جستجوی استاد راهنمای دانشگاهی

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +۵ مورد دیگر

Professor Zhiyong Wang is a faculty member at the School of Computer Science, University of Sydney, serving as Deputy Director of Sydney Informatics Hub and Director of the Multimedia Computing Laboratory. His research focuses on multimedia computing, including information retrieval, computer vision, AI, and applications in agriculture, healthcare, and environmental monitoring. He holds a B.Eng., M.Eng. from South China University of Technology, and a Ph.D. from The Hong Kong Polytechnic University. His work bridges theoretical advancements and real-world applications, with over 200 peer-reviewed publications. He is a member of IEEE and former President of Australia Pattern Recognition Society (APRS). Research Interests: Enabling computers to understand and create multimedia content, with emphasis on human-centric computing, remote sensing, and interdisciplinary applications. Notable areas include action recognition, medical image analysis, and environmental monitoring. Current Roles: Leads the Multimedia Computing Lab, collaborates with Sydney Institute of Agriculture and Brain and Mind Centre. Teaches COMP5216 (Mobile Computing), COMP5405 (Digital Media Computing), and COMP5425 (Multimedia Retrieval). Students: Supervises 9 Ph.D. students focusing on topics like precision agriculture, biomedical engineering, and AI-driven video analytics. Key projects include soil moisture forecasting, biomechanical risk analysis for knee osteoarthritis, and explainable machine learning.

• Multimedia Computing
• Computer Vision
• Computer Graphics
• +۷ مورد دیگر

Guillaume DUCOURNAU is a **Professor** at the **Institute of Electronics, Microelectronics and Nanotechnologies (IEMN)**, part of the **University of Lille**, France. His research focuses on **Terahertz (THz) wireless communications**, including the development of THz sources, detectors (e.g., photomixers, Schottky diodes), and instrumentation for millimeter-wave and THz characterization. He leads projects such as **GRAPH-X**, **TIMES (6G SNS)**, and coordinates ANR-funded programs like **FUNTERA** (THz converters) and **SYSTERA** (systems beyond 90 GHz for future networks). His work spans European collaborations (e.g., **ThoR H2020**) and national initiatives, including the COM'TONIQ project for 300 GHz THz communications. **Research Interests**: THz communication systems, photonic integrated circuits, semiconductor device characterization (e.g., GaN Schottky diodes), on-wafer measurement techniques up to 1.1 THz, and hybrid packaging strategies for silicon ICs. His contributions include advancements in frequency stability, high-data-rate links (e.g., 327 Gbps), and topological photonics for 6G applications. **Key Projects**: STREP ROOTHz (2010-2013), TERASONIC (ANR/DFG), France 2030 PEPR programs. **Grants**: ANR INFRA 2013, ANR PEPR, Marie-Curie TERAOPTICS network. **Labs & Groups**: Active in the **PHOTONIQUE THz** and **EPIPHY** groups at IEMN, focusing on device characterization, silicon technologies, and collaborative research across 12+ labs in SYSTERA.

• Terahertz (THz) Communications
• Photonics
• Semiconductor Devices
• +۳ مورد دیگر

Kuo-Ming Chao is a Researcher at the Centre for Research in Arts and Creative Exchange within the School of Arts, Humanities, and Social Sciences. His work bridges computational methods and applied research, with focus areas spanning medical imaging systems, distributed computing architectures, acoustic positioning technologies, social network analysis, and multimodal misinformation detection. Chao maintains active involvement in the academic community through sustained editorial contributions and conference participation. Research Focus: His interdisciplinary research integrates computer science with domain-specific applications. Key themes include: Development of orchestrated workflows for medical image analysis systems Multimodal fusion techniques for information verification Acoustic signal processing for location tracking Graph-based algorithms for social network prediction Professional Activities: Peer reviewer for Service-Oriented Computing and Applications journal since 2006 Invited keynote speaker for the 2025 IEEE Conference on Artificial Intelligence

• Medical Image Analysis
• Distributed Systems
• Acoustic Location
• +۲ مورد دیگر

ZGHAL Mourad is a Researcher-Lecturer at CESI LINEACT, holding an HDR (2008) from Sup’Com, Carthage University and a PhD in Electrical Engineering (2000) from University Tunis Manar. He specializes in Optimization, IoT, Sensors, and Smart Healthy Cities , with a strong focus on Photonic Crystal Fibers and Nonlinear Optics . Education: HDR in Engineering (2008), Sup’Com, Carthage University PhD in Electrical Engineering (2000), University Tunis Manar Engineering Degree in Telecommunications (1995), Sup’Com, Carthage University Research Interests: Mourad’s work bridges IoT sensor networks with optical communication systems . He pioneers mid-infrared supercontinuum generation in chalcogenide fibers and explores optical mode multiplexing for high-speed communications. His recent work integrates federated learning for intrusion detection in smart grids and optimizes photovoltaic energy systems for building decarbonization . Publications Trends: His 2023–2025 work emphasizes AI-driven energy management , cybersecurity for IoT , and federated learning frameworks . Earlier contributions (2016–2019) focused on nonlinear optical effects in photonic fibers and high-bit-rate networks . Awards: Elected Vice-Präsident of the International Commission for Optics Fellow Optica (ex OSA) and SPIE Associate scientist at ICTP (UNESCO Category 1 Institute) Advising & Grants: Supervised 9 PhD students (e.g., Z. MONLA’s work on BIM/VR in building maintenance). Active member of the LINEACT Scientific Council and CTI Commission des Titres d’Ingénieurs. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT. Collaborates with IMT Télécom SudParis as an Adjunct Professor.

