جست‌وجوی استاد و پژوهشگر

Qingjin Pengمشاهده پروفایل

استاد

• Product Design and Manufacturing
• Digital Manufacturing
• Design Methodology
• +۵ مورد دیگر

Qingjin Peng is a Professor in the Department of Mechanical Engineering at the University of Manitoba’s Price Faculty of Engineering. His work focuses on Product Design and Manufacturing, with expertise in Digital Manufacturing, Design Methodology, and Virtual Reality applications. He holds a PhD from the University of Birmingham (1998) and master’s and bachelor’s degrees from Xi’an Jiaotong University (1988, 1982). Education: PhD in Mechanical and Manufacturing Engineering, University of Birmingham (1998) M.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1988) B.Sc. Mechanical and Manufacturing Engineering, Xi’an Jiaotong University (1982) Research Interests: His research spans Digital Manufacturing, Product Assembly/Disassembly Planning, CAD/CAM systems, and System Modeling. He pioneered methods integrating AI (e.g., Reinforcement Learning, Machine Learning) into product design and manufacturing processes. Notable contributions include optimizing Additive Manufacturing parameters using Taguchi methods and developing VR-based training systems for Coordinate Measuring Machines. Publications: Recent work emphasizes AI-driven innovation in manufacturing, with focus areas including sustainability in Additive Manufacturing, fault diagnosis of rotating machinery, and multi-agent systems for concept evaluation. His 2024 studies highlight advancements in product design through radical problem-solving frameworks, causal chain analysis, and genetic algorithm optimization. Advising & Grants: Currently no graduate student opportunities are listed, but his past collaborations include projects funded by national grants. Research teams often involve interdisciplinary partnerships, leveraging AI and data-driven approaches. Labs/Teams: His lab focuses on Digital Manufacturing and AI applications, though specific team names are not explicitly mentioned in the provided text.

• Artificial Intelligence
• Data Science
• Health Informatics
• +۷ مورد دیگر

Andrew Ware is a Professor of Computing at the University of South Wales, leading the Wales Institute of Digital Information (WIDI) as its Research Director. His work focuses on applying AI and data science to solve real-world problems, such as medical diagnostics, environmental monitoring, and educational systems. He teaches courses on artificial intelligence, data mining, and programming. Andrew has supervised nearly forty PhD students and actively collaborates with industry partners like Tata Steel and Goggleminds. Beyond academia, he directs Techno Camps (a Welsh Government-funded initiative to engage youth in computing) and chairs 360 Ability Sport, a charity promoting disability inclusion in sports. As Editor-in-Chief of the Annals of Emerging Technologies in Computing (AETiC), he oversees cutting-edge research dissemination. Affiliations: University of South Wales, Wales Institute of Digital Information, Techno Camps, 360 Ability Sport Research Interests: AI-driven healthcare systems, image processing for medical diagnostics, IoT optimization, and educational technology. His recent publications span AI applications in brain tumor detection, nanomaterials for cancer therapy, and YOLOv8-based attendance monitoring. Andrew’s interdisciplinary work bridges academia and industry, addressing societal challenges through innovation. He serves on the board of the University of South Wales’s Chaplaincy Support Services and actively participates in international research projects, contributing to global knowledge networks.

Chris Köcherمشاهده پروفایل

پژوهشگر

• Algorithms
• Theory & Logic
• Programming Languages
• +۹ مورد دیگر

Chris Köcher is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on theoretical computer science, with a strong emphasis on formal methods, automata theory, and verification of complex systems. He leads research in areas such as algorithms, programming languages, cyber-physical systems, and security. His research bridges foundational theory and practical applications, addressing challenges in distributed systems, mobile systems, and privacy-preserving technologies. His research interests span formal language theory, verification of concurrent systems, and the analysis of storage mechanisms in automata. Notably, he has contributed to the study of queue automata, multi-pushdown systems, and the decidability of reachability problems in lossy systems. His work often intersects with theoretical computer science and algebraic structures, exploring topics like monoids and trace languages. Chris’s publications reflect a deep engagement with foundational questions in computer science, including the verification of cooperating multi-pushdown systems, the complexity of semilinear set separability, and the formal analysis of hard attention mechanisms in transformers. His research has implications for improving the reliability and security of software systems through rigorous mathematical foundations. He is affiliated with MPI-SWS, a leading research institution in software systems, and contributes to the institute’s mission to advance theoretical and applied research in computer science. His work is supported by collaborations within the institute and the broader academic community.

