جست‌وجوی استاد و پژوهشگر

Andrew Robert Gibsonمشاهده پروفایل

مدرس

• Plasma Physics
• Plasma Chemistry
• Materials Science
• +۳ مورد دیگر

Dr Andrew Robert Gibson is a Lecturer in Plasma Physics at the University of York. He holds affiliations with the Centre for Future Health Research and has previously served as a Postdoctoral Research Associate in Physics. His research focuses on plasma physics, plasma chemistry, and their applications in materials science and health-related technologies. He is actively involved in supervising PhD students and has presented at international conferences on topics such as plasma diagnostics and low-pressure plasma modeling. Education: PhD in Physics (exact institution not specified in text) and prior postdoctoral experience at York and Ruhr University Bochum. Research interests include plasma electrolytic oxidation, reactive oxygen species dynamics, and plasma treatment of materials/water. He has contributed to 50+ research outputs, including preprints on plasma diagnostics and datasets on plasma kinetics. His awards include the Gaseous Electronics Conference Student Award (2014), C2D2 Award (2013), and a poster prize (2016). He collaborates internationally, with recent activities in Germany and the UK. His datasets explore plasma emission modeling, chemical kinetics, and vacuum UV photon formation.

Eric Millerمشاهده پروفایل

استاد

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +۴ مورد دیگر

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.

• Material Science
• Nanomaterials
• Perovskite Solar Cells
• +۱۲ مورد دیگر

Jan-Henrik Smått is a Senior University Lecturer at the Laboratory of Molecular Science and Engineering within the Faculty of Science and Engineering at Åbo Akademi University. His research focuses on sustainable materials, nanotechnology, and renewable energy applications, particularly in perovskite solar cells, lignin-based carbon materials, and functional nanomaterials. His research interests span Material Science , Nanomaterials , Sustainable Materials , Perovskite Solar Cells , Lignin Utilization , and Functional Coatings . He explores innovative synthesis routes for advanced materials, emphasizing environmental sustainability and scalability. His work integrates chemistry, physics, and engineering principles to develop next-generation materials for energy and environmental applications. The recent publication trend highlights a strong focus on perovskite thin films , lignin fractionation , porous carbon synthesis , antimicrobial nanomaterials , and radiative cooling technologies . These works reflect interdisciplinary efforts in sustainable material development, combining green chemistry with advanced functional properties for real-world applications. While no formal scientific awards are listed in the provided text, his contributions are recognized through active collaborations and publications in high-impact journals. Dr. Smått is involved in research supervision and academic collaboration, having hosted visiting researchers and participated in joint projects. He was a Co-Principal Investigator in the ASPIRE project focused on stable perovskite solar cells. His research is supported by external funding, indicating active grant acquisition. He also engages in international academic exchange, including visits to institutions like Paderborn University. His work is embedded within the Laboratory of Molecular Science and Engineering, where he contributes to a collaborative team advancing sustainable materials. The research group emphasizes innovation in nanomaterial design, synthesis, and characterization, with strong ties to circular economy and UN Sustainable Development Goals.

André Lamúriasمشاهده پروفایل

استادیار

• Biomedical Information Extraction
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

André Lamúrias serves as an Assistant Professor in the Department of Computer Science at NOVA School of Science and Technology, Universidade Nova de Lisboa, Portugal. His academic journey includes a postdoctoral position at Aalborg University's Department of Computer Science (DKWE group) and prior work at Priberam Labs. He completed his PhD at LASIGE, Faculdade de Ciências, Universidade de Lisboa. His research specializes in biomedical information extraction from scientific papers, electronic health records, and genomics data using machine learning algorithms integrated with domain ontologies. Current projects include metagenomic binning with graph neural networks and low-resource biomedical document processing. His work bridges computational methods with biological applications through extensive GitHub development. Lamúrias teaches Bioinformatics, Algorithms and Data Structures for Biology, Artificial Intelligence Techniques for Biology, Databases, and Artificial Intelligence courses at both MSc and BSc levels. His publication record shows consistent output in top bioinformatics journals including BMC Bioinformatics, Frontiers, and Journal of Cheminformatics, with research evolving from chemical entity recognition to advanced metagenomic analysis. He actively supervises MSc students on projects spanning information extraction in low-resource scenarios, cell type classification, genome recovery, and document retrieval systems. His GitHub profile demonstrates ongoing technical contributions with 28 commits in the last year across multiple repositories.

