جست‌وجوی استاد و پژوهشگر

Sanjay Madriaمشاهده پروفایل

استاد

• Cybersecurity
• Sensor Cloud Computing
• Delay-Tolerant Networks
• +۵ مورد دیگر

Sanjay Madria is a Curators’ Distinguished Professor in the Department of Computer Science at Missouri University of Science and Technology. His research spans mobile and sensor computing , machine learning , cloud security , and blockchain , with applications in underground mining safety , UAV systems , and disaster response . PhD: Indian Institute of Technology, Delhi (1995) His work integrates secure sensor cloud architectures , delay-tolerant network optimization , and AI-driven risk assessment . Recent publications focus on UAV swarm detection , reinforcement learning for mining environments , and privacy-preserving social media analytics . Scientific Awards include IEEE Best Paper Awards , NRC Fellowships , and JSPS Fellowship . Funded by NSF , NIST , DOE , Honeywell , and Boeing . He contributes to IEEE/ACM Distinguished Speaker programs and leads the Center for Intelligent Infrastructure at Missouri S&T.

• Deep Learning
• Federated Learning
• Data Analysis on Graphs
• +۹ مورد دیگر

Vinay Chakravarthi Gogineni is an Assistant Professor at The Maersk Mc-Kinney Moller Institute , University of Southern Denmark (SDU), specializing in SDU Applied AI and Data Science . His research integrates advanced AI techniques into healthcare, industrial IoT, and fusion energy systems, with a strong focus on federated learning, privacy-preserving AI, and ethical machine learning practices. Education: Ph.D. in Distributed Machine Learning, Indian Institute of Technology Kharagpur (Aug 2019) Research Interests: Dr. Gogineni's work spans Deep Learning , Federated Learning , and Graph Data Analysis . He develops personalized AI models for healthcare applications, including cervical cancer screening, colorectal cancer detection, and dementia prediction. His innovations in Machine Unlearning ensure compliance with GDPR and the EU AI Act, while his work on Physics-Informed Neural Networks enhances predictive accuracy in fusion energy systems. Key areas include self-supervised learning, continual learning, and fairness-aware AI. Scientific Awards: HC Ørsted Research Talent Award (2024, Denmark) ERCIM Alain Bensoussan Fellowship (2019) Best Paper Award , APSIPA ASC-2021, Tokyo Professional Roles: IEEE Senior Member and editorial board member of IEEE Sensors Journal . He teaches courses on AI for Healthcare Data and Calculus and Linear Algebra , fostering interdisciplinary education in applied AI.

John Breslinمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Social Semantics
• +۷ مورد دیگر

John Breslin is a Personal Professor in Electronic Engineering at the University of Galway, leading the TechInnovate/AgInnovate programs. His roles include Director of the Insight (Data Analytics) and VistaMilk (AgTech) research centers. He has authored 350+ publications and co-created the SIOC framework, widely used in web applications. His research spans Data Science, AI, Semantic Web, IoT, and Entrepreneurship. He has won multiple awards, including the Best Irish-Published Book and IIA Net Visionary Awards. His teaching spans 25 years, covering topics like Digital Control Systems and Innovation. He co-founded boards.ie, StreamGlider, and leads initiatives in Galway's innovation ecosystem. Research Interests: Data Science, AI, Social Semantics, IoT, Agricultural Technology, and Innovation. He is ranked top in Engineering and Computer Science citations at the University of Galway. His work contributes to UN SDGs related to Industry, Innovation, and Infrastructure. Education: Details not explicitly provided in text. Collaborations include Bosch, Cisco, IBM, and Harvard Medical School. Active in industry partnerships and startups, including the PorterShed Innovation District. Awards: Best Paper Awards, Entrepreneurship Recognition, and Academic Excellence. Supervised nearly 30,000 ECTS across modules, with a focus on graduate and postgraduate tech innovation programs.

• Cybersecurity
• Blockchain Technology
• Internet of Things (IoT)
• +۵ مورد دیگر

Rajarajan, M. is a Professor at City, University of London , specializing in Cybersecurity , Blockchain Technology , and Internet of Things (IoT) Security . His research focuses on secure access control, privacy-preserving systems, and risk assessment in cloud and smart grid environments. Research Interests : Blockchain for IoT and IIoT Smart Grid Security and Pricing Models Privacy-Preserving Machine Learning Cloud Computing Risk Assessment DDoS Attack Mitigation Biometric and Continuous Authentication Article Trends : 2023: Blockchain for IoT access control, dynamic cloud risk models, ransomware detection 2022: Smart grid security, DDoS propagation, privacy in autonomous vehicles 2021: Post-quantum cryptography, decentralized identity frameworks 2020: Microgrid pricing, IoT message broker transparency Key Collaborators : Smith-Creasey, M. (IoT Authentication) Tahir, S. (Privacy in IoT) Acarali, D. (Smart Grid Security) Boiarkin, V. (Energy Grid Pricing)

Michael Kampمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Deep Learning Theory
• +۵ مورد دیگر

Michael Kamp is an Associate Professor for Machine Learning and Artificial Intelligence at TU Dortmund University and a faculty member of the Lamarr Institute for Machine Learning and Artificial Intelligence. He maintains affiliations with the Institute for Artificial Intelligence in Medicine (IKIM) at University Medicine Essen and collaborates with institutions in the UA Ruhr consortium. Formerly led Trustworthy Machine Learning group at IKIM Postdoctoral researcher at CISPA Helmholtz Center (2021) and Monash University (2019-2021) Doctorate from University of Bonn His research focuses on trustworthy machine learning through three pillars: deep learning theory (loss surfaces, flatness, generalization), causal representation learning for explainability, and federated learning for privacy-preserving decentralized systems. Applications span healthcare, autonomous driving, and cybersecurity. Recent publications at AAAI/ICLR/NeurIPS explore: federated optimization algorithms for non-IID data, privacy-preserving causal discovery, and flatness-aware regularization techniques. His work combines theoretical rigor with real-world deployment challenges. NeurIPS 2021 Outstanding Reviewer Award ICLR 2021 Reviewer Award Best Paper at PDFL'20 Workshop NeurIPS 2019 Best Reviewer Award ICML 2019 Reviewer Award He contributes to the editorial board of Springer's Machine Learning journal and serves in the ELLIS society. His group's tools address early disease detection, 3D medical shape analysis, and domain transfer challenges while maintaining ethical constraints.