• Optimization
• IoT
• Sensors
• +۳ مورد دیگر

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +۳ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Bharat Biswal is a Distinguished Professor in the Department of Bio-Medical Engineering at New Jersey Institute of Technology (NJIT), serving as Director of the Center for Brain Imaging. His primary affiliation is with the College of Engineering, where he leads neuroimaging research initiatives. Biswal’s work focuses on functional magnetic resonance imaging (fMRI), brain connectivity analysis, and translational applications in neuropsychiatric disorders. Research Interests: Functional and Resting-State fMRI Methodology Neurovascular Coupling Mechanisms Connectomics and Network Neuroscience Clinical Applications in ADHD, OCD, and Neurodegenerative Diseases Post-COVID Neuroimaging White Matter Function Grant Activity: NIH-funded projects on laminar-specific connectivity (2022–2025) National Science Foundation MRI infrastructure grants (2019–2021) Longitudinal HIV brain studies (2015–2018) Recent Articles Highlight: Biswal’s 2025 work advances understanding of cocaine use disorder neurobiology, obsessive-compulsive disorder network dysregulation, and standardized PET nomenclature. His lab also innovates in AI-driven defect detection and transcriptomic-neuroimaging integrations. Awards: Recipient of NJIT’s 2024 Excellence in Research Award for pioneering contributions to resting-state fMRI and brain connectivity research. Labs/Teams: Leads the Center for Brain Imaging at NJIT, collaborating internationally on neuroimaging standards and translational neuroscience projects.

• Functional Magnetic Resonance Imaging (fMRI)
• Functional Connectivity
• Resting-state fMRI
• +۵ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.

• Electrical Engineering
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +۳ مورد دیگر

Han Mao Kiah is an Assistant Professor at Nanyang Technological University , specializing in Coding Theory and Combinatorics . He earned his Ph.D. in Mathematics at NTU under Yeow Meng Chee and held a postdoctoral position at the Coordinated Science Lab, University of Illinois at Urbana-Champagne with Olgica Milenkovic . Current Role: Assistant Professor, NTU, Department of Mathematics Education: Ph.D. in Mathematics (NTU), Postdoc (University of Illinois) His research focuses on Coding Theory for applications in DNA-based data storage , Reed-Solomon codes , and combinatorial designs . Recent work includes Private Information Retrieval , Sequence Reconstruction , and Error Correction in distributed systems. Key trends in his publications involve Reed-Solomon codes , Private Information Retrieval , and DNA sequence profiling with a focus on Efficient Algorithms and Constrained Coding for error control in emerging storage systems. ISITA Early Career Researcher Paper Award (2022, Researcher: D. T. Dao) Memorable Paper Award Finalist (2021, Student: J. Chrisnata) Best Student Paper Award (2020, Student: J. Chrisnata) Student Paper Award Finalist (2012)

• Coding Theory
• Combinatorics
• Information Theory
• +۱ مورد دیگر

Prof. Amila Akagic is an Associate Professor at the Department of Computer Science and Informatics, Faculty of Electrical Engineering, University of Sarajevo. Her expertise spans Computer Architecture , Artificial Intelligence , Computer Vision , and High-Performance Computing . She holds a Ph.D. from Keio University (Amano Lab) and has conducted research at UC Riverside and the University of Ljubljana under prestigious scholarships including the Fulbright and MEXT awards. Her work focuses on FPGA-accelerated algorithms, reconfigurable architectures, and deep learning applications in medical imaging and environmental monitoring. Education: Bachelor's/Master's (Electrical Engineering) – University of Sarajevo (2006/2009) Ph.D. (Computer Science) – Keio University, Japan (2013) Research Interests: Combines image segmentation , embedded systems , and energy-efficient computing . Recent work includes semantic tumor segmentation (MRI), blockchain-based UXO tracking, and FPGA-based CRC acceleration. She explores interdisciplinary topics like AI-driven plant phenotyping and wildfire detection using computer vision. Recent Trends in Publications: Over 40% of her 2023-2025 articles address medical imaging and fire detection , with growing emphasis on explainable AI and humanitarian technology . Her work frequently uses frameworks like OpenVINO and TensorFlow to optimize computational efficiency. Awards: Fulbright Visiting Student Award (2007/2008) MEXT Scholarship (Japan, 2010) Grants & Labs: Active in the HiPEAC project (EU Horizon Europe), focusing on edge computing and smart grid resilience. Collaborates with the Embedded Systems and Architectures Lab (UC Riverside) and Amano Lab (Keio University). Labs/Teams: Leads research groups in FPGA-accelerated architectures and AI-driven environmental sensing. Engages in humanitarian demining initiatives via data observatories and blockchain systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۵ مورد دیگر

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

• Computer Science
• Electrical Engineering
• Robotics
• +۷ مورد دیگر

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +۳ مورد دیگر