• Information Retrieval
• Search Engine Technologies
• Data Compression
• +۴ مورد دیگر

Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.

• Large-scale Scientific Computing
• Computational Linear Algebra
• Parallel and Distributed Processing
• +۴ مورد دیگر

Professor Efstratios Gallopoulos is a faculty member at the Department of Computer Engineering & Informatics , University of Patras, where he holds the Division of Computer Software . He currently serves as Deputy Department Chair and Director of the High Performance Information Systems Laboratory (HPCLab) . His academic career spans multiple institutions including the University of Illinois at Urbana-Champaign, University of California Santa Barbara, and collaborations with INRIA Rennes and NASA Goddard Space Flight Center. Education : B.Sc. in Mathematics (First Class Honours) from Imperial College London (1979) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (1985) Research Focus : His work centers on Large-scale Scientific Computing with emphasis on Computational Linear Algebra , Parallel/Distributed Processing , and Data Mining . Recent publications highlight innovations in Randomized Numerical Linear Algebra , Heterogeneous Cluster Scheduling , and GPU-Accelerated Inversion Techniques . Article Trends : His research bridges High-Performance Computing with Data Science , focusing on scalable algorithms for Matrix Computations , Recommender Systems , and Biomarker Analysis . The work spans theoretical advancements (e.g., Givens Rotations ) and practical implementations (e.g., pylspack library). Scientific Awards : NASA Group Achievement Award for Massively Parallel Processor (MPP) development ACM SIGWEB Hypertext Ted Nelson Newcomer Award (2012) Advising and Grants : He has advised numerous research projects funded by European Research Council , Hellenic Foundation for Research and Innovation (HFRI) , and international bodies like the US National Science Foundation. Notably, he co-organized the 2015 Gene Golub SIAM Summer School and served as Chair of the SIAM Gene Golub Summer School Committee (2020-24). Labs and Teams : He leads the High Performance Information Systems Laboratory (HPCLab) and co-directs the interdisciplinary graduate program Data Driven Computing and Decision Making . His teams have contributed to the Cedar vector multiprocessor project at UIUC and Text-to-Matrix Generator (TMG) tools for data mining.

Preben Kidmoseمشاهده پروفایل

استاد

• Biomedical Engineering
• Electrophysiology
• Electroencephalography (EEG)
• +۶ مورد دیگر

Preben Kidmose is a Professor at Aarhus University, affiliated with the Department of Electrical and Computer Engineering and Biomedical Engineering under AU Engineering. He is a pioneer in ear-EEG and leads the Center for Ear-EEG and the Bioelectrical Instrumentation and Signal Processing research group, which operates within the Neurotechnology Lab. His research focuses on biomedical instrumentation, electrophysiology, and biomedical signal processing, particularly leveraging EEG data. He develops machine learning algorithms for physiological signals and designs wearable biomedical devices and medical systems. His work spans applications in sleep monitoring, auditory attention, hypoglycemia detection, and neurodegenerative disorders. While no explicit scientific awards or student mentees are listed in the provided text, his projects include significant initiatives like the Ear-EEG Sleep Monitor (2017–2021), Sleep after Stroke (2020–2024), and ongoing studies on cognitive impairment and neurodegenerative diseases. His lab explores system engineering solutions for medical devices and applies EEG/signal processing to clinical challenges such as Alzheimer’s and epilepsy research.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.

