor at Universitat Politècnica de Catalunya (UPC-BarcelonaTech), affiliated with the Castelldefels School of Telecommunications and Aerospace Engineering. His research focuses on remote sensing, particularly passive microwave remote sensing, system design, modeling, and geophysical data analysis. He holds a B.S. in Mechanical Engineering from KAIST (2001) and M.S./Ph.D. in Information and Mechatronics from GIST (2003/2009). Notable grants include a Korean NRF Grant (2011) and a Juan de la Cierva Grant (Spain). He currently leads the RSLab and CommSensLab, exploring applications like GNSS-Reflectometry (GNSS-R) for soil moisture, ionospheric scintillation, and disaster monitoring. His work integrates satellite data from missions like CYGNSS, FY-3E, and FSSCat to advance environmental monitoring. Recent studies include correlating ionospheric anomalies with earthquakes, improving flood water level estimation, and developing AI-driven agriculture systems. Awards include the Ramon y Cajal Fellowship, highlighting his contributions to sensor technology and Earth observation. Key projects involve instrument design (e.g., FMPL-2 dual payload), algorithm optimization for hyperspectral imaging, and mitigating radio-frequency interference. His research bridges theoretical modeling with practical applications in climate science, geophysics, and disaster response.

Jihoon Ryooمشاهده پروفایل

دانشیار

• Wireless Networking
• Mobile Computing
• Internet of Things
• +۷ مورد دیگر

Jihoon Ryoo is an Associate Professor in the Department of Computer Science at SUNY Korea, where he has been employed since 2017. He directs the AI2S Lab and co-founded the startup IDCITI. He holds a Ph.D. from Stony Brook University (2017) and M.S./B.S. degrees from Korea University. Research Interests: Dr. Ryoo's work focuses on practical implementations in wireless networking and mobile systems, including backscatter communication, IoT connectivity, saliency-based video streaming, and GNSS-independent localization. His projects span uGPS (metro localization), SALI360 (360° video optimization), and autonomous anti-drone systems, often leveraging deep learning and RF analytics. Awards & Grants: Incheon City Mayor Award (Entrepreneur, 2024 & S/W Hackathon, 2020) Prime Minister Awards (ICT Colloquium & Applied Data Competition, 2020) IITP Excellence Research Award (2019) Grants: National XR-Lab initiatives (MSIT), Incheon-RISE program (2025–2030), NRF streaming platform research, and multiple Incheon Techno Park projects. Teaching & Service: Courses include Computer Networks, Computer Vision, Algorithms, and Wireless Networks. Service includes TPC roles (MobiSys, ICCCN), Director of SUNY Korea's ICT CCP Program (2018–2020), and XR-Lab Director (2021–2023).

Khan Wahidمشاهده پروفایل

استاد

• Health informatics
• Wireless capsule endoscopy
• Wearable health monitoring
• +۵ مورد دیگر

Professor Khan Wahid is a faculty member in the Department of Electrical and Computer Engineering at the University of Saskatchewan , affiliated with the Division of Biomedical Engineering . His research focuses on health informatics, IoT infrastructure, medical imaging, and wearable health monitoring , with notable contributions to wireless capsule endoscopy, smart-city applications, and sensor systems. He holds a GCC Stars in Global Health Award (2013) and has secured funding from multiple government and institutional grants. Education: BSc, MSc, PhD in relevant fields (details not specified in text). Research Interests : Health informatics & smart-health systems IoT design/deployment and drone infrastructure Medical imaging reconstruction (CT/MRI) Wireless sensor networks and body area networks Quantum-dot cellular automata (QCA) for nanoelectronics FPGA/ASIC-based embedded systems Funding & Recognition : Harper Government investment in medical imaging (2014) UofS GCC Stars in Global Health Award (2013) Advising & Grants : Supervises graduate students in interdisciplinary projects (specific names not listed). Active in securing grants for IoT-enabled healthcare and agricultural sensing systems. Collaborates on projects involving smart-pill devices, fluorometers for cancer detection, and LiDAR-based plant phenotyping. Labs & Teams : Leads projects integrating biomedical engineering, IoT, and nanotechnology within the College of Engineering. Collaborations span academic and industrial partners for medical device innovation.