Muhammad Asadمشاهده پروفایل

مدرس

• Federated Learning
• Wireless Communication
• Blockchain Technologies
• +۷ مورد دیگر

Dr. Muhammad Asad is a Lecturer in Computer Science at the School of Engineering, Computing and Mathematics. With 13+ years of research experience, his work focuses on federated learning, wireless communication systems, and IoT network optimization. Primary Role: Lecturer in Computer Science Key Research Areas: Federated Learning, Wireless Communication, and IoT Security His research spans communication-efficient AI systems, blockchain-integrated learning frameworks, and energy optimization in sensor networks. Recent work includes security-enhanced federated learning protocols for vehicular networks and healthcare analytics. Publications (2018-2024) demonstrate consistent contributions to fields including: Privacy-preserving machine learning IoT-oriented network architectures Blockchain-based security systems Medical imaging diagnostics

Tughrul Arslanمشاهده پروفایل

استاد

• Integrated Electronic Systems
• Reconfigurable Hardware and Software
• Low Power Design
• +۶ مورد دیگر

Professor Tughrul Arslan holds the Chair of Integrated Electronic Systems at the School of Engineering, University of Edinburgh . He leads the Embedded Wireless and Wearable Sensor Systems (EWireless) Group and co-founded sensewhere Ltd. and Sofant Technologies . His research spans reconfigurable architectures, low-power wireless systems, and biomedical RF sensing. Academic Background: BEng and PhD in Electronics Professional Affiliations: Senior Member IEEE, Fellow IET, Chartered Engineer His work focuses on smart wearable devices , indoor positioning systems , and AI-driven healthcare monitoring . Recent publications emphasize microwave imaging for dementia detection , edge AI accelerators , and non-invasive tremor monitoring . Key contributions include patented technologies like the Reconfigurable Instruction Cell Architecture (RICA) . Award-winning academic, he has supervised over 40 PhD students and authored 400+ peer-reviewed papers. His external roles include Chief Technology Officer at sensewhere , driving commercialization of indoor navigation solutions.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۱۱ مورد دیگر

Fatima Boukari is an Associate Professor in Computer Science within the Division of Physics, Engineering, Mathematics and Computer Sciences at Delaware State University. Her research bridges artificial intelligence, deep learning, and mathematical modeling to develop robust solutions for biomedical engineering, cell biology, and agricultural technology challenges. Education: B.Sc. in Computer Science Engineering from University of Annaba, Algeria Dual M.Sc. degrees in Computer Systems Architectures and Parallel Computing from Algeria-Glasgow Ph.D. in Mathematics & Physics from Delaware State University Dr. Boukari's research centers on foundational Deep Learning architectures and mathematical modeling applied to biomedical diagnostics and cell dynamics analysis. Her work in reinforcement learning and transfer learning enhances decision-making for autonomous systems, while her cognitive modeling research decodes human perception using EEG data. She pioneers multi-modal distributed learning systems that maintain privacy across heterogeneous sensor networks, addressing critical gaps in military ISR applications. Her recent publications reveal a strong trajectory toward spectral data analysis for medical diagnostics and AI-driven cognitive modeling , with increasing emphasis on trustworthy AI solutions for healthcare and environmental sustainability. The consistent focus on cell segmentation/tracking algorithms demonstrates her commitment to advancing biomedical image analysis. Scientific Awards: No scientific awards, prizes, or fellowships listed in available information Dr. Boukari has mentored over 40 undergraduate and 2 graduate students from underrepresented STEM backgrounds. Her active research portfolio includes: NSF CISE grant for biomolecular detection using physics-informed machine learning Air Force RITA/UARC project on neuroscience computational modeling Air Force project building robust multi-modal distributed learning systems DE-CTR ACCEL project for COVID-19 respiratory disease diagnosis NSF grant for Delaware and Mid-Atlantic Data Science Corps Research scientist role in AI-CLIMATE National AI Research Institute She leads the Applied Interdisciplinary Data Science (AIDA) Laboratory and serves as Project Lead for the CAST E-IoT Center's four agricultural research thrusts. As Team Lead of the 1890 Working Group on Artificial Intelligence, she drives initiatives addressing climate change resilience and food security through responsible AI development.

Stephan Siggمشاهده پروفایل

استاد

• Distributed Systems
• Ubiquitous Computing
• RF Sensing
• +۵ مورد دیگر

Stephan Sigg is a Professor at Aalto University's Department of Information and Communications Engineering, where he leads the Ambient Intelligence research group . His research focuses on algorithm design for distributed systems, including RF sensing, context-based security, and device-free activity recognition. He serves on editorial boards for Elsevier Journal on Computer Communications and Springer Personal and Ubiquitous Computing , and actively contributes to conferences like IEEE PerCom and ACM Ubicomp. Research Interests: Dr. Sigg specializes in proactive