Umar Razaمشاهده پروفایل

مدرس ارشد

• Internet of Things
• Machine Learning
• Wireless Sensor Networks
• +۹ مورد دیگر

Dr. Umar Raza serves as a Senior Lecturer in Networking, IoT and Smart Systems at Manchester Metropolitan University's Department of Engineering, where he concurrently manages the Cisco Network Academy. His academic career includes prior lecturing roles in Robotics and Computing at Staffordshire University, with research spanning industrial applications of emerging technologies. His educational credentials include a PhD in Wireless Sensor Networks from the University of Bradford (2014), Postgraduate Certificates in Research Methods and Professional Higher Education from Staffordshire University (2005, 2007), an MSc in Electronics and Computer Systems from Huddersfield University (1995), and a BSc in Engineering Electronics from DeMontfort University Leicester. Dr. Raza's research centers on Internet of Things applications integrated with Machine Learning across industrial, agricultural, and healthcare domains. Current projects address IoT/ML solutions for domicile care monitoring, cognitive impairment assistance, natural disaster early warning systems, and smart farming in Pakistan, with emphasis on practical implementations in real-world environments. His recent publications demonstrate a pronounced interdisciplinary trajectory, particularly at the IoT-Machine Learning nexus for healthcare innovation (cardiac diagnostics, autism support wearables) and agricultural technology. Significant contributions also appear in blockchain scalability, vehicular network security, and 5G propagation modeling, reflecting both theoretical rigor and industry-relevant problem solving. Professional recognition includes: Fellow of the Higher Education Academy (FHEA) Dr. Raza actively supervises two MSc and two PhD candidates while leading multiple funded projects: Principal Investigator for KTP projects with Kindus Solutions (completed) and Trumeter Ltd Project Advisor for KTP with RAIT Ltd Lead researcher on NATO Science for Peace Security grant for natural disaster warning systems Principal Investigator for Ignite National Technology Fund smart farming initiative in Pakistan As Cisco Network Academy Manager, he drives industry-academia collaboration in networking education, developing curricula that integrate cutting-edge IoT and cybersecurity concepts while maintaining strong professional engagement through IEEE, IET, and BCS memberships.

Brendon Andersonمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۴ مورد دیگر

Brendon Anderson is an Assistant Professor in the Department of Mechanical Engineering at Cal Poly San Luis Obispo's College of Engineering. His research focuses on developing safety guarantees for machine learning systems, bridging advanced mathematics, computer science, and engineering principles like optimization and control theory. He emphasizes interdisciplinary approaches to address limitations in AI deployment for safety-critical applications such as autonomous vehicles. Academic Rank: Assistant Professor Department: Mechanical Engineering University: Cal Poly San Luis Obispo Anderson's recent publications highlight expertise in neural network robustness certification, adversarial defense mechanisms, and evolutionary game theory applications. Key trends include randomized smoothing techniques, structure-aware computation, and min-max optimization frameworks. While no scientific awards are explicitly mentioned in this dataset, his work demonstrates a strong focus on cutting-edge machine learning safety research and student mentorship. His teaching philosophy emphasizes hands-on problem-solving and fostering 'aha!' moments in students. Outside academia, Anderson connects engineering principles to personal hobbies like skateboarding and vintage vehicle restoration, viewing these activities as practical applications of optimization and dynamics analysis.

Darko Zibarمشاهده پروفایل

استاد

• Machine Learning in Photonic Systems
• Optical Communication
• Quantum Communication
• +۳ مورد دیگر

Darko Zibar is a Professor at the Department of Electrical and Photonics Engineering, Technical University of Denmark (DTU), and leads the Machine Learning in Photonics Systems (MLiPS) group. He holds a M.Sc. in Telecommunication and a Ph.D. in Optical Communications from DTU (2004, 2007). As a Visiting Professor, he has contributed to research at Politecnico di Torino, Friedrich Alexander University of Erlangen, University of California Santa Barbara, and University of Colorado, Boulder. Research Interests: Machine learning applications in optical communication systems Digital signal processing for classical and quantum photonics Optimization of Raman amplifiers and frequency combs Nonlinear fiber optic transmission modeling Photonic reservoir computing with silicon microring resonators Scientific Contributions: His work includes record-breaking achievements in optical phase noise measurement (approaching quantum limits) and programmable gain Raman amplifier design (S+C+L band). He has received prestigious awards such as the ERC Consolidator Grant (2017), Humboldt Bessel Research Award (2021), and Villum Investigator Award (2023).

