جست‌وجوی استاد و پژوهشگر

Faramarz Fekriمشاهده پروفایل

استاد

• Machine Learning
• Information Theory
• Semantic Communication
• +۷ مورد دیگر

Dr. Faramarz Fekri is the John Pippin Chair Professor and ECE-GTRI Fellow at Georgia Tech's School of Electrical and Computer Engineering. He leads the SENTINEL Research Lab, focusing on interdisciplinary research in machine learning, semantic communication, causal discovery, and biomarker sensing. His work bridges theoretical foundations with practical applications in federated learning, neuro-symbolic AI, and molecular communication. He has held editorial roles at IEEE Transactions journals and has received numerous awards including IEEE Fellow (2015) and the Sony Faculty Research Innovation Award (2018). His research spans over 150 publications, with recent emphasis on differentiable inductive logic programming, adversarial defense frameworks, and compressed sensing systems. Education: B.Sc./M.Sc. from Sharif University, Ph.D. from Georgia Tech. Affiliations include the Center for Machine Learning and the Center for Energy and Geo Processing (CeGP). Current projects include learning via inductive logic reasoning, neuro-symbolic reinforcement learning, and causal discovery from data. Notable achievements include developing BP-based trust systems, network compression frameworks using finite-field wavelets, and frameworks for analog joint source-channel coding. His SENTINEL Lab collaborates on biomarker sensing and molecular communication in biological systems.

Georgios Exarchakisمشاهده پروفایل

مدرس

• Machine Learning
• Theoretical Neuroscience
• Data Science
• +۱۶ مورد دیگر

Georgios Exarchakis is a Lecturer at the University of Bath, specializing in Machine Learning, Theoretical Neuroscience, and Data Science. His research emphasizes transparent and interpretable modeling, with applications in Neuromorphic Engineering, Health Science, and Quantum Chemistry. He has held research positions at IHU Strasbourg, Institut de la Vision, and École Normale Supérieure, and earned his PhD from the University of Oldenburg. Education: Dr. rer. nat. in Machine Learning, 2016, Carl von Ossietzky University of Oldenburg M.Sc. in Computational Science, 2012, Goethe University Frankfurt Diploma in Mathematics, 2008, Aristotle University of Thessaloniki His research interests span Interpretable Machine Learning, Invariant Representations, Sparse Coding, Wavelet Scattering, Probabilistic Models, and Deep Learning . He investigates how models can extract meaningful, stable features from complex data, drawing inspiration from biological systems and theoretical neuroscience. His work often bridges theory and application, particularly in quantum chemistry and neurotechnology. His recent publications highlight a strong focus on efficient and interpretable models , including wavelet scattering for molecular property prediction, discrete sparse coding, and clustering algorithms for event-based vision. The articles show a consistent theme of developing mathematically grounded, invariant, and scalable methods for data analysis. He is the developer or a key contributor to open-source libraries such as Kymatio (Scattering Transforms in Python) and ProSper (Probabilistic Sparse Coding), which facilitate the use of advanced signal processing and learning techniques in the research community. Georgios has taught Machine Learning courses at the University of Strasbourg, École Polytechnique, and Oldenburg. He has collaborated with leading researchers like Stéphane Mallat and Jörg Lücke. His work is published in top-tier venues including CVPR, NIPS, JMLR, and JCP.

Hafeez Ullah Aminمشاهده پروفایل

مدرس ارشد

• Neuroimaging
• Biomedical Signal Processing
• EEG Signal Processing with Machine Learning
• +۳ مورد دیگر