Tomislav Stankovskiمشاهده پروفایل

پژوهشگر

• Neuroscience
• Biomedical Engineering
• Dynamical Systems
• +۴ مورد دیگر

Dr. Tomislav Stankovski is a Visiting Researcher in the Department of Physics at Lancaster University. His research spans interdisciplinary domains, focusing on dynamical systems, neuroscience, and biomedical engineering. Key areas include neural cross-frequency coupling, cardiorespiratory interactions, and synchronization mechanisms in complex networks. Recent publications highlight his work on cortico-muscular connectivity in Parkinson’s disease, time-varying interactions in coupled systems, and applications of Bayesian inference in physiological dynamics. His research integrates physics and computational methods to analyze biological and medical data, particularly in neurophysiology and cardiovascular systems. Neuroscience Biomedical Engineering Dynamical Systems Signal Processing Physics Computational Biology His scientific contributions emphasize understanding synchronization, cross-frequency coupling, and emergent behaviors across physical, biological, and medical contexts. No specific awards or student advisement details are provided in the available texts.

SEMA KAYHANمشاهده پروفایل

استاد

• Image Processing
• Compressive Sensing
• Signal Processing
• +۱۱ مورد دیگر

Professor Sema Kayhan is a faculty member at Gaziantep University , Faculty of Engineering , Department of Electrical and Electronic Engineering . She has held the academic rank of Professor since 2021, after progressing from Associate Professor (2016-2021) and Assistant Professor (2007-2015). Her career began at Gaziantep University as a Research Assistant (1996-2005) and includes administrative roles as Vice Head of Department (2013-2015). Doctorate: 1999-2005, Gaziantep University Institute of Science, Electrical and Electronic Engineering Degree: 1996-1999, Gaziantep University Faculty of Engineering, Electrical and Electronic Engineering & Computer Science Licence: 1991-1996, Gaziantep University Faculty of Engineering, Electrical and Electronics Engineering (English) Her research spans Image Processing , Compressive Sensing , Machine Learning , and Biomedical Signal Processing , with notable contributions to ECG signal reconstruction , chaotic encryption , and FPGA-based hardware acceleration . She has supervised 23 theses, including PhD works on hybrid deep learning for schizophrenia detection , sparse extreme learning machines , and smart home solar energy systems . Her 15 most recent publications (2015-2022) demonstrate expertise in image watermarking , encryption , antenna design , and ECG signal processing , often leveraging chaos theory , extreme learning machines , and FPGA implementations . She has also contributed to NLP for Arabic-alphabet languages and retinal blood vessel segmentation via transfer learning. Professor Kayhan's projects include renewing jacquard control systems (2007-2008) and ECG signal reconstruction via compressive sensing on FPGA (2016-2017). She co-designed a smart home model using solar energy (2017) and has been a Member of the Chamber of Electrical Engineers since 1997.

• Computer Science
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Nazmul Siddique is a Senior Lecturer at Ulster University's School of Computing, Engineering and Intelligent Systems. His research focuses on computer science, artificial intelligence, deep learning, and robotics. Research Interests: Neural Networks, Reinforcement Learning, Multimodal Systems, Biomedical Applications, Industrial Automation. His recent work includes object detection with YOLOv5, emotion recognition via cross-modal attention, and applications of deep learning in healthcare. He collaborates with researchers on topics like visuo-tactile recognition and Bangla sign language processing. Key Projects: Stochastic Regression Model for Robot Localization, IMCLEVER Project on Cumulative Learning in Robots. He has supervised PhD researcher John Doherty and contributed to advancements in industrial automation, autism detection, and diabetic eye disease diagnostics.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۹ مورد دیگر

Ioannis (Ion) Androutsopoulos is a Professor at the Department of Informatics, Athens University of Economics and Business (AUEB), where he has worked since 2002. His expertise spans Artificial Intelligence, with a focus on Natural Language Processing (NLP) and Machine Learning. Education: PhD and MSc in Artificial Intelligence from the University of Edinburgh (1991-1996); Diploma in Electrical Engineering from the National Technical University of Athens (1986-1991). Prior Institutions: Researcher at the National Center for Natural Sciences Research 'Demokritos' (1998-2002) and Macquarie University, Sydney (1996-1997). Research Interests: Androutsopoulos's work includes deep learning, large language models, question-answering systems, emotion recognition from texts and speech, misinformation detection, and text generation from medical images. He also explores data protection for biomedical, legal, and financial domains, as well as EFG tools for the Greek language.