Anis Koubâaمشاهده پروفایل

استاد

• Real-Time Systems
• Quality of Service (QoS)
• Wireless Sensor Networks
• +۴ مورد دیگر

Anis Koubâa is a Professor at the Department of Electrical Engineering and Computers, Instituto Superior de Engenharia do Porto (ISEP), part of the Polytechnic Institute of Porto (IPP). He is a core member of the CISTER Research Centre, focusing on real-time systems, quality of service, wireless sensor networks, and cyber-physical systems. He holds a PhD in Computer Science from the National Polytechnic Institute (INPL, France), a Master’s from University of Nancy I, and a Telecommunications Engineering degree from Sup’Com Tunisia. His postdoctoral research at CISTER (2005–present) concentrated on real-time aspects of wireless sensor networks. Research interests span cooperative autonomous driving, drone networks, edge computing, and secure communication protocols. He has published extensively on topics like vehicular platooning, real-time QoS monitoring, and cloud-edge architectures for AI applications. His work integrates robotics (ROS frameworks), network protocols (IEEE 802.15.4e), and safety-critical systems. Over 100 papers in journals/conferences and co-authored books on ROS and cyber-physical systems demonstrate his leadership in these areas. Current projects include COPADRIVe (cooperative driving simulation), COPA-Drive (platooning validation), and secure architectures for autonomous systems. He leads the RIOTU-Lab for IoT and robotics research, emphasizing practical applications in disaster management, UAV coordination, and smart city infrastructure.

• Technology Credit Scoring
• Operational Research
• Data Mining
• +۵ مورد دیگر

So Young Sohn is a distinguished Professor at Korea University's College of Business, Department of Management Engineering, with over two decades of impactful research in technology management and operations research. Her scholarly contributions have established her as a leading expert in technology credit scoring, data mining applications, and technology convergence analysis. Dr. Sohn's research interests span technology credit scoring for SMEs, operational research methodologies, data mining techniques, machine learning applications in business contexts, technology convergence patterns, patent analysis, and SME financing mechanisms. Her work bridges theoretical rigor with practical business applications, particularly focusing on Korean case studies that have broader international relevance. She has pioneered innovative approaches using knowledge graphs, multiplex networks, and deep learning techniques to solve complex business problems. Her publication portfolio reveals consistent research trends toward increasingly sophisticated analytical methods, evolving from traditional statistical models to advanced machine learning and network science approaches. Recent work demonstrates particular focus on technology convergence, digital therapeutics, and AI applications in business decision-making. The interdisciplinary nature of her research spans business analytics, engineering, healthcare, and environmental science. Dr. Sohn has received recognition through numerous high-impact publications in premier journals including Expert Systems with Applications, European Journal of Operational Research, Scientometrics, and IEEE Transactions. Her research has been consistently funded through competitive grants focusing on technology management and innovation. As an academic mentor, Dr. Sohn has advised numerous doctoral students who have gone on to productive research careers, with many continuing to collaborate with her on ongoing projects. Her research team has secured substantial funding for projects related to technology credit scoring, technology convergence analysis, and predictive analytics applications. Dr. Sohn leads a dynamic research laboratory focused on technology analytics and decision support systems, collaborating with industry partners and government agencies to translate research findings into practical business solutions. Her current work emphasizes sustainable technology development and AI-driven decision support systems for complex business environments.

• Audio Signal Processing
• Beamforming
• Speech Enhancement
• +۴ مورد دیگر

Kouei Yamaoka is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Tokyo Metropolitan University's College of Engineering, specializing in advanced audio signal processing techniques. With over 24 publications from 2017-2024, Yamaoka has established a strong research presence in the international signal processing community. Yamaoka's research focuses on innovative beamforming techniques, time delay estimation methods, and speech enhancement algorithms. Their work consistently addresses challenging problems in multichannel audio processing, particularly in underdetermined scenarios where the number of sources exceeds available microphones. Key contributions include causal distortionless response beamforming, minimum-spanning-tree-based time delay estimation robust to outliers, and sound field interpolation for rotation-invariant processing. Analysis of Yamaoka's publication trends reveals a consistent focus on practical audio processing solutions with applications in source separation, speech enhancement, and acoustic scene analysis. The research demonstrates strong theoretical foundations combined with practical implementation considerations, particularly for real-time and online processing scenarios. Recent work has expanded into deep learning applications for bioacoustic analysis, as evidenced by the 2023 publication on marmoset vocalization analysis. Yamaoka maintains a productive collaboration network, with frequent co-authorship with Nobutaka Ono (20 joint publications), Shoji Makino (9 joint publications), and other researchers in the Japanese signal processing community. Publications appear consistently in top venues including IEEE/ACM Transactions on Audio Speech and Language Processing, APSIPA, EUSIPCO, and ICASSP.