Hafeez Ullah Amin is a Senior Lecturer in the School of Computer Science at Edge Hill University, UK. Previously, he served as an Assistant Professor at the University of Nottingham Malaysia Campus (2020–2023) and held various research positions at Universiti Teknologi PETRONAS (UTP), including Postdoctoral Researcher and Research Scientist. He holds a PhD in Electrical and Electronic Engineering from UTP, specializing in EEG Signal Processing with Machine Learning. His expertise spans Neuroimaging, Biomedical Signal Processing, and Applied AI/ML in healthcare and education. Education: PhD (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, 2011–2015 MPhil (Computer Science with AI), Kohat University of Science and Technology, 2006–2009 BSc (Information Technology), Kohat University of Science and Technology, 2001–2005 Research Interests: Neuroimaging, EEG Signal Processing, AI in Mental Healthcare, Data Analytics, and Machine Learning applications in education and business. His work intersects neuroscience, biomedical engineering, and computational methods to address challenges in memory assessment, stress mitigation, and learning efficacy. Publications: Over 50 articles in high-impact journals and conferences, including work on EEG-based neurofeedback, machine learning for healthcare analytics, and 3D educational content efficacy. Recent trends focus on EEG functional connectivity, fraud detection via graph autoencoders, and energy forecasting. Awards: Senior Member, IEEE Fellow (FHEA), Advance HE, UK Advising & Grants: Supervises PhD students in AI/ML applications. Active in research grants involving EEG experiments, patent on LTM assessment via EEG, and collaborations in smart cities and energy systems. Teaching: Leads courses in Data Science, AI, Microprocessor Systems, and Knowledge Representation. Past roles include Module Leader for Programming Principles and Techniques. Labs/Tech Teams: Associated with Edge Hill's Data and Complex Systems Research Centre and the International Centre for Applied Research in Education.

Raviraj Adveمشاهده پروفایل

استاد

• Wireless Communications
• Radar Signal Processing
• Cooperative Networks
• +۷ مورد دیگر

Professor Raviraj Adve is affiliated with the University of Toronto , working in the Department of Electrical and Computer Engineering and cross-listed with the Electromagnetics Group . He holds a PhD in Electrical Engineering from Syracuse University (1996) and previously worked at Research Associates for Defense Conversion Inc. (1997–2000). Education: B.Tech, Indian Institute of Technology Bombay (1990) PhD, Syracuse University (1996) Research Interests span two main areas: Wireless Communications (heterogeneous networks, massive MIMO, cooperative communication, resource allocation) and Radar Signal Processing (waveform diversity, space-time adaptive processing, HF radar systems). His work emphasizes practical algorithm development for real-world constraints. Scientific Contributions include 15 recent articles covering cooperative networks , radar clutter modeling , low-complexity MIMO algorithms , and molecular communication . Notable awards: IEEE Fellow (2017), Young Radar Engineer of the Year (2009), and multiple teaching awards. Leadership Roles: Vice-chair of IEEE Radar Systems Panel, Associate Editor for IEEE Trans. on Aerospace and Electronic Systems , and editorial board member for IET Trans. on Radar, Sonar and Navigation .

Christine Schubert Kabbanمشاهده پروفایل

استاد

• Structural Health Monitoring
• Target Detection
• Autonomous Systems
• +۵ مورد دیگر

Christine Schubert Kabban is a Professor of Statistics at the Graduate School of Engineering and Management , Air Force Institute of Technology (AFIT). Her work spans structural health monitoring, target detection, autonomous systems, and natural language processing, with over 150 publications in clinical, engineering, and statistical journals. She has secured funding from agencies like AFOSR, AFRL, and NIH. PhD in Applied Mathematics, AFIT MS in Applied Statistics, Wright State University BS in Mathematics, University of Dayton Her research integrates statistical methods with interdisciplinary applications, including biomedical studies (cytokine analysis, sleep disorders), network science (Barabási-Albert models), and defense systems (satellite reliability, ejection system safety). Recent work focuses on machine learning validation , 6D pose estimation , and ferroelectric material analysis . Notable awards include the 2024 Dean’s Distinguished Teaching Professor , 2022 Air Force Gage H. Crocker Outstanding Professor Award , and multiple Instructor of the Quarter recognitions. She has advised over 100 graduate students and contributed to defense acquisition cost modeling. She serves on the Wright Patterson Air Force Base IACUC and reviews grants for AFOSR, ensuring rigorous statistical evaluation in research ethics and funding.

• Multimedia Signal Processing
• Biomedical Signal Processing
• Telecommunications
• +۴ مورد دیگر

John Soraghan is a Visiting Professor at the Department of Electronic and Electrical Engineering, University of Strathclyde. His research spans interdisciplinary domains in signal processing, biomedical engineering, and synthetic aperture radar (SAR) systems. Active in neuromorphic computing and spiking neural networks Led projects funded by EPSRC and Air Force Office of Scientific Research Research Focus: • Video coding and wavelet transforms for multimedia • Non-linear adaptive signal processing in biomedical and telecom applications • SAR signal analysis and parallel processing algorithms Scientific Contributions: • Pioneering work in radar-based physiological signal detection • Development of machine learning techniques for SAR image processing Best Paper Award (2022) for neural network applications Texas Instruments Chair in Signal Processing (2004) Collaborative Impact: Active in 29 research projects including neuromorphic computing initiatives and DTP programs. His work contributes to UN Sustainable Development Goals through medical technology advancements.

