جست‌وجوی استاد و پژوهشگر

Ala Al Kafriمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Medical Imaging Analysis
• +۵ مورد دیگر

Ala Al Kafri is a Senior Lecturer in Computer Science at Teesside University's Department of Computing & Games, affiliated with the Centre for Digital Innovation. With over 16 years of teaching experience across multiple institutions globally, he specializes in machine learning, artificial intelligence, and medical imaging applications. His research focuses on AI-driven diagnostics for lumbar spine diseases, with notable contributions to MRI analysis using convolutional neural networks. He has collaborated on projects involving physiotherapy education technology, autism screening, and multidisciplinary learning initiatives. Education includes a PhD in Machine Learning for Lumbar Spine Disease Diagnosis from Liverpool John Moores University (2019), an MSc from New York Institute of Technology (2005), and a bachelor’s degree. Professional development includes a Postgraduate Certificate in Academic Practice. Research interests span medical AI, data science, and education technology, with emphasis on practical applications in healthcare and learning environments. Key collaborations include developing an AI tool for lumbar spine disease prediction with University Multimedia Nusantara, and creating video-based physiotherapy assessment systems. Recent projects include AI-powered physiotherapy education systems, autism screening using ML, and smart highway vehicle detection. His work integrates technical innovation with real-world societal impact through multidisciplinary approaches.

Dhirendra Shuklaمشاهده پروفایل

استاد

• Entrepreneurial Finance
• Corporate Governance
• Venture Capital
• +۷ مورد دیگر

Dr. Dhirendra Shukla is the Dr. J. Herbert Smith ACOA Chair at the University of New Brunswick's J Herbert Smith Centre for TME. He holds a PhD in Entrepreneurial Finance from King's College London, an MBA from the Telfer School of Management (University of Ottawa), and advanced engineering degrees from the University of Bradford. His research focuses on venture capital, corporate governance, entrepreneurial finance, and sustainability in renewable energy and technology-driven industries. Education: BEng/MSc in Chemical Engineering & Computing (University of Bradford) MBA (University of Ottawa/Telfer School) PhD in Entrepreneurial Finance (King's College London) His teaching portfolio includes courses on entrepreneurial finance, global engineering, and leadership in technology management. Research interests span venture capital dynamics, IPO governance, blockchain applications, and smart energy systems. He has presented at conferences like the Strategic Management Society and Academy of International Business, contributing to cross-national studies on corporate governance and IPO performance. Shukla's work bridges academia and industry, with projects addressing renewable energy partnerships in Atlantic Canada and federated learning systems for privacy-preserving data analytics. His recent studies explore AI-driven digital twins, cyber-physical systems in smart cities, and blockchain adoption in higher education.

Shameek Bhattacharjeeمشاهده پروفایل

استادیار

• Cybersecurity
• IoT Security
• Wireless Networks
• +۳ مورد دیگر

Dr. Shameek Bhattacharjee is an Assistant Professor of Computer Science at Western Michigan University and Director of the STAR Lab (Security Trust and AI for Resilient Systems). He holds a PhD in Computer Engineering from the University of Central Florida. Research Focus: Designs data-driven and model-driven frameworks for safety and security in smart connected systems. Key areas include IoT security, anomaly detection, trust management, and resilient learning for cyber-physical systems. Publications: Recent work demonstrates consistent focus on security challenges in critical infrastructure including smart transportation, water systems, and energy grids. Research employs advanced machine learning techniques for attack detection and resilient system design in IoT environments.