Yogendra K Joshiمشاهده پروفایل

استاد

• Thermal Management
• Computational Fluid Dynamics
• Electronics Packaging
• +۵ مورد دیگر

Yogendra K Joshi is a Professor at the Georgia Institute of Technology, holding a joint appointment in the School of Mechanical Engineering and the School of Electrical and Computer Engineering. He specializes in thermal management of electronic, telecommunication, and power systems, with expertise in computational modeling of semiconductor processes and microfabricated thermal devices. His work spans thermal modeling of data centers, electric vehicle motors, and high-power electronics packaging. Education: B.Tech. (Mechanical Engineering, IIT Kanpur, 1979); M.S. (SUNY Buffalo, 1981); Ph.D. (University of Pennsylvania, 1984). Previous academic positions include the University of Maryland (1993–2001) and the Naval Postgraduate School (1986–1993). He has also contributed to semiconductor industry R&D. Research focuses on thermal phenomena in electronics cooling, including flow boiling, microfluidics, and embedded evaporative cooling. He has pioneered internet-based distance learning programs in thermal management. Awards: Fellow of ASME (American Society of Mechanical Engineers) 1999 ASME Curriculum Innovation Award Multiple Faculty Performance Awards at Naval Postgraduate School His research integrates thermal-fluid modeling with engineering applications, addressing challenges in data center efficiency, electric vehicle thermal systems, and high-power electronics. Collaborations span academia and industry, emphasizing practical solutions for modern thermal management problems.

Hadj Batatiaمشاهده پروفایل

دانشیار

• Computational Imaging
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Hadj Batatia is an Associate Professor at the School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University Dubai (HWUD) since 2020, serving as Associate Director of Research. He previously held roles at the National Polytechnic Institute of Toulouse (INP Toulouse), Conservatoire National des Arts et Métiers (CNAM Toulouse), and University of Toulouse, where he led research teams and departments. His academic journey includes a PhD in Computer Vision from the University of Toulouse (1992) and an MSc in Artificial Intelligence (1987), with undergraduate studies at the University of Algiers. His research focuses on computational imaging, machine learning, and statistical modeling applied to medical imaging and computer vision. Key areas include deep learning, inverse problems, and biomedical image processing. He has supervised 15 PhD theses and contributed to 82 research outputs, achieving an h-index of 26. Notable grants include the ESMS project (2021-2023) for healthcare smart home systems and CAMM4D (2008-2012) for skin oncology imaging. Dr. Batatia teaches Advanced Machine Learning, Biologically Inspired Computation, and Big Data Management. He has led international collaborations across Europe, Latin America, and Asia, addressing challenges in medical imaging, cybersecurity, and education. His work aligns with UN SDGs, particularly in health and innovation. Awards include qualifications to professorship in Computer Science and Engineering.

Wei Weiمشاهده پروفایل

استادیار

• Behavioural Economics
• Financial Mathematics
• Time-Inconsistent Decision Making
• +۴ مورد دیگر

Dr. Wei Wei is an Assistant Professor in the Department of Actuarial Mathematics and Statistics at Heriot-Watt University, part of the School of Mathematical & Computer Sciences. He holds a PhD from the University of Oxford and previously worked as a postdoctoral researcher at the University of Waterloo. His research focuses on behavioral economics, time-inconsistent decision making, stochastic control, and applications of deep learning in dynamic models. He teaches courses such as F70TS: Time Series and Machine Learning. Education: PhD in Mathematical and Computational Finance (University of Oxford, 2017); MSc in Mathematics (Tongji University, 2012); BSc in Mathematics (Tongji University, 2009). Research interests include collective decision making, risk management, and financial engineering. His work bridges theoretical frameworks (e.g., HJB equations, optimal stopping) with practical applications in reinsurance, investment, and algorithm design. He collaborates internationally and contributes to journals like SIAM Journal on Control and Optimization and the Journal of Economic Theory. Grants and funding details are not explicitly listed, but his publications suggest engagement with topics requiring computational and theoretical resources. He is affiliated with the Maxwell Institute for Mathematical Sciences and serves as a reviewer for multiple top-tier journals.

