جست‌وجوی استاد و پژوهشگر

Mian M. Hamayunمشاهده پروفایل

دانشیار

• Computer Science
• Transaction Level Modeling (TLM)
• MPSoC Simulation
• +۱۵ مورد دیگر

Mian M. Hamayun is an Associate Professor of Computer Science and Head of Subject Group at the University of Birmingham Dubai campus, part of the College of Engineering and Physical Sciences. He holds a PhD in System Modelling, Simulation & Virtualization from the University of Grenoble Alpes, France, and has academic qualifications from Joseph Fourier University and COMSATS Institute of Information Technology, Pakistan. PhD in System Modelling, Simulation & Virtualization, University of Grenoble Alpes, 2013 MS in Informatics (Parallel & Distributed Systems), Joseph Fourier University, 2009 MSE in Software Engineering (Gold Medalist), COMSATS, 2005 PGCHE, University of Birmingham, 2021 FHEA and SFHEA, University of Birmingham (2021, 2024) His research spans Transaction Level Modeling (TLM) , MPSoC simulation , cloud computing , virtualization , Internet of Things/Vehicles , edge AI , and TinyML . He focuses on performance estimation, binary translation, and system-level simulation for embedded and distributed systems. The recent publications reflect a strong trend in IoT and vehicular networks , secure telemedicine , cloud orchestration , and embedded system simulation . His work integrates virtualization with security, real-time constraints, and machine learning on edge devices. Senior Fellow of the Higher Education Academy (SFHEA), 2024 Fellow of the Higher Education Academy (FHEA), 2021 Dr. Hamayun has supervised multiple research students and contributed to projects involving fatigue detection, person re-identification, retinal image analysis, and secure surgery frameworks. He has taught core computer science courses including Operating Systems, Networks, Data Structures, Algorithms, and Functional Programming at both undergraduate and postgraduate levels. His prior roles include Assistant Professor at NUST SEECS, R&D Engineer in France, and Lecturer at IUT1. He leads research in virtual prototyping and edge intelligence at the Dubai campus, contributing to smart city and cognitive systems initiatives. His lab work involves hybrid simulation environments, secure automotive platforms, and distributed IoT systems.

• Cybersecurity
• Internet of Things (IoT)
• Artificial Intelligence (AI)
• +۱۲ مورد دیگر

Jawad Ahmad is a Lecturer in the School of Computing, Engineering and the Built Environment at Edinburgh Napier University . His work is closely associated with the Centre for Cybersecurity, IoT and Cyberphysical Systems and the Centre for Distributed Computing, Networking and Security , where he contributes to cutting-edge research in secure and intelligent systems. His research interests are centered on cybersecurity , artificial intelligence , and Internet of Things (IoT) technologies. He focuses on developing advanced intrusion detection systems, privacy-preserving frameworks using federated learning and homomorphic encryption, and secure data transmission mechanisms leveraging chaos-based and quantum-inspired encryption. His interdisciplinary work extends to healthcare, smart agriculture, and environmental monitoring, demonstrating the broad applicability of his research. The analysis of his recent publications reveals a strong trend toward AI-driven security solutions, particularly using deep learning models like transformers and attention mechanisms for network intrusion detection. He also explores the integration of machine learning with blockchain and distributed ledgers for trusted threat intelligence sharing. His work consistently emphasizes real-world deployment, performance optimization, and resilience against cyber threats in industrial and healthcare settings. Funded Research Projects: Data Sharing in Highly Secure Environments (Innovate UK, £273,181) PhD Studentship on Homomorphic Encryption (6G Health Institute GmbH, £35,082) TrustShare: Privacy-Preserving Threat Intelligence Sharing (Innovate UK, £31,386) Cyber Hunt: Automated Cyberthreat Hunting (Norway Research Council, £37,500) AI Dashboard for COVID-19 Sentiment Analysis (Chief Scientists Office, £135,104) Dr Ahmad actively supervises postgraduate research, currently serving as Director of Studies for Hisham Ali and as second supervisor for other PhD candidates. He is involved in multiple collaborative research teams focusing on cybersecurity, AI, and IoT, often working with Prof Bill Buchanan and other leading researchers in the field. His research is published in high-impact journals such as IEEE Access , Frontiers in Computational Neuroscience , and Sensors , and presented at international conferences.