• Computer Systems Performance Analysis
• Autonomic Computing
• Cloud Computing
• +۵ مورد دیگر

Daniel Brazier is a part-time Research Affiliate at George Mason University, focusing on advanced computing systems research. His work intersects autonomic computing, cybersecurity, and distributed systems optimization. He specializes in performance modeling for cloud/fog environments, dynamic reconfiguration strategies, and resilience engineering. Key research areas include: Autonomic resource management in edge and cloud systems Stochastic optimization algorithms Security mechanisms like moving target defense Performance-security tradeoff analysis Decentralized runtime system modeling His recent work emphasizes practical frameworks for distributed system trustworthiness and adaptive elasticity control under variable workloads. Outputs include novel meta-heuristic algorithms for virtual network optimization and analytic models for parallel server architectures. Publications highlight contributions to fog/cloud computing, manufacturing process optimization, and security-aware system design. Current efforts focus on applying decision analytics to smart manufacturing and autonomic emergency department frameworks.

Pantaleone Nespoliمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Cyber defense
• Data analysis
• +۳ مورد دیگر

Pantaleone Nespoli is a postdoctoral researcher at the University of Murcia's Department of Information and Communications Engineering. He holds a PhD (cum laude) and Master's degree (110/110 cum laude) from the University of Naples Federico II, Italy. His research focuses on cybersecurity, cyber defense, IoT security, and SIEM systems. He is affiliated with CyberDataLab UMU and has contributed to projects like DEFENDER, COBRA 2.0, and EU-GUARDIAN, addressing AI-driven cyber defense and threat simulation. Education: PhD in Cybersecurity (2021, University of Murcia), M.Sc. in Computer Engineering (2017, University of Naples), B.Sc. in Computer Engineering (2013, University of Naples). Professional experience includes internships at NEC Labs EU and research collaborations with institutions like INCIBE and the Ministry of Defence. Research Interests: Cybersecurity frameworks, intrusion detection systems (IDS), SIEM integration, cyber deception tactics, and AI applications in defense. He emphasizes practical solutions for IoT security and cyber range training platforms. Notable Projects: EU-GUARDIAN (€13M+ funding) for AI-based cyber incident management, DEFENDER targeting IoT/5G/IoC security, and CDL-TALENTUM for cybersecurity workforce development. Awards include the Clusit Thesis Award (2018) and Ericsson Innovation Awards recognition. Labs/Teams: Active in CyberDataLab UMU, developing cutting-edge tools like SCORPION Cyber Range for gamified cybersecurity training and deception-based defense systems.

Lav Guptaمشاهده پروفایل

استادیار

• Cybersecurity
• Networking
• Cloud Computing
• +۵ مورد دیگر

Lav Gupta is an Assistant Professor of Computer Science at the University of Missouri–St. Louis. He holds a Ph.D. and MS from Washington University in St. Louis, and BS/MS degrees from Indian Institute of Technology Kanpur and Roorkee. His research focuses on cybersecurity, networking, cloud computing, and AI applications in critical infrastructure like healthcare and finance. Research interests include secure multi-cloud management, AI-driven fault detection in virtual networks, and resilient healthcare systems. He has published 13+ first-author papers in journals/conferences, with work on hierarchical deep learning for cybersecurity and edge-cloud AI. His funded projects include NSF/SaTC grants and UM System Broadband Research initiatives. Current work involves standardization efforts at ITU-T Geneva for inter-cloud service management frameworks. Notable awards include the 2015 Broadcom Foundation Research Award and Vodafone’s 2014 Wireless Innovation Prize. He leads the Cybersecurity and Networking Lab, offering GRA positions in multi-cloud security and 5G/6G systems. Students can contact via email or office hours (Wednesdays in-person/Zoom). Grants and collaborations highlight industry ties through Sony and ITU-T. Future work emphasizes socially impactful applications like smart cities and critical patient monitoring systems.