Christian Küchererمشاهده پروفایل

پژوهشگر

• Requirements Engineering
• Decision Support Systems
• Healthcare Information Management
• +۳ مورد دیگر

Dr. rer. nat. Christian Kücherer is a researcher at Heidelberg University's Institute of Computer Science , associated with the Department of Software Engineering . He completed his diploma in Computer Science from Hochschule Mannheim and continues his academic work in requirements engineering and healthcare information systems. Education: Diploma in Computer Science, Hochschule Mannheim Primary Research Areas: Ontologies in Requirements Engineering, Decision Support Systems, Healthcare IT Key Collaborations: Work with Prof. Dr. Barbara Paech and teams at HEC/MIE, INFORMATIK, and WI conferences Kücherer's research spans interdisciplinary applications of software engineering in healthcare, focusing on ontologies for decision support systems, information management in hospitals, and task-oriented requirements engineering. His work addresses challenges in collaborative ontology construction, traceability, and domain-specific method adaptation. His publications cover ontology-based healthcare systems analysis, requirements quality improvement, and empirical studies on hospital IT governance. He has presented at conferences like REFSQ, ICEIS, WI, and MIE, with a particular emphasis on integrating semantic technologies into software practices. Christian Kücherer contributes to projects such as SNIK and has explored practical applications of wavelet filtering, service mediation platforms, and SSL security architectures. His scholarly output reflects a long-standing commitment to bridging theoretical software engineering with real-world healthcare challenges from 2001 to 2028.

Ali Dogruمشاهده پروفایل

استاد

• Software Engineering
• Component-Based Systems
• Complex Event Processing
• +۷ مورد دیگر

Ali Dogru is a Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. He holds a Ph.D. in Computer Science from Southern Methodist University, an M.S. in Electrical Engineering from the University of Texas at Arlington, and a B.S. in Electrical Engineering from the Technical University of Istanbul. His research focuses on software engineering methodologies, particularly component-based systems, compositional software development, complex event processing, and fault management. He explores interdisciplinary solutions through connector technologies to address IoT heterogeneity and variability modeling in microservices architecture. Dr. Dogru's work emphasizes axiomatic design theory applications in software systems, including service-oriented computing, model-driven engineering, and cloud-enabled development. His contributions span domain-specific languages for fault management, adaptive workflows in cloud environments, and synthetic medical data generation for healthcare analytics. He has published extensively on topics like UML connector enhancements, microservice interoperability, and EEG emotion recognition using wavelet transforms. His interdisciplinary research bridges theoretical concepts with practical industry applications, as evidenced by his work on legacy system migration and mashup strategies in service-oriented computing. While no advising or grant details are explicitly mentioned, his academic profile reflects a strong focus on innovation in software development practices. His involvement in conferences such as the Turkish National Software Architecture Conference highlights his role in advancing software engineering standards and methodologies. Dr. Dogru's research lab (if applicable) is not explicitly detailed in the provided texts, but his publications indicate sustained engagement with cutting-edge computational challenges in software systems.

Peter de Withمشاهده پروفایل

استاد

• Video Compression
• Image Analysis
• Healthcare Applications
• +۴ مورد دیگر

Peter de With is a Part-time Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures Group. His expertise spans video compression, image analysis in healthcare, surveillance, and multimedia. He holds a PhD from Delft University of Technology (1992) and has held roles at Philips Research, the University of Mannheim, and Cyclomedia Technology. His research focuses on video/image signal processing, machine learning, and healthcare applications such as esophageal cancer detection. He is an IEEE Fellow and has authored over 400 papers, receiving multiple awards for his work. Research interests include video coding, medical imaging, and system innovations in health, surveillance, and automotive sectors. Key publications address challenges in medical instrument detection, 3D ultrasound, and multiview rendering. He collaborates with industry partners like Bosch Security and hospitals for oncology research. His work bridges academic innovation with real-world applications, emphasizing practical solutions in healthcare technology and multimedia systems. Awards include IEEE Fellow status and paper awards from CE Chester Sall, SPIE, and Elsevier. He chairs program committees for IEEE conferences and holds 30 patents. His contributions to video analysis and system design have driven advancements in safety, security, and healthcare technologies.