Michael Minockمشاهده پروفایل

دانشیار

• Natural Language Interfaces
• Knowledge Representation
• Databases
• +۴ مورد دیگر

Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.

Furat Al-Obaidyمشاهده پروفایل

مدرس

• Machine Learning
• Intelligent Systems
• VLSI Circuits
• +۷ مورد دیگر

Furat Al-Obaidy is a Lecturer in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn's College of Engineering and Computer Science. His expertise spans machine learning, intelligent systems, VLSI circuits, multi-core systems, FPGA architecture, and deep learning applications. PhD in Electrical & Computer Engineering from Ryerson University (2021) MSc in Electrical & Computer Engineering from Ryerson University (2016) MSc in Control & Instrumentation Engineering from University of Technology, Baghdad (1999) BSc in Control & Systems Engineering from University of Technology, Baghdad (1996) His research focuses on power-aware computing systems, thermal imaging for IC testing, hybrid cache architectures, and AI-driven network optimization. He has published extensively on topics including GPGPU power management, 3D NoC routing, and wireless sensor networks. His recent publications highlight applications of neural networks in cache optimization, FPGA architecture, and thermal imaging for hardware diagnostics. Earlier work includes control system simulations for wind turbines and power factor analysis in electrical circuits. Ryerson Graduate Development Award (2021) Ontario Graduate Scholarship Award (OGS) (2020) Ryerson Graduate Fellowship (2018) Graduate Research Excellence Award (2017) Queen Elizabeth II Graduate Scholarship in Science and Technology (2017)

David Bernal Neiraمشاهده پروفایل

استادیار

• Quantum Computing
• Optimization Algorithms
• Chemical Engineering
• +۳ مورد دیگر

David Bernal Neira is an Assistant Professor in the Davidson School of Chemical Engineering at Purdue University, joined in August 2023. His research focuses on optimization algorithms, quantum computing, and computational methods applied to chemical and energy systems. He holds a PhD in Chemical Engineering from Carnegie Mellon University and degrees from Universidad de Los Andes, Colombia. Education: PhD in Chemical Engineering, Carnegie Mellon University (2017–2021) M.Sc. in Chemical Engineering, Universidad de Los Andes (2014–2016) B.A.Sc. in Chemical Engineering, Universidad de Los Andes (2010–2014) B.A.Sc. in Physics, Universidad de Los Andes (2011–2018) Research Interests: His work bridges classical and quantum optimization, with applications in process systems, energy, and chemical engineering. Key areas include mathematical modeling, quantum annealing, and federated learning. He develops algorithms and software tools, such as GDP and QUBO frameworks, and explores quantum computing for chemistry and combinatorial problems. Publications: Over 30 peer-reviewed articles since 2020, focusing on quantum optimization, federated learning, and algorithm design. Recent work emphasizes benchmarking quantum hardware and hybrid quantum-classical methods. Awards: Fellow, National Academies (2025, 2023) Best Talk Award (2022) Outstanding Teaching Assistant (2019) Advising & Grants: Supervises graduate students in quantum computing and optimization. Collaborates with NASA, USRA, and industry on quantum projects. Formerly an Associate Scientist at NASA QuAIL and Adjunct Professor at Carnegie Mellon. Labs & Teams: Leads the SECQUOIA Research Group at Purdue, focusing on systems engineering via quantum and classical optimization. Active in federally funded initiatives and industry partnerships.

David Boyceمشاهده پروفایل

استاد مدعو

• Metropolitan Transportation Planning
• Urban Travel Forecasting
• Transportation Network Modeling
• +۳ مورد دیگر

David Boyce is an Adjunct Professor of Civil and Environmental Engineering at Northwestern University's McCormick School of Engineering. His expertise spans metropolitan transportation planning, urban travel forecasting models, and transportation network analysis. He holds a BS from Northwestern University, an MCP from the University of Pennsylvania, and a PhD in Regional Science from the University of Pennsylvania (1965). BS: Civil Engineering, Northwestern University (1961) MCP: City and Regional Planning, University of Pennsylvania (1963) PhD: Regional Science, University of Pennsylvania (1965) His research focuses on integrating transportation networks with land use, discrete choice theory, and entropy-based methods. Notable contributions include work on the TAPAS traffic assignment model and user-equilibrium route choice problems. Boyce has authored over 50 publications, emphasizing network equilibrium models, historical analyses of transportation theory, and policy applications. His articles highlight advancements in urban travel forecasting, multi-class models, and algorithmic solutions for traffic assignment. Recent work explores the historical foundations of transportation science and computational impacts on planning practices. Robert Herman Lifetime Achievement Award (2003) Fellows Awards from ASCE (2009), INFORMS (2003), and RSAI (2002) Boyce has advised on large-scale transportation projects and contributed to interdisciplinary teams advancing regional science. His research bridges theoretical models with practical urban planning challenges, emphasizing sustainable infrastructure and policy innovation.