Xuefei LUمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Uncertainty Quantification
• Big Data Problems
• +۹ مورد دیگر

Xuefei LU is an Associate Professor at SKEMA Business School (France), affiliated with the SKEMA Center for Analytics and Management Science and the Digitalization Research Center. Her academic journey includes a Ph.D. in Statistics from Bocconi University (Italy) and an MSc in Analytics from the University of Manchester (UK). Prior roles include Assistant Professorships at SKEMA Business School and the University of Edinburgh Business School, along with a postdoctoral position at Politecnico di Milano (Italy). Her research focuses on Statistical Machine Learning , Uncertainty Quantification , and Big Data Problems , with applications in epidemiological modeling, decision analysis, and industrial systems. She has developed frameworks for identifying critical components in complex systems and pioneered methods like the Cohort Shapley value for fairness in SME financing. Her work has been recognized with prestigious awards, including the 2024 INFORMS Data Mining Best Paper Award (Runner-Up) and multiple Excellence in Reviewing Awards from the European Journal of Operational Research. She actively supervises doctoral students in areas like anomaly detection and pandemic modeling. Xuefei LU collaborates across disciplines, contributing to labs focused on Reliability Engineering , Molten Salt Reactors , and Transportation Systems . Her research bridges theoretical advancements with practical applications in public health, infrastructure resilience, and sustainable energy systems.

John K. Tsotsosمشاهده پروفایل

استاد پژوهشی

• Computer Vision
• Visual Attention
• Active Visual Search
• +۵ مورد دیگر

John K. Tsotsos is a Distinguished Research Professor at York University's Lassonde School of Engineering, holding positions in both the Department of Electrical Engineering & Computer Science and the Centre for Vision Research (CVR). His work bridges computer science, cognitive science, and neuroscience with a focus on visual attention and active vision systems. Dr. Tsotsos's research interests center on visual attention mechanisms, active visual search, and visuospatial reasoning. His work explores how humans and machines process visual information, with applications in autonomous driving, robotics, and human-computer interaction. He investigates the computational principles underlying visual attention, comparing biological systems with artificial implementations. His research has significant implications for developing more human-like computer vision systems that can effectively navigate and interpret complex visual environments. Analysis of his recent publications reveals a strong focus on active vision systems where observers dynamically control their viewpoints during visual search tasks. His work spans both theoretical foundations of visual attention and practical applications in autonomous vehicles. A significant portion of his recent research addresses driver attention modeling, gaze prediction, and the challenges of real-world visual processing where traditional computer vision approaches often fail. His work consistently bridges cognitive theory with practical engineering applications. Dr. Tsotsos has made substantial contributions to the field of computational vision through his theoretical work on attentional mechanisms and their implementation in artificial systems. His research has influenced both academic understanding of visual processing and practical applications in autonomous systems and human-machine interfaces. As a faculty member at York University, Dr. Tsotsos contributes to the vibrant research ecosystem of the Centre for Vision Research, where interdisciplinary teams work on cutting-edge problems in visual perception, cognitive modeling, and machine vision. His work exemplifies the integration of cognitive science principles with advanced computational techniques to solve complex visual processing challenges.

• Computer Vision
• Machine Learning
• Statistics
• +۳ مورد دیگر

Marcus A. Brubaker is an Associate Professor at York University's Department of Electrical Engineering and Computer Science within the Lassonde School of Engineering. He also holds an Adjunct Professor status at the University of Toronto's Department of Computer Science and serves as a Research Scientist at Google DeepMind. His primary affiliations include co-directorship of the Computational Vision and Imaging Lab (CVIL) at York University and membership in the Vector Institute as a Faculty Affiliate. Brubaker's research spans Computer Vision, Machine Learning, Statistics, and their applications to problems like cryo-electron microscopy (cryo-EM) and 3D molecular reconstruction. Education: He earned a Ph.D. in Computer Science from the University of Toronto (2011), with a thesis on physical models of human motion. His M.Sc. (2006) and B.Sc. (2004) in Computer Science were also from the University of Toronto. Research focuses include cryo-EM structure determination (notably the cryoSPARC software), normalizing flows, and probabilistic modeling. His work bridges computational imaging, statistical inference, and deep learning. Key contributions include advancements in cryo-EM algorithms, generative models, and camera noise analysis. He co-founded Structura Biotechnology and has contributed to open-source tools like Stan's math library. Publications emphasize novel view synthesis, neural radiance fields, sensor noise modeling, and cryo-EM reconstruction techniques. His research trends highlight integration of physics-based models with machine learning for solving complex imaging problems. Awards and recognition include impactful contributions to cryo-EM through cryoSPARC, a Nature Methods paper in 2017, and leadership in academic-industrial collaborations. Brubaker's labs and teams focus on advancing imaging technologies and their applications in structural biology and computer vision.