• Machine Learning
• Healthcare Analytics
• Telecommunication Industry Analysis
• +۳ مورد دیگر

Karl Hall is a Lecturer in Computing & Games at the Department of Computing & Games, Teesside University. His research focuses on machine learning applications across diverse domains including healthcare analytics, telecommunication, traffic modeling, and social network analysis. He specializes in developing predictive models for credit risk assessment, disease diagnosis, customer behavior analysis, and infrastructure optimization. Key research interests include machine learning algorithms (e.g., Random Forest, CNNs), explainable AI systems, and their practical implementations in real-world scenarios. He has conducted studies on diabetes prediction, pandemic forecasting, and metaverse risk assessment. His work often bridges theoretical models with practical industry challenges, particularly in finance, healthcare, and transportation sectors. Recent research trends show a focus on interdisciplinary applications of AI, such as biomedical information retrieval frameworks and transformer-based models. He has collaborated on projects analyzing UK traffic datasets and Bitcoin trader networks, highlighting his versatility in data-driven research. No awards or grants are explicitly mentioned in the provided texts. His advising and professional activities are not detailed in the current data, but his publications reflect active engagement with both academic and applied research challenges.

• Blockchain Technology
• Cybersecurity
• Healthcare Informatics
• +۵ مورد دیگر

Prof. Hassan Abdalla serves as Vice-President (London Campus) at the University of East London (UEL), where he also contributes to research across interdisciplinary fields. His work spans blockchain technology, healthcare informatics, sustainable engineering, and cybersecurity. He has co-authored numerous peer-reviewed articles and conference papers, focusing on applications of technology to address societal challenges such as corruption in smart cities, energy efficiency in housing, and pandemic impacts on healthcare systems. Research Interests: Development of blockchain-based solutions for e-procurement and cybersecurity. Healthcare innovations, including predictive diagnostics for necrotizing enterocolitis using machine learning. Sustainable design methodologies, including fractal approaches to particulate systems and autonomous vehicle systems for reducing emissions. Neurological and biomechanical studies on gait stability and pedestrian behavior analysis. Energy conservation through smart technologies and behavioral interventions in domestic settings. Notable Articles Trends: His publications reflect a strong emphasis on interdisciplinary research, integrating computer science, engineering, and healthcare. Recent work highlights blockchain’s role in public administration transparency, while earlier studies explore materials science and manufacturing optimization. Grants and Awards: No specific grants or awards are documented in the provided texts. Labs/Teams: While not explicitly stated, his research collaborations suggest involvement with UEL’s technology and health sciences groups, as well as international partnerships in global health studies.

Emad Kassemمشاهده پروفایل

دانشیار

• Pavement Engineering
• Materials Characterization
• Infrastructure Materials
• +۲ مورد دیگر

Emad Kassem is an Associate Professor in the Department of Civil & Environmental Engineering at the University of Idaho. His research focuses on pavement materials characterization, infrastructure durability, and sustainable construction technologies. Education includes: Ph.D. in Civil Engineering, Texas A&M University (2008) M.S. in Civil Engineering, Texas A&M University (2005) B.S. in Civil Engineering, Zagazig University (2001) Research areas include asphalt material performance, recycled materials utilization, non-destructive pavement evaluation, and computational modeling of infrastructure systems. Kassem develops predictive models for pavement performance and material behavior. Courses taught: Properties of Construction Materials Pavement Preservation and Management Engineering Statics Engineering Mechanics of Materials Awards: Engineers' Council Outstanding Engineering Achievement Merit Award (2016) Texas A&M Transportation Institute/Trinity New Researcher Award (2011)