Luca Caucciمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Optical Sciences
• Biomedical Imaging
• +۶ مورد دیگر

Luca Caucci is an Assistant Research Professor in Medical Imaging at the University of Arizona, affiliated with the Arizona Health Sciences Center (AHSC). His research integrates optical sciences and computational imaging to advance medical imaging technologies, particularly in nuclear medicine and 3D imaging . He has pioneered methods for photon-processing detectors and stochastic modeling in oncology and virology. Research Trends: His publications focus on positron emission tomography (PET) , machine learning for image reconstruction , and radiance sensor development . Key themes include GPU acceleration , list-mode data processing , and statistical decision theory in biomedical imaging. His work addresses challenges in 3D imaging , noise reduction , and task performance optimization . Collaborative Efforts: While the text does not explicitly mention lab teams or grants, his contributions to FastSPECT III calibration and alpha/beta emission tomography suggest active involvement in interdisciplinary projects bridging optical engineering and medical diagnostics .

• Electronic systems
• Biomedical engineering
• Electronic instrumentation
• +۳ مورد دیگر

Eduardo Alonso Rivas is an Assistant Professor (Doctor Level 3) in the Department of Electronics, Automation and Communications at the Higher Technical School of Engineering (ICAI) , Comillas Pontifical University , Madrid. With 13 years of teaching experience and recent appointment as Professor in 2023, he specializes in electronic systems, biomedical engineering, and instrumentation. Education PhD in Industrial Engineering, Comillas Pontifical University, 2023 MSc in Engineering Systems Modeling, Comillas Pontifical University, 2015 Industrial Engineering Degree, Comillas Pontifical University, 2009 Degree in Automation & Industrial Electronics, Comillas Pontifical University, 2009 Research Interests Dr. Alonso Rivas focuses on the convergence of electronics and biomedical applications. His work spans intraoperative neurophysiological monitoring systems , fast-settling biosignal amplifiers , skin–electrode interface characterization , and portable impedance-based health monitors . He also investigates power-line communication strategies for smart-grid metering, illustrating a dual expertise in clinical electronics and industrial communications. Publication Focus Across seven peer-reviewed articles (2014-2022), his research follows two dominant threads: (1) biomedical instrumentation —designing wireless, low-noise devices for real-time neural monitoring and diagnostics; (2) power-line communications —optimizing latency and polling strategies in PRIME-based smart-meter networks. This duality underscores a commitment to both cutting-edge healthcare technology and sustainable energy systems. Scientific Awards No specific awards or fellowships are listed in the provided text. Teaching & Supervision Since 2010 he has taught Electronics , Digital Electronics , Microprocessors , Electronic Systems , and Biomedical Instrumentation , as well as supervising Master theses. He progressed from Assistant Collaborator to full Professor in 2023, demonstrating sustained pedagogical commitment. Laboratory & Team Affiliation While explicit laboratory names are not provided, his affiliation with the Department of Electronics, Automation and Communications and prior research at the Technological Research Institute suggests active participation in instrumentation and communications laboratories within ICAI.

Momen Abayazidمشاهده پروفایل

استادیار

• Robotic Image-Guided Needle Insertion
• Motion Compensation Using Machine Learning
• Soft Robotics
• +۳ مورد دیگر

Momen Abayazid is an Assistant Professor in the Robotics and Mechatronics group (RaM) at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente. He holds a PhD in Surgical Robotics from the University of Twente and an M.Sc. in Biomedical Engineering from Delft University of Technology. His research focuses on robotic image-guided interventions, soft robotics, and machine learning applications in medical systems. Education: M.Sc. in Biomedical Engineering (Delft University of Technology), PhD in Surgical Robotics (University of Twente) His expertise includes haptic feedback, needle insertion control, and organ motion compensation. Recent projects involve AI-driven medical imaging, soft robotics for rehabilitation, and sensorized medical devices. Collaborations span institutions like Harvard Medical School and Brigham and Women’s Hospital. ResearchGate and Google Scholar profiles highlight his contributions to medical robotics. Scientific Awards: No explicit awards mentioned in the provided text.