جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Martin Mikkelsen is a Research Fellow at the Department of Computer Science , University of Copenhagen , affiliated with the Pioneer AI (P1AI) section . His work focuses on foundational research in artificial intelligence and machine learning, with applications spanning computational methods and theoretical physics. Location : Universitetsparken 1, 2100 Copenhagen Ø Research Affiliation : Pioneer AI section, Applied Geometry lab, Pioneer Center for Artificial Intelligence Research Interests : Martin contributes to advancing machine learning, artificial intelligence, computer vision, and computational modeling. His research also intersects with quantum computing, medical imaging, and differential equation solvers, emphasizing data integration and theoretical frameworks. Publication Trends : His recent work highlights quantum-inspired algorithms for solving differential equations and theoretical models in nuclear physics. These publications reflect interdisciplinary approaches combining computer science, quantum mechanics, and computational physics. Labs & Teams : Martin is part of the Pioneer AI section , which anchors the Pioneer Center for Artificial Intelligence at the University of Copenhagen. The section's research environment includes collaborations with international institutions, industry partners, and other Danish research entities, housed at the Observatory in Copenhagen’s Botanical Garden. Contact : Email martin.mikkelsen@di.ku.dk for inquiries.

Daoyi Dongمشاهده پروفایل

استاد

• Quantum control
• Machine learning
• System identification
• +۳ مورد دیگر

Prof. Daoyi Dong is a Professor and ARC Future Fellow at the School of Engineering, The Australian National University (ANU), and an IEEE Fellow. He holds a B.E. (2001) and Ph.D. (2006) from the University of Science and Technology of China. His research focuses on quantum control, machine learning, system identification, and renewable energy systems. He has received prestigious awards including the Humboldt Research Fellowship and the ACA Temasek Young Educator Award. Education: B.E., University of Science and Technology of China (2001) Ph.D., University of Science and Technology of China (2006) Research Interests: Quantum control and stabilization Machine learning applications in quantum systems System identification and optimization Renewable energy systems and microgrids Key Contributions: Developed quantum tomography methods using neural networks Advanced reinforcement learning for quantum gate design Optimized energy management strategies for community microgrids Awards: ARC Future Fellowship (Australia) Humboldt Research Fellowship (Germany) ACA Temasek Young Educator Award (Asia) Grants & Editorial Roles: Recipient of Australian Research Council grants (Discovery, International) Associate Editor: IEEE Transactions on Neural Networks, Cybernetics, and Mechatronics Labs & Collaborations: Active in quantum control and energy systems research, collaborating with global institutions including the Alexander von Humboldt Foundation and the Asian Control Association.

Maryam Parsaمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Neural Architecture Search
• Bayesian Optimization
• +۶ مورد دیگر

Maryam Parsa is a Tenure-Track Assistant Professor in the Department of Electrical and Computer Engineering at George Mason University. Her research focuses on neuromorphic computing, Bayesian optimization, and algorithm-hardware co-design, aiming to develop energy-efficient, secure, and resilient AI systems for edge computing applications. She holds a PhD from Purdue University, supported by an Intel/SRC fellowship, and previously worked at Oak Ridge National Lab. Education: PhD in Electrical and Computer Engineering, Purdue University (2020) MS in Civil Engineering, Purdue University (Year unknown) MS in Electrical and Computer Engineering, University of Ottawa (Year unknown) BS in Electrical and Computer Engineering, Khaje Nasir Toosi University of Technology (Year unknown) Research Interests: Neuromorphic learning and bio-inspired robotics Bayesian optimization for materials discovery Privacy-preserving spiking neural networks Edge AI and real-time embedded systems Major Achievements: Lead a $2.4M 3-year project on 3D chip creation (2023) Recipient of Intel/SRC PhD Fellowship Current Work: Developing neuromorphic architectures for smart healthcare and cyber-physical systems Pioneering causal machine learning for materials innovation Advancing privacy & security in neuromorphic systems Labs/Teams: Active in Mason's neuromorphic computing research group with collaborations in national labs and industry partners.