• Machine Learning
• Intelligent Agents
• Computer Vision
• +۴ مورد دیگر

Konstantinos Blekas is a Professor at the Department of Computer Engineering and Informatics, University of Ioannina, Greece. He is affiliated with the Polytechnic School and teaches advanced courses such as 'Machine Learning' (MYE002) and 'Probability and Statistics' (MYY304). His research focuses on Machine Learning, Intelligent Agents, Computer Vision, and Bioinformatics, with particular expertise in Reinforcement Learning, Deep Learning, and their applications in autonomous systems, traffic management, and aerospace engineering. Education: Ph.D. in Electrical and Computer Engineering, National Technical University of Athens (1997) Diploma in Electrical Engineering, National Technical University of Athens (1993) Research Interests: Dr. Blekas explores cutting-edge topics in machine learning, including generative adversarial networks (GANs), multi-agent systems, and reinforcement learning for autonomous navigation. His work spans domains such as unmanned surface vehicles, air traffic management, and medical informatics. Notable contributions include advanced frameworks for flight trajectory modeling, urban traffic optimization, and brain functional network analysis. Awards and Recognition: While specific awards are not explicitly listed, his extensive publications and contributions to AI and robotics reflect significant academic impact. Advising and Grants: He supervises research in machine learning applications, though specific student names or grant details are not provided in the texts. His courses emphasize practical implementation, with resources available on e-learning platforms like e-course.uoi.gr. Labs and Teams: Engaged in collaborative projects involving robotics and AI, though no specific lab names are mentioned. His work integrates interdisciplinary approaches across computer science, engineering, and biomedical fields.

• Maritime Engineering
• Fluid Mechanics
• Geospatial Ship Data Analysis
• +۳ مورد دیگر

Anna Mujal Colilles is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), specifically affiliated with the Faculty of Nautical Engineering of Barcelona (FNB) within the Department of Nautical Science and Engineering. She holds a PhD in Civil Engineering and serves as a researcher at the CREMIT - Centre de Recerca de Motors i Instal·lacions Tèrmiques. Her academic career spans multiple research domains with 97 documented research activities as of the latest data. Her primary research interests include Maritime Engineering, Fluid Mechanics, and Geospatial Analysis of Ship Data using Automatic Identification System (AIS) and Vessel Monitoring System (VMS) technologies. Her work bridges theoretical fluid dynamics with practical maritime applications, particularly focusing on vessel traffic patterns, port operations, and environmental impacts of maritime activities. Recent publications demonstrate her growing interest in maritime education challenges, particularly absenteeism patterns in nautical studies. Dr. Mujal Colilles' research output shows consistent productivity across multiple disciplines, with recent publications spanning from vessel motion monitoring to offshore wind energy impacts and fisheries management. Her work aligns with several Sustainable Development Goals, particularly Affordable and Clean Energy (SDG 7) and Life Below Water (SDG 14), reflecting her commitment to environmentally sustainable maritime practices. She maintains active collaborations with numerous researchers across UPC, particularly with Marcel·la Castells-Sanabra, Xavier Gironella, and Mark James. Her research projects often involve interdisciplinary teams addressing complex maritime challenges from multiple perspectives including engineering, environmental science, and social sciences. Her methodological approach emphasizes empirical data collection combined with advanced analytical techniques, particularly in geospatial data processing and fluid dynamics modeling. Recent work demonstrates increasing sophistication in handling large-scale maritime datasets and developing standardized workflows for vessel tracking analysis.

Visham Ramsurrunمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Internet of Things (IoT)
• Network Security
• +۷ مورد دیگر

Visham Ramsurrun is a Senior Lecturer and Programme Coordinator of the MSc Cyber Security programme at Middlesex University Mauritius. He holds a Ph.D. and B.Sc. in Computer Science & Engineering from the University of Mauritius, and a Postgraduate Certificate in Higher Education from Middlesex University (UK). His research focuses on cybersecurity, IoT, network design, machine learning, and smart agriculture. Education: Ph.D. in Computer Science & Engineering (2007-2010), University of Mauritius Postgraduate Certificate in Higher Education (2017-2018), Middlesex University (UK) BSc (Hons) Computer Science & Engineering (2000-2003), University of Mauritius Research interests include: Cybersecurity frameworks for IoT and autonomous systems Blockchain applications in healthcare and data management Network defense mechanisms (e.g., SDN-based moving target defense) Energy-efficient sensor networks and transmission optimization Smart agriculture systems and environmental monitoring Recent publications emphasize cybersecurity in autonomous vehicles, IoT-enabled healthcare systems, and sensor-based home security solutions. He has secured grants from the Mauritius Research and Innovation Council (MRIC) and collaborates with industry partners on innovation projects. His work includes developing augmented reality control platforms and low-cost air quality monitoring systems. As a member of the IPv6 Forum Mauritius, he advocates for network architecture advancements. His research spans academic collaborations across disciplines, with a focus on underserved regions' technological challenges.