Raymond O. Wellsمشاهده پروفایل

استاد

• Complex Analysis
• Wavelet Analysis
• Differential Geometry
• +۳ مورد دیگر

Raymond O. Wells is a Distinguished Professor of Mathematics at the School of Computer Science & Engineering, Constructor University. Previously, he held roles as Professor of Mathematics and Vice President for External Affairs at Jacobs University Bremen, and served as Professor Emeritus at Rice University. His academic journey includes a BA in Mathematics from Rice University (1962) and a PhD from New York University (1965). His research focuses on complex analysis, wavelet theory, differential geometry, and the history of mathematics. Notable contributions include foundational work on complex manifolds, cohomology in theoretical physics, and applications of wavelet analysis in signal processing. He has authored influential books such as *Differential Analysis on Complex Manifolds* and *Wavelet Analysis: The Scalable Structure of Information*. Wells has received prestigious awards including the Guggenheim Fellowship (1974–1975) and Humboldt Senior Scientist Award. Beyond academia, he has served as President of the Boulder Philharmonic Orchestra and the Boulder Ensemble Theatre Company. His career reflects a blend of mathematical rigor and interdisciplinary engagement, spanning academic leadership, research innovation, and cultural contributions.

• Internet of Things
• Edge Computing
• Smart Agriculture
• +۵ مورد دیگر

Professor Yi-Bing Lin is a distinguished faculty member in the Department of Computer Science at National Yang Ming Chiao Tung University, Taiwan, where he leads pioneering research in Internet of Things (IoT) systems and applications. His work primarily focuses on developing the IoTtalk platform and its numerous derivatives across various domains including smart agriculture, smart homes, environmental monitoring, and creative applications. His research interests span Internet of Things, Edge Computing, Smart Agriculture, Sensor Networks, AI Integration, Wireless Networking, and Smart Home Systems. Professor Lin has developed the IoTtalk framework that enables rapid development of IoT applications with numerous specialized implementations including VoiceTalk, SensorTalk, AgriTalk, and many others that address specific domain challenges. His work emphasizes practical implementations with real-world impact, particularly in precision agriculture where his team has developed systems for orchid disease detection, rice blast monitoring, turmeric farming, and watermelon ripeness prediction. Analysis of his recent publications (2023-2025) reveals a strong trend toward integrating AI with IoT systems, particularly for agricultural applications and smart environments. His work increasingly incorporates advanced techniques like continuous wavelet transform, deep learning, and computer vision to solve practical problems in precision farming and environmental monitoring. The publications also show growing interest in creative applications of IoT technology for performing arts, interactive experiences, and educational contexts. Professor Lin has received recognition through consistent high-volume publication output in top-tier venues including IEEE Internet of Things Journal, IEEE Access, and Sensors. His collaborative network is extensive, with frequent co-authorship with researchers like Yun-Wei Lin, Wen-Liang Chen, and Min-Zheng Shieh. His advising has produced numerous researchers who continue to work in IoT and related fields, with many former students maintaining collaborative relationships. Professor Lin's research has been supported by multiple grants enabling the development of practical IoT systems with real-world implementations. His lab has developed numerous specialized IoT applications through the IoTtalk framework, creating a cohesive research ecosystem. Current work shows expansion into new application domains including interactive miniature worlds, simultaneous performance across locations using IoT-based motion capture, and IoT-based musical instruments like piano playing robots and violin robots, demonstrating the versatility of his research approach.