Jun Yuمشاهده پروفایل

استاد

• Statistical Learning
• Spatiotemporal Data Analysis
• Compressive Sensing
• +۷ مورد دیگر

Jun Yu is a Full Professor in Mathematical Statistics at the Department of Mathematics and Mathematical Statistics, Umeå University, Sweden, where he also serves as Director of Doctoral Studies. His research focuses on Statistical Learning and Inference for Spatiotemporal Data, with applications in artificial intelligence and various scientific domains. Professor Yu leads a research group dedicated to tackling theoretical data science problems and developing statistical learning methods for solving real-world challenges across multiple disciplines. Professor Yu's primary research interests include statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modeling, nonparametric density/intensity estimation, statistical inference for hidden Markov models, and wavelet theory applied to signal and image analysis. His work spans numerous application areas including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry, hydrology, radiation oncology, spatial ecology, sports science, and transportation. Professor Yu's recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of statistical methodology and environmental science, medical imaging, and transportation systems. His work on tree-ring isotope analysis for climate reconstruction, compressive sensing for medical imaging, and statistical models for train delay prediction shows his ability to develop sophisticated statistical methods that address complex real-world problems across diverse domains. As Director of Doctoral Studies, Professor Yu oversees doctoral education in Mathematical Statistics and has supervised numerous PhD students. His teaching spans mathematical statistics at all levels, from basic education to postgraduate courses, for students in mathematics, statistics, biology, engineering, and forestry, delivered in English, Swedish, or Chinese. Professor Yu leads the research group on statistical learning and inference for spatiotemporal data at Umeå University. This group develops innovative approaches for analyzing complex spatiotemporal datasets using tools such as intelligent data sampling, large-scale environmental data modeling, multimodal image processing, and tree growth models, with applications across multiple scientific fields.

Roel Sniederمشاهده پروفایل

استاد

• Geophysics
• Seismic Interferometry
• STEM Education
• +۳ مورد دیگر

Roel Snieder is the W.M. Keck Distinguished Professor of Professional Development Education at the Colorado School of Mines. His primary affiliation is with the university's academic affairs division, focusing on STEM education, geophysics, and professional ethics. He holds an office in Hill Hall 206A and can be contacted at rsnieder@mines.edu. Snieder's research spans geophysical methodologies, including seismic interferometry, Marchenko imaging, and computational geophysics. He is also deeply engaged in pedagogical innovation, emphasizing ethical practices and emotional intelligence in STEM education. His work integrates advanced mathematical approaches with practical applications in wavefield reconstruction and subsurface imaging. Notable contributions include the development of Marchenko-based imaging techniques and the exploration of pedagogical love in academia. His books, such as The Art of Being a Scientist and The Joy of Science , reflect his commitment to fostering scientific curiosity and ethical professionalism. His recent publications highlight advancements in compressive sensing for seismic data, energy partitioning in elastic media, and the ethical dimensions of engineering education. Snieder's interdisciplinary approach bridges geophysics with educational theory, influencing both technical and humanistic aspects of academic practice.

• Geoinformatics
• Intelligence Informatics
• Geospatial Data Mining
• +۷ مورد دیگر

Ickjai Lee is an Associate Professor at the School of IT, James Cook University, specializing in geoinformatics and intelligence informatics. He leads the Information Technology department and has held roles including Senior Lecturer and Lecturer since 2003. He obtained his PhD in 2002 from the University of Newcastle, Australia. Research Interests: Geospatial data mining, trajectory analysis, Voronoi tessellations, mobile AR, and IoT applications. Projects: Includes VR healthcare trials, AI-driven vehicle damage assessment, and environmental monitoring systems. His work focuses on applying machine learning and geospatial techniques to solve real-world problems in health, environmental, and urban domains. Notable achievements include best paper awards at international conferences and faculty citations for teaching excellence. He collaborates on interdisciplinary projects, such as using AR for marine growth monitoring and developing tools for crime pattern analysis. His research also involves large-scale ecoacoustic data analysis and VR-based education platforms. Lee has supervised numerous PhD students exploring topics like differential privacy in biokinetics, spatio-temporal anomaly detection, and VR in cultural heritage preservation.