Giridhar Reddy Bojjaمشاهده پروفایل

استادیار

• Business Analytics
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Giridhar Reddy Bojja is an Assistant Professor of Information Systems & Analytics at the College of Business, Michigan Technological University. His academic office is located in Academic Office Building G018. He holds a strong focus on interdisciplinary research and teaching in data-driven fields. Research Interests: Information Systems Capabilities and Firm Performance Social Media Analytics and Healthcare Applications Econometric Modeling in Technology Impact Design Science Research Methodologies Teaching Specializations: Business Analytics (ML, DL, Text Mining) Generative AI and Its Applications Management Science and Social Network Analysis Research Themes: His work spans AI-driven IoT security frameworks, healthcare data analytics, blockchain-enabled healthcare systems, and organizational impact of IT investments. Recent projects include predictive models for patient outcomes and frameworks for resilient IoT networks. Professional Background: His research has been published in top-tier journals and conferences, with a focus on healthcare IT, cybersecurity, and AI applications. He actively contributes to both theoretical and applied research in information systems.

Kui Wuمشاهده پروفایل

استاد

• Computer Networks
• Wireless and Mobile Networking
• Mobile Computing
• +۵ مورد دیگر

Prof. Kui Wu is a Professor in the Department of Computer Science at the University of Victoria, affiliated with the Faculty of Engineering and Computer Science. His research focuses on computer networks, wireless and mobile networking, mobile computing, and network security. He is part of the Parallel, Networking and Distributed Computing (PANDA) research group. Key areas of expertise include distributed learning frameworks, autonomous systems, IoT anomaly detection, and edge computing architectures. His work integrates machine learning techniques with network optimization, addressing challenges in real-time systems, security, and resource allocation. Notable contributions include advancements in federated learning, privacy-preserving distributed systems, and UAV-based monitoring solutions. Prof. Wu's research also explores edge computing innovations, such as smart contract-aided IoT resource sharing and energy-efficient edge data centers. He has contributed to over 50 peer-reviewed publications, with recent work emphasizing AI-driven network design, anomaly detection in IoT, and reinforcement learning applications in autonomous driving safety. His research has practical implications for improving the reliability and efficiency of next-generation communication and computing infrastructures.

Sean Choiمشاهده پروفایل

استادیار

• Computer Networking
• Programmable Hardware
• Machine Learning Infrastructure
• +۳ مورد دیگر

Sean Choi is an Assistant Professor in the Department of Computer Science and Engineering at Santa Clara University. He holds a Ph.D. in Electrical Engineering from Stanford University (2020), M.S. in Computer Science from Stanford (2013), and a dual B.S. in Computer Science and Business Economics & Management from Caltech (2011). His research focuses on programmable network hardware, including smartNICs, and their applications in machine learning, blockchain, and cloud computing. Education: Ph.D. in Electrical Engineering, Stanford University (2020) M.S. in Computer Science, Stanford University (2013) B.S. in Computer Science & Business Economics, California Institute of Technology (2011) Research Interests: Programmable Network Switches, SmartNIC Acceleration, Cloud Infrastructure, Serverless Computing, Blockchain Optimization, and Privacy-Preserving Machine Learning . Recent Work: Explores leveraging programmable hardware for low-latency machine learning inference, blockchain scalability via NIC-assisted consensus, and privacy-preserving federated learning through homomorphic encryption offload. His NSF-funded project develops ultra-low-latency ML systems using programmable networks. Awards: NSF CRII 2023 Award Hewlett Packard Enterprise Grant Labs/Teams: Leads research on network programmability and hardware acceleration within Santa Clara University's School of Engineering. Collaborates with industry on data center network optimization and cloud infrastructure projects.