Ye Xiaمشاهده پروفایل

دانشیار

• Computer Networking
• Wireless Networks
• Resource Allocation
• +۷ مورد دیگر

Ye Xia is an Associate Professor at the Department of Computer and Information Science and Engineering within the Herbert Wertheim College of Engineering at the University of Florida. Her research focuses on computer networking, particularly in wireless networks, resource allocation, energy efficiency, and content distribution algorithms. She holds a Ph.D. from UC Berkeley, an MS from Columbia University, and a BA from Harvard University. Education: Ph.D. in Electrical Engineering and Computer Science, University of California, Berkeley (2003) MS in Electrical and Computer Engineering, Columbia University (1995) BA in Electrical Engineering, Harvard University (1993) Research Interests: Dr. Xia’s work spans performance evaluation of network protocols, wireless link scheduling, energy-saving techniques in sensor networks, and stochastic processes. Her recent publications explore scheduling algorithms, network lifetime optimization, and distributed systems. Publications: Her recent work includes studies on maximum weight scheduling in wireless networks, distributed CSMA algorithms, and node density optimization in wireless sensor networks. These contributions emphasize practical applications of theoretical models to improve network efficiency and reliability. Awards & Grants: No specific awards or grants are listed in the provided text. Advising & Labs: No student advisees or lab affiliations are explicitly mentioned here.

Shixun Huangمشاهده پروفایل

مدرس

• Data Mining
• Machine Learning
• Algorithm Design
• +۳ مورد دیگر

Dr. Shixun Huang is a Lecturer in the School of Computing and Information Technology at the University of Wollongong, Australia. He holds a PhD from RMIT University and specializes in data mining, machine learning, and optimization algorithms for high-dimensional data problems. His research develops efficient algorithms for data discovery, similarity search, and network analysis. Current projects focus on optimized data acquisition strategies for machine learning, cost-effective labeling for graph neural networks, and cardinality estimation in high-dimensional databases. Methodologically, he combines combinatorial optimization with machine learning techniques. Dr. Huang supervises graduate research on diffusion models for medical imaging, graph prompt learning, and image captioning systems. His honors include multiple best paper awards at top database conferences and recognition for teaching excellence (College Top Course Award at RMIT). Recent publications address dataset distinctiveness maximization (WWW 2025), high-dimensional similarity search (VLDB 2025), and edge computing optimization (2024). Earlier foundational work established new approaches for influence maximization in social networks and temporal graph representation learning.

Zhongyuan Zhaoمشاهده پروفایل

عضو هیئت علمی

• Wireless Communications and Networking
• Machine Learning
• Signal Processing
• +۷ مورد دیگر

Zhongyuan Zhao is a Research Assistant Professor at Rice University's Department of Electrical and Computer Engineering, affiliated with the George R. Brown School of Engineering. He holds a Ph.D. in Computer Engineering from the University of Nebraska-Lincoln and completed his postdoctoral studies at Rice under Prof. Santiago Segarra. His research integrates graph-based neuro-symbolic approaches with domain-specific models to address challenges in wireless communications, edge computing, and networked systems. Dr. Zhao has over a decade of industry experience in 4G base-station development at Ericsson and ArrayComm, followed by academic research roles focusing on distributed algorithms and machine learning applications. His education includes a B.Sc. and M.S. in Electronic Engineering from the University of Electronic Science and Technology of China, where he also worked as a teaching assistant for national design contests. He earned a minor in Finance (15 credits) during his Ph.D. and is completing the CFA Level III program in 2024. Zhao has contributed to large-scale wireless testbeds like NEXTT and authored/co-authored 25+ peer-reviewed publications, 2 patents, and delivered academic service roles including conference session chair and journal reviewer. Research interests span autonomous networking, graph-based machine learning, stochastic optimization, and their applications in infrastructureless wireless systems. His work emphasizes distributed solutions for resource allocation, scheduling, and edge intelligence. Awards include the Future Faculty Fellowship (2023) and IEEE travel grants. He has mentored over 40 students through research internships, hackathons, and undergraduate projects, showcasing his commitment to education. Recent projects focus on scalable computation offloading, generalized backpressure routing in tactical networks, and neuro-symbolic AI frameworks for networked systems. His lab work involves open-source contributions to tools like the Actor-Twin Framework and SelR algorithm, promoting reproducibility and collaboration in the field.