Zoran Cvetkovicمشاهده پروفایل

استاد

• Digital Signal Processing
• Wavelets
• Time-Frequency Analysis
• +۹ مورد دیگر

Professor Zoran Cvetkovic is currently based at the Faculty of Natural, Mathematical & Engineering Sciences at King's College London , where he serves as Professor of Digital Signal Processing and Deputy Head of Department (Research) . He is affiliated with the Centre for Telecommunications Research , Engineering Security Hub , and Music Computing Lab . Education: PhD (University of California, Berkeley, 1995), MPhil (Columbia University, 1998), MSc (University of Belgrade, 1992), BSc (University of Belgrade, 1989) Previous Positions: Principal Member of Technical Staff at AT&T Labs (1997-2022), Research Fellow at Harvard University (2002-2003), Research Associate at EPFL (1996-1997) and UCSF (1994), Research/Teaching Assistant at University of Belgrade (1989-1992) Professor Cvetkovic's research spans signal processing theory , wavelets , time-frequency analysis , and their applications in neurophysiology , audio/spoken language technology , and biomedical systems . His recent work focuses on immersive audio technology , real-time acoustic simulation , superdirective beamforming , and multimodal speech recognition . With over 146 research outputs, his publications demonstrate expertise in machine learning for speech processing , acoustic environments , and information theory . His academic activities include organizing IEEE conferences , editorial roles at IEEE Transactions on Signal Processing , and collaborations with institutions like Stanford University , University of California, Berkeley , and SRI International . He supervises 7 research projects totaling over £3.5 million in funding from EPSRC , Leverhulme Trust , and Royal Academy of Engineering . His work contributes to UN Sustainable Development Goals related to education and technological innovation .

Heiko Schwarzمشاهده پروفایل

استاد

• Video Coding
• Image Compression
• Multimedia Systems
• +۳ مورد دیگر

Prof. Heiko Schwarz is a Professor at Freie Universität Berlin and Head of the Image & Video Coding Group at the Fraunhofer Heinrich Hertz Institute (HHI). He holds a Dipl.-Ing. (1996) and Dr.-Ing. (2000) in Electrical Engineering from the University of Rostock. Research & Expertise: Specializes in video coding standards including HEVC (H.265) and H.264/MPEG-4 AVC. Key contributions include scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimized encoding. His work has been integral to international standards through participation in ITU-T VCEG and ISO/IEC MPEG. Developed core tools for HEVC and its 3D video extensions Co-designed H.264/AVC's motion compensation and entropy coding Co-editor of H.264/AVC and SVC reference software Awards & Recognition: 2014 SMPTE Certificate of Merit 2012 German Future Prize Nomination 2011 Karl Heinz Beckurts Award 2009 IEEE Best Paper Award Academic Engagement: Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology (2016–present). Teaches Data Compression at FU Berlin. Leads a research group focusing on next-generation video coding technologies. Labs/Teams: Directs the Image & Video Coding Group at Fraunhofer HHI, advancing standards like HEVC and exploring wavelet-based video coding techniques.

Nicolas Menicucciمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Computation
• Quantum Optics
• +۳ مورد دیگر

Professor Nicolas Menicucci is a Professor of Theoretical Physics at RMIT University's School of Science, located at the City Campus in Australia. His research focuses on quantum information theory, quantum computation, quantum optics, and relativistic quantum information, with affiliations to the QuRMIT Theory Lab and as a Chief Investigator in the Centre for Quantum Computation and Communication Technology (CQC2T). Education: PhD in Theoretical Physics from Princeton University (2008), BS in Physics from the University of New Mexico (2002). Positions: Held roles at Perimeter Institute for Theoretical Physics (2008–2011), University of Sydney (2011–2015), and RMIT since 2015, where he became a Professor in 2022. His research interests span quantum computing architectures, quantum error correction, and foundational aspects of quantum mechanics. Notable contributions include pioneering work on continuous-variable cluster states for optical quantum computing and exploring relativistic effects in quantum information. He has published over 150 articles and secured prestigious awards like the 2012 ARC DECRA and the 2015 RMIT Vice-Chancellor’s Senior Research Fellowship. Recent projects include developing fault-tolerant quantum computing protocols, studying entanglement harvesting in relativistic settings, and advancing quantum sensing techniques. His work bridges theoretical insights with experimental feasibility, emphasizing scalable quantum technologies. Key Awards: 2015 Vice-Chancellor Senior Research Fellowship 2012 Australian Research Council DECRA Advising and Grants: Supervises research on topics like machine learning-based quantum control and photonic quantum computation. Manages projects on quantum systems identification, error correction, and hybrid quantum processors. He leads the QuRMIT Theory Lab and collaborates internationally on quantum technologies. His work aligns with UN Sustainable Development Goals 4 (Quality Education) and 9 (Industry, Innovation, and Infrastructure).

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.