• Biomedical Image Analysis
• Digital Image and Video Processing
• Dynamically Reconfigurable Architectures
• +۵ مورد دیگر

Pattichis Marios is an Associate Professor in the Department of Electrical and Computer Engineering and Radiology at the University of New Mexico (UNM). He directs the Image and Video Processing and Communications Lab (ivPCL) and serves on the board of the FPGA Mission Assurance Center (FMAC). His research focuses on biomedical image analysis, dynamically reconfigurable architectures, and educational technology for underrepresented student groups. Education: Ph.D. in Computer Engineering (UT Austin, 1998), dual bachelor's degrees in Mathematics and Computer Sciences (UT Austin, 1991). He has taught 15 courses across multiple universities and secured $15.9M in research funding from NSF, AFRL, and NIH. Research areas include CAD systems for medical imaging, explainable AI, and large-scale video analytics in clinical and educational settings. Developed AM-FM representations for image/video features and contributed to solar image analysis methodologies. Awards: EAMBES Fellow (2022), Harrison Faculty Excellence Award (2006), Best Paper Award (AIAI06), Teacher of the Year (UNM ECE). Current projects include the AOLME educational initiative and the DRASTIC adaptive video processing platform. He edits special issues in IEEE journals and Teachers College Record.

Jing Liuمشاهده پروفایل

استاد

• Cardiovascular Imaging
• MRI Data Acquisition Techniques
• Medical Imaging Technology
• +۳ مورد دیگر

Jing Liu, PhD, is a Professor in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco (UCSF). She holds adjunct faculty roles and leads research in advanced MRI techniques. Her primary focus is developing 3D dynamic MRI methods for cardiovascular imaging, including coronary imaging and cardiac-cine techniques. Dr. Liu's work emphasizes improving scan speed, spatial/temporal resolution, and clinical applicability of MRI. Education: BS (Electrical Engineering, University of Science and Technology of China, 2001), MS (Electrical Engineering, McMaster University, 2003), PhD (Electrical Engineering, University of Wisconsin-Madison, 2008). Postdoctoral Training: Radiology at Weill Cornell Medical College (2008–2010). Research interests include 4D MRI, fast imaging protocols, self-gating techniques, non-cartesian data acquisition, compressed sensing, and parallel imaging. She has pioneered methods for reducing scan time and motion artifacts in clinical settings. Publications reflect her expertise in MRI reconstruction algorithms, cardiac imaging, and automated segmentation. Key contributions include papers on respiratory-resolved 3D cine MRI and non-contrast black blood MRI for abdominal aneurysms. Awards: NIH K25 Award (2012–2017), multiple Distinguished Reviewer accolades, and American Heart Association grants. Professional Affiliations: International Society for Magnetic Resonance in Medicine, American Heart Association, Society of Cardiovascular Magnetic Resonance. Liu’s lab focuses on translational MRI technologies. Current projects include 3D multi-contrast MRI for glioma assessment and developing AI-driven segmentation tools for cardiac and vascular imaging.

Nam Buiمشاهده پروفایل

استادیار

• Mobile/Wearable Health Sensing
• Embedded Intelligent Systems
• Artificial Intelligence
• +۲ مورد دیگر

Nam Bui is an Assistant Professor in the Department of Electrical Engineering at the University of Colorado Denver, where he founded and directs the Intelligent Networked Systems and Cybernetics Laboratory (InsCy Lab). His research focuses on developing intelligent cybernetic systems at the intersection of AI, machine learning, and embedded systems, with applications in mobile/wearable health monitoring and physiological sensing. His research interests span: Innovative sensing techniques for health monitoring Embedded intelligent systems leveraging AI/ML Wearable technology for physiological measurement Multidisciplinary approaches to medical cybernetics Analysis of recent publications reveals consistent themes in wearable health technology development, particularly focusing on non-invasive monitoring techniques for vital signs (blood pressure, respiration, glucose) using ear-worn and behind-ear devices. Key technical innovations include compressed sensing frameworks, closed-loop acoustic stimulation systems, and novel sensing modalities like polarization-based optical detection. Awards and Honors: Best Paper Awards at ACM MobiCom (2019, 2017) ACM SigMobile Research Highlights (2020) Communication of ACM Research Highlights (2021) Colorado OEDIT Award (2024) NASA MUREP Program Award (2024) NSF Sense Award (2024) DoD STTR Grant (2024) Teaching Innovation Award (2023) Dr. Bui actively advises graduate students including Anh-Huy Dinh and undergraduate researchers including Luis Carlos Gutierrez and Alejandro Cordova. His laboratory has secured multiple grants from NSF, NASA, DoD, and Colorado economic development programs. He directs the Intelligent Networked Systems and Cybernetics Laboratory (InsCy Lab), which focuses on practical intelligent systems for health monitoring. The lab emphasizes multidisciplinary collaboration and has developed technologies like in-ear blood pressure monitors and closed-loop sleep aid devices.

• Embedded Systems
• Multimedia Computing
• VLSI Design
• +۶ مورد دیگر

Professor Hua Harry Li serves in the Department of Computer Engineering at San José State University, teaching specialized courses including Embedded Hardware Systems (CMPE 242), Embedded Software (CMPE 244), and Embedded Wireless Systems (CMPE 245). His academic profile spans semiconductor physics, real-time control systems, and multimedia hardware implementation. Education Background: Doctor of Philosophy, University of Iowa, 1989 Master of Science in Electrical and Computer Engineering, University of Iowa, 1984 Bachelor of Science in Electronics Engineering, Tianjin University, 1981 Graduate Studies in Electronic/Electrical Engineering, Tsinghua University, 1982 Li's research integrates embedded systems with intelligent control methodologies , particularly focusing on fuzzy logic applications in semiconductor manufacturing and neural network implementations for real-time processing. His work bridges theoretical algorithms and hardware constraints, evidenced by publications on vision chips, wafer defect detection, and pH neutralization control systems. The Encyclopedia of Electrical and Electronics Engineering features his contributions to neural fuzzy control techniques for semiconductor equipment. Analysis of his 1993-2005 publications reveals three distinct phases: early neural network applications (1993-1994) in wafer manufacturing and radar systems, mid-career expansion into video compression and vision chips (1995-1997), and mature work on embedded video processing systems (2001-2005). This trajectory demonstrates consistent focus on hardware-constrained intelligent systems while adapting to emerging technologies in wireless communications and multimedia computing. Key Honors: Industrial Neural Network Award (1994 World Congress Neural Network Conference) Who’s Who in Science and Engineering (4th Edition, 1999) Who is Who in America (53rd Edition) Professor Li contributes significantly to academic discourse as Guest Editor for IEEE Transactions on CPMT Special Emerging Technology Section (1994, 1996) and encyclopedia contributor. While specific grant details aren't documented, his sustained publication record and industry-recognized awards indicate substantial research funding, particularly in semiconductor process control and embedded vision systems. His teaching portfolio reflects direct alignment with research expertise through advanced embedded systems courses. Current work appears focused on embedded multimedia hardware systems with practical applications in semiconductor manufacturing and real-time control, though explicit details about active research teams or laboratory facilities aren't provided in available materials.

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +۱۲ مورد دیگر

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

Asim Smailagicمشاهده پروفایل

استاد پژوهش

• Wearable Computing
• Context-Aware Systems
• Healthcare Technology
• +۲ مورد دیگر

Asim Smailagic is Research Professor at Carnegie Mellon University directing the Laboratory for Intelligent Interactive Real-Time Computing Systems. His work develops wearable systems and virtual coaches for healthcare applications. Current research creates AI-assisted rehabilitation technologies, privacy-preserving medical imaging systems, and explainable AI for clinical decision support. His virtual coaches combine sensing, machine learning and human-computer interaction for personalized therapy. Recent publications address stroke rehabilitation assessment, algorithmic bias in medical imaging, and privacy-aware medical data compression. These innovations bridge AI with clinical applications for improved patient care. He received the Allen Newell Research Excellence Award, Carnegie Science Center Excellence Award, and leads projects funded by NSF, NIH and DoD.