Florentin Rochetمشاهده پروفایل

استادیار

• Anonymous Communication
• Network Security
• Transport Protocols
• +۵ مورد دیگر

Florentin Rochet is an Assistant Professor at the University of Namur's Faculty of Computer Science. His research focuses on anonymous communication , network security , and encrypted transport protocols , with a particular emphasis on improving privacy-enhancing technologies and protocol design. He holds a PhD in Applied Cryptography and has contributed to projects like TCPLS (TCP/TLS integration) and CLAPS (Tor path selection). Key academic milestones include joining UNamur in 2022 as an Assistant Professor, completing a postdoc at the University of Edinburgh (2021), and serving on program committees for Usenix Security, ACM CCS, and PoPETs. He has received the Usenix Security Noteworthy Reviewer Award (2023) and the 2020 Alan Berman Research Publication Award. His research spans flexible anonymous networks , secure transport protocols , and authentication technologies , with notable work on improving Tor's resilience against attacks through location-aware path selection and enhancing TLS/TCP integration for modern networks. He also leads a research group exploring eBPF security and WebAssembly runtime protections. Teaching responsibilities include computer security and privacy courses, with a 6-year teaching assistant position involving bachelor's/master's instruction and PhD supervision. His lab focuses on applied cybersecurity, network protocol innovation, and privacy-preserving systems design.

• Circular Economy
• Sustainable Development
• Sustainable Manufacturing
• +۴ مورد دیگر

Pezhman Ghadimi is an Assistant Professor of Manufacturing Systems at the School of Mechanical and Materials Engineering, University College Dublin (UCD). He holds a PhD in Industrial Engineering and Operations Management from the University of Limerick. His research focuses on Circular Economy, Sustainable Manufacturing, and Operations Research, with notable contributions to supply chain resilience, waste management, and global trade dynamics. He has secured significant grants, including a €2.56m Marie Curie Doctoral Network (iCircular3) for circular economy research. Education: BEng (Mazandaran University of Science & Tech, Iran), MEng (UTM Malaysia), PhD (University of Limerick), and a Professional Diploma in University Teaching & Learning (UCD). Research Interests: Circular Economy, Sustainable Development, Sustainable Supply Chains, Data Analytics, and Risk Analysis. He emphasizes applying these themes to global trade networks, medical device supply chains, and EV battery systems. Key Awards: Second Best PhD Student Award in Engineering (2015) by the Iranian Association for Postgraduate Students in Europe and Russia. He actively reviews for journals like International Journal of Production Research and Journal of Cleaner Production . Grants & Projects: Over 14 grants, including leadership in the iCircular3 project and contributions to CircularDev initiatives. His work addresses disruptions in global supply chains, particularly under pandemic conditions and geopolitical shifts. Labs/Teams: Leads research groups focused on circular economy strategies, sustainable manufacturing systems, and Industry 4.0 integration in supply chains. Collaborates internationally on projects involving China, the EU, and Ireland.

Liang Zhangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Mobile Edge Computing
• UAV Communications
• +۳ مورد دیگر