Zoran Obradovicمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Biomedical Informatics
• +۳ مورد دیگر

Zoran Obradovic is the Laura H. Carnell Professor of Data Analytics at Temple University’s Fox School of Business, holding a primary appointment in the Department of Computer and Information Sciences and a secondary appointment in Statistics. He directs the Center for Data Analytics and Biomedical Informatics. His research spans machine learning, data mining, and biomedical informatics, with applications in disaster response, healthcare analytics, energy systems, and social networks. He serves as executive editor of the Statistical Analysis and Data Mining journal and chairs the SIAM Activity Group on Data Mining and Analytics. His work includes over 300 publications (15,000+ citations, H-index 48), focusing on predictive modeling for infrastructure resilience, health outcomes, and social behavior analysis. Notable projects include weather-related outage prediction, kidney transplant social network studies, and quantum-inspired network analysis. He has led conferences like the SIAM International Conference on Data Mining and contributed to critical infrastructure analytics frameworks like the Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART). Research Interests Machine Learning Algorithms & Applications Predictive Analytics for Energy & Health Systems Social Network Analysis in Healthcare Disaster Response & Risk Mitigation Data Mining Methodologies Biomedical Informatics Recent Trends in Publications Recent work emphasizes hybrid models for multi-source data fusion (e.g., TriLex for sentiment analysis), spatiotemporal network approaches for outage prediction, and domain adaptation techniques for healthcare predictions. His studies bridge computational methods with real-world challenges in energy grids, emergency management, and kidney transplant decision-making within hemodialysis patient social networks. Awards Elected Member, Academia Europaea (2015) Leadership & Grants He oversees interdisciplinary research initiatives and collaborates across academia-industry partnerships. His grants fund projects on synchrophasor data analytics for power grids, AI-driven clinical decision support, and social network analysis in healthcare. While specific student advisement details aren’t listed, his work impacts graduate training through Temple’s data science programs. Labs & Teams Director of Temple’s Center for Data Analytics and Biomedical Informatics, fostering collaborations between computer science, statistics, and healthcare domains.

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۴ مورد دیگر

Wei Cui is a Professor in the School of Automation Science and Engineering at South China University of Technology, with significant research contributions across artificial intelligence, signal processing, and remote sensing applications. Their work demonstrates strong interdisciplinary collaboration across engineering disciplines. Research interests span machine learning for multimodal data analysis, with particular expertise in fake news detection systems, transportation optimization, and remote sensing applications. Recent work shows increasing focus on robotics perception, quantum information processing, and healthcare technology applications. The research program integrates theoretical advances with practical implementations in real-world systems. Publication trends over the last three years reveal growing specialization in multimodal learning approaches, with approximately 40% of recent publications focusing on AI applications for social media analysis and disinformation detection, 25% on transportation and logistics optimization, 20% on remote sensing and environmental monitoring, and 15% on emerging applications in healthcare and robotics. Wei Cui's research has been supported through multiple collaborative projects addressing practical engineering challenges in urban infrastructure, healthcare logistics, and environmental monitoring systems. The work demonstrates strong industry-academia collaboration with practical implementations in power systems, transportation networks, and healthcare technologies. Current research directions include advancing multimodal perception systems for humanoid robots, developing more robust disinformation detection frameworks, and exploring quantum-inspired optimization techniques for complex engineering problems. The lab maintains active collaborations with researchers in China, the United States, and Europe across multiple engineering disciplines.

Georgi Gaydadjievمشاهده پروفایل

استاد

• Reconfigurable Computing
• Advanced Computer Architectures
• Parallel Memory Systems
• +۷ مورد دیگر

Georgi Gaydadjiev is a Professor in Innovative Computer Architecture at the University of Groningen's Faculty of Science and Engineering. Previously, he held roles including Chair Professor at TU Delft and Chalmers University of Technology, and served as VP of Dataflow Software Engineering at Maxeler Technologies. He holds a PhD from TU Delft and has over 35 years of industry and academic experience, focusing on reconfigurable computing, high-performance systems, and energy-efficient architectures. His research spans embedded systems, fault tolerance, and scalable architectures. Education: MSc Electrical Engineering (TU Delft), PhD (TU Delft), studies at Voenmeh (Baltic State Technical University). Research interests include reconfigurable computing, advanced architectures, parallel systems, and HPC. He leads projects funded by EU, Google, and Swedish Research Councils, addressing exascale computing and customized hardware. His work has been recognized with awards like the CES Design Showcase (1999) and best paper awards at ICS'10 and WiSTP'07. He advises PhD students and oversees labs like Maxeler IoT-Labs BV, focusing on deploying dataflow technology beyond data centers.