Simon Heneinمشاهده پروفایل

دانشیار

• Micromechanical Design
• Precision Mechanisms
• Flexure Guidance Systems
• +۸ مورد دیگر

Simon Henein is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Micromechanical and Horological Design Laboratory (INSTANT-LAB) under the School of Engineering and Institute of Mechanical Engineering . Since 2024, he holds a Courtesy Appointment at the Digital Humanities Institute (DHI-GE) . He was Tenure Chairholder at Patek Philippe (2012–2020) and a Visiting Professor at the University of Lausanne's Centre d'études théâtrales (2020–2021). His research bridges micromechanical design and creative pedagogy , focusing on flexure mechanisms for aerospace, biomedical devices, and horology, alongside improvisational arts in education. He has authored over 25 peer-reviewed journal articles and 60+ conference papers , with 35 patents. Notable awards include the Best Teacher Award (2023) , OMEGA Scientific Prize (1996) , and Maillefer SA Award (1996) . His teaching involves 12 ECTS credits annually with over 200 students , including mentorship of 26 Master’s and 60 semester projects . He has directed 6 completed PhD theses and currently supervises 4 PhD students , co-supervising 1 more. His lab, INSTANT-LAB, comprises a dozen researchers focusing on centimeter-scale mechanisms with novel kinematics and technologies.

Ian Wassellمشاهده پروفایل

مدرس ارشد

• Wireless Communications
• Sensor Networks
• Cooperative and Relay Networks
• +۷ مورد دیگر

Dr. Ian Wassell is a University Senior Lecturer at the University of Cambridge's Department of Computer Science and Technology. He holds a PhD from the University of Southampton (1990) and BSc/BEng degrees from the University of Loughborough (1983). His research focuses on wireless communications, sensor networks, and electromagnetic propagation modeling. He has authored over 200 publications since 1999 and supervised 23 PhD and 6 MPhil students. Currently, he leads wireless communications research in the Digital Technology Group and is a Fellow of Churchill College. His research interests include broadband fixed wireless access (FWA), cooperative networks, MIMO systems, compressive sensing, and AI-driven radio propagation models. He teaches Digital Electronics and Hardware Practical Classes at the undergraduate level. Key contributions include developing data-driven propagation models, deep learning applications in wireless networks, and optimizing heterogeneous radio access networks. Dr. Wassell's work integrates theoretical advancements with practical systems, addressing challenges in high-speed rail communications, indoor/outdoor detection, and 6G IoT opportunities. His research bridges signal processing, machine learning, and wireless network design, emphasizing real-world deployment in infrastructure like tunnels and vehicles.

Zijian Wangمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Deep Learning
• Domain Adaptation
• +۵ مورد دیگر

Dr. Zijian Wang is a Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on improving vision model transferability, domain adaptation, and deep learning applications in computer vision and remote sensing. He holds a PhD in Computer Science from The University of Queensland (2023). Research interests include domain adaptation techniques, neural network generalization, unsupervised learning, and applications in satellite imagery analysis, vegetation segmentation, and disaster damage assessment. His work emphasizes practical solutions for open-world domain shifts and efficient data utilization. Recent publications explore transfer learning via feature space perturbation, neural network-based satellite image delivery (MIDNet), and benchmark datasets for model generalization (CIFAR-10-Warehouse). He collaborates on platforms like FluMA for influenza monitoring and RoadAtlas for infrastructure management. Currently advises PhD candidates in vegetation segmentation for Australian wheat data and crop health assessment. Active in top conferences (CVPR, ICCV, NeurIPS) and journals (IEEE TPAMI, TMM).