Dr. Liang Zhang is an Assistant Professor in the Department of Engineering and Aviation Sciences at the University of Maryland Eastern Shore (UMES). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (NJIT) and an M.S. in Information and Communication Engineering from the University of Science and Technology of China (USTC). His research focuses on machine learning, mobile edge computing, UAV communications, wireless communications, and IoT, with an emphasis on resource optimization and algorithm design. Education: Ph.D., Electrical and Computer Engineering, NJIT (2014–2020) M.S., Information and Communication Engineering, USTC (2011–2014) B.S., Electronic Science and Technology, Huazhong University of Science and Technology (HUST) Research Highlights: Dr. Zhang has pioneered work on deep reinforcement learning algorithms for UAV-assisted edge computing, caching optimization, and latency reduction in IoT systems. His contributions include the BRIDGES testbed project at George Mason University (GMU), supported by a $2.5M NSF grant, and the development of QoE-optimized frameworks for wireless VR and airborne networks. Awards & Recognition: Outstanding Dissertation Award (NJIT, 2023) Hashimoto Prize (NJIT, 2020) IEEE GLOBECOM Travel Grant (2016) Best Paper Award (IEEE ICNC, 2014) Professional Contributions: He has published 32 peer-reviewed articles and serves as a reviewer for IEEE journals. His work integrates machine learning with networking challenges, addressing practical issues like spectrum sharing in 5G, energy-efficient VM management, and dynamic resource allocation in optical networks.

Bing Liمشاهده پروفایل

استادیار

• Spatial Intelligence
• Sensing and Perception
• 3D Vision
• +۷ مورد دیگر

Dr. Bing Li is an Assistant Professor in the Department of Automotive Engineering at Clemson University, where he directs the AutoAI Lab. His research focuses on Spatial Intelligence for safer/assistive mobility and robots in dynamic environments, covering areas such as 3D vision, SLAM, deep learning, and human-centered AI. His work bridges fundamental AI research with practical applications in transportation and accessibility, particularly developing technologies to aid individuals with visual impairments. Dr. Li actively mentors students across academic levels and leads educational initiatives including summer programs for teenage drivers learning intelligent vehicle operation. Recent publications demonstrate a strong focus on 3D scene understanding, multi-sensor fusion, and assistive navigation systems. These works leverage cutting-edge deep learning approaches to solve complex perception challenges in autonomous systems and accessibility technologies.

• Control Systems Engineering
• Biomedical Engineering
• Aerospace Engineering
• +۳ مورد دیگر

Professor Paul Stewart is affiliated with the University of Derby's College of Science and Engineering, specializing in Control and Systems Engineering. His research focuses on biomedical monitoring systems, nuclear reactor optimization, airport operations, and aerospace propulsion. Notable projects include developing real-time blood pressure estimation systems for dialysis patients and advancing modular nuclear reactor designs using genetic algorithms. He collaborates across disciplines, addressing challenges in energy efficiency, control systems, and transportation logistics. His work integrates advanced algorithms and engineering principles to solve complex problems in healthcare, aerospace, and sustainable energy. Research interests span hemodialysis monitoring technologies, genetic algorithm applications in modular systems, and multi-objective optimization for airport ground movements. His contributions also include pioneering studies on solar-powered high-altitude aircraft and energy-efficient electric propulsion systems. Despite no explicitly listed awards or grants, his prolific publications reflect sustained academic and industrial engagement. Key collaborations involve institutions like the University of Derby and international teams in nephrology, aerospace, and transportation engineering fields. While no labs or formal research teams are named, his work indicates involvement in interdisciplinary research networks focused on practical engineering solutions with societal impact.

Irina Benedykمشاهده پروفایل

استادیار

• Intermodal Freight Transportation
• Optimal Investment Allocation
• Freight Demand Modeling
• +۶ مورد دیگر

Dr. Irina Benedyk is an Assistant Professor at the Department of Civil, Structural and Environmental Engineering, School of Engineering and Applied Sciences, SUNY Buffalo. She holds a PhD and has over 10 years of combined industry and academic experience in marine intermodal logistics and freight transportation. Previously, she worked at CMA CGM S.A. and a major Russian forwarding company in roles ranging from import/export specialist to head of consulting. Her research focuses on intermodal freight transportation, human factors in transportation engineering, driver cognition, and sustainable transportation. She develops quantitative models to analyze freight demand, investment decisions, and emerging technologies like self-driving cars. She also investigates the impact of augmented reality (e.g., Pokémon GO) on behavior and transportation safety during extreme weather events. Her work integrates large-scale behavioral data analysis, simulation experiments, and mixed-methods approaches. Notable contributions include modeling driver physiological states using EEG and analyzing temporal instability of environmental factors during snowstorms. She collaborates on projects like vehicle-to-infrastructure communication systems for winter driving. Dr. Benedyk has received an Editor’s choice award for her 2018 paper on freight decision-making for the Northern Sea Route. Her research spans academic publications, industry partnerships, and innovative applications of AI in training and safety systems.