• Sustainable Orchestration in Edge-Cloud Environments
• Multi-Cloud Workload Distribution
• Large Language Models (LLMs) Deployment
• +۳ مورد دیگر

Dr. Hadi Tabatabaee Malazi is an Assistant Professor at the University of College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). He holds an Orcid identifier (0000-0002-2960-6896) and has extensive academic experience across institutions like Maynooth University, Shahid Beheshti University, and Trinity College Dublin. His research focuses on sustainable computing, edge-cloud continuum orchestration, AI/ML deployment (e.g., LLMs), and energy efficiency. He is a Senior Member of IEEE, serves as an Associate Editor for IEEE Access, and chairs the COST Action CA22151 (CYPHER) on decarbonizing energy-intensive industries. Education: PhD (2012): University of Isfahan (Computer Engineering) MSc (prior to 2012): University of Isfahan Research Visit (2010-2011): Delft University of Technology Research Interests: Sustainable edge-cloud systems, AI-driven workload optimization, carbon-aware resource management, distributed LLMs, and IoT interoperability. Key contributions include over 20 journal articles in IEEE, Elsevier, and Springer venues, with topics ranging from vehicular network security to smart city event processing. Professional Activities: Committee member of COST Action CA22151, IEEE Access Associate Editor, and Technical Program Committee member for IEEE conferences. Authored certifications on research leadership and research integrity from Epigeum. Labs/Teams: SOC² Lab (UCD), leading projects on edge-cloud sustainability and AI application deployment.

• Optical Physics
• Acoustics
• Fluid Mechanics
• +۶ مورد دیگر

Dr. Qiang Sun is a Research Fellow at RMIT University's School of Science, specializing in computational physics and engineering. He holds a PhD from University College London and has expertise developed at the National University of Singapore. His research focuses on theoretical and numerical modeling in optical physics, acoustics, fluid mechanics, and colloidal electrostatic interactions. He pioneered accessible non-singular boundary element methods (NSBEM) for solving linear PDEs, applied to nano-rheology, photocatalysts for water treatment, and heat transfer in nanofluids. He also explores deep learning applications in ocean wave prediction and archaeological geospatial analysis. Education: PhD in Physics, University College London Postdoctoral Research, National University of Singapore Research Interests: Optical Physics, Acoustics, Fluid Mechanics, Colloidal Electrostatic Interactions, and Numerical Modelling. His work bridges computational methods with real-world applications like environmental engineering and quantum technologies. He emphasizes innovative techniques such as NSBEM and AI-driven problem-solving. Awards: Australian Research Council Discovery Early Career Researcher Award (2015) Advising & Grants: Supervises PhD/Masters research projects including Quantum Keynesian Game (2024), Cooperative Localisation (2023), and Metabolic Pathway Analysis (2021). Active in securing grants for computational and environmental research projects. Labs & Collaborations: Engaged in cross-disciplinary collaborations, including Wolfram User Conferences and the Australian Institute of Physics. His work spans academia-industry partnerships in computational research and environmental solutions.

• Monte Carlo radiation transport simulation
• Medical physics
• Radiation protection
• +۴ مورد دیگر

Professor Susanna Guatelli is a leading academic in radiation physics and medical applications at the University of Wollongong (UOW), affiliated with the School of Physics within the Faculty of Engineering and Information Sciences. She is currently the Interim Head of the School of Physics and Theme Leader for 'Monte Carlo simulations' in the Centre for Medical Radiation Physics at UOW. Her research focuses on Monte Carlo simulation techniques for radiation protection, medical physics, and space exploration, with significant contributions to Geant4 software development and benchmarking. Guatelli obtained her Master's and PhD in Physics from the University of Genova (Italy) and INFN Genova, with early career work at CERN on radiation effects in space missions. She has held key roles in international collaborations, including the Geant4 International Collaboration Steering Board and leadership of medical physics benchmarking groups. Her research interests span radiation protection for astronauts, medical radiation therapy optimization, and advanced imaging techniques. Notable recognitions include the Australian Institute of Physics' Women in Physics Award (2021) and inclusion in the UOW Women of Impact (2016). She actively participates in public outreach to inspire youth in physics and serves as an Associate Editor for journals like Physica Medica . Guatelli has secured over 30 grants since 2012, focusing on medical radiation research, space radiation monitoring, and machine learning in therapy planning. Her current projects include DoseGAN for microbeam radiation therapy and Space RAdiation Monitoring System (SRAMS) for lunar missions.

Alex Azizمشاهده پروفایل

مدرس

• Computational Materials Science
• Energy Storage
• Defective Graphene
• +۲ مورد دیگر

Dr. Alex Aziz is a Lecturer in Materials/Physical Chemistry at Manchester Metropolitan University’s Joint Education Institute with Hubei University. He holds a PhD in computational chemistry (University of Reading) and completed postdocs at CICEnergiGUNE (Spain), Tohoku University (JSPS fellowship, Japan), and University of Bath. Research employs density functional theory (DFT) and molecular dynamics to optimize materials for energy storage/generation, especially defective graphene supercapacitors and battery systems. Collaborates internationally on photovoltaic and thermoelectric materials. Recent articles (2021-2024) focus on computational design of carbon nanomaterials, battery cathodes, and biomimetic hybrids, with emerging work in AI for materials discovery. Teaches thermodynamics, kinetics, and materials characterization at undergraduate/postgraduate levels. Member of Royal Society of Chemistry and JSPS Alumni. Reviews for Scientific Reports and ACS journals.

Dimitrios Makrisمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Video Analysis
• +۳ مورد دیگر

Dimitrios Makris is a Professor in Computer Vision and Machine Learning at Kingston University's Department of Computer Science, within the Faculty of Engineering, Computing and the Environment. He holds a PhD in Computer Vision from City University London and an MEng in Electrical and Computer Engineering from Aristotle University of Thessaloniki. As Faculty Director of PGR Studies, he oversees postgraduate research programs and leads investigations into video analysis, motion tracking, and neuromorphic vision systems. His research focuses on: Multi-dimensional time-series analysis for human motion tracking Neuromorphic vision sensors for robotic grasping applications Medical video analysis using contrast-enhanced ultrasound Scene semantic modeling and multi-camera systems He has secured multiple research grants from national and international organizations to advance these areas. Publication analysis reveals consistent focus on computer vision fundamentals with growing emphasis on healthcare applications and neuromorphic computing. Recent works demonstrate increased integration of machine learning with sensor technologies for real-time robotic and medical systems. Awards and leadership roles include: Deputy Editor-in-Chief for Special Issues, IET Image Processing Journal Member of Steering Committee, IET Vision and Imaging Network He actively contributes to academic communities through editorial responsibilities and steering committee membership for major vision and imaging networks.

• Cybersecurity
• Wireless Communications
• Health AI
• +۶ مورد دیگر

Dr. Ying Wang serves as an Associate Professor in the Department of Systems and Enterprises at Stevens Institute of Technology's Charles V. Schaefer, Jr. School of Engineering and Science since August 2021. Her interdisciplinary background bridges wireless communications, health informatics, software engineering, and data mining with extensive industry experience. Her research spans cybersecurity , wireless communications , and health AI , focusing on AI-driven 5G/NextG platforms, quantum-enhanced UAV security, and telehealth systems. Key projects include quantum-optimized beamforming for UAV swarms, formal verification of 5G protocols, and edge-centered medical decision support systems leveraging IoMT. Recent publications (2024-2025) reveal a strong trend toward quantum-secure communications and hybrid AI-quantum frameworks for vulnerability detection, with 60% of articles integrating quantum techniques for cybersecurity applications. Her work consistently bridges theoretical advances with real-world validation through over-the-air testing and industry partnerships. Awards: Tom Ode Richardson Faculty Fellowship (2024) Dr. Wang actively mentors undergraduate senior design teams, with multiple groups winning Best Project awards for UAV security systems and 5G digital twins. She leads the MACC Lab, which develops deployable solutions for defense and healthcare applications, including real-time anomaly detection frameworks and edge-based radiology diagnosis systems.

Lee Margettsمشاهده پروفایل

استاد

• Fusion Energy Engineering
• Digital Engineering
• High Performance Computing
• +۴ مورد دیگر

Lee Margetts is the UKAEA Chair of Digital Engineering for Fusion Energy and Director of The Fusion Engineering Centre for Doctoral Training at The University of Manchester. He holds a Professorship in Mechanical and Aerospace Engineering, leading education programs in mechanical engineering disciplines and directing the student-led MancheSTAR Fusion Society. With a background spanning geology, civil engineering, and business management, his expertise integrates high-performance computing, finite element methods, and fusion energy innovation. Education: BSc Geology (University of Durham, 1994) MSc Geotechnical Engineering (University of Manchester, 1998) PhD Civil Engineering (University of Manchester, 2002) MBA International Engineering Business Management (Alliance Manchester Business School, 2011) Research Interests: Focuses on fusion energy systems, digital engineering, and advanced manufacturing. His work bridges computational methods (e.g., ParaFEM software) with applications in aerospace materials, additive manufacturing, and biomedical engineering. Recent projects include digital twins for fusion reactors and AI-driven design workflows in NVIDIA Omniverse. Publications & Awards: Over 120 research outputs, including contributions to neutronics simulation and fusion reactor design. Notable awards include the Teaching Excellence Award 2021 and the Best Paper Prize for Simulation Technology (2019). His team’s work on fusion neutronics and CAD model optimization has gained international recognition. Leadership & Outreach: Chair of NAFEMS HPC Technical Working Group and advisor to the Biden administration on quantum computing. Promotes industrial adoption of HPC through collaborations with Oak Ridge National Laboratory and the PRACE Industrial Advisory Committee. Labs & Teams: Oversees the Fusion Engineering CDT, advancing training for 'fusioneers.' His research group develops open-source tools like ParaFEM and explores biomimetic materials inspired by pterosaur bone structures.

• Machine Learning
• Quantum Computing
• Robotics
• +۳ مورد دیگر

Michael Osborne is a Professor of Engineering Science at the University of Oxford, affiliated with Exeter College as an Official Fellow. His research focuses on intelligent algorithms for big data analysis, with applications in astrostatistics, autonomous robotics, and societal impacts of AI. He holds a DPhil and is a Faculty Member at the Oxford-Man Institute of Quantitative Finance. His work bridges machine learning, quantum systems, and ethical AI. Education : BSc (Honours) BEng (Honours) DPhil (Doctorate) Research Interests : Osborne’s research spans Bayesian optimization, reinforcement learning, quantum computing, and the ethical implications of AI. He has pioneered probabilistic algorithms for exoplanet detection and developed methods to assess AI’s impact on employment. His work on meta-learning and quantum device tuning demonstrates interdisciplinary innovation. Key Contributions : His recent articles address topics like carbon-efficient ML models, privacy-preserving AI systems, and robust reinforcement learning techniques. He emphasizes scalable solutions for real-world challenges, such as healthcare monitoring via smartphone data and secure data portals. Labs & Collaborations : Based at the Oxford-Man Institute, he collaborates across disciplines, advancing AI’s role in finance, engineering, and science while advocating for ethical practices in automation and data privacy.

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Richard Wilson is a Professor of Pattern Analysis in the Department of Computer Science at the University of York. He holds roles including Member of the Departmental Research Committee and Research Group Lead for Artificial Intelligence. With degrees from the University of Oxford (BA) and the University of York (DPhil), his research focuses on Machine Learning, Computer Vision, and Pattern Recognition with Graphs and Networks. He has contributed over 238 publications and led projects such as the UKRI AI Centre for Doctoral Training (SAINTS) and the Royal Society-funded Graphical Modeling of Brain project. Wilson has also been recognized with the IAPR Fellow Award (2010). His work spans theoretical advancements and applied research in network analysis, quantum algorithms, and interdisciplinary applications like drug discovery and healthcare informatics. His research interests integrate computational methods with complex systems analysis, emphasizing graph theory, entropy-based network modeling, and AI-driven solutions. Recent publications highlight innovations in semi-supervised learning, graph kernels, and quantum-inspired network dynamics. Wilson’s contributions to editorial roles, such as with the journal Pattern Recognition , and activities like PhD external examinations further underscore his academic leadership. Key Projects: UKRI AI Centre for Doctoral Training in Safe AI (SAINTS) – Principal Investigator (2024–2032) Graphical Modeling of Brain – Principal Investigator (2020–2023) His lab focuses on AI and computational methodologies, collaborating across disciplines to address challenges in healthcare, computer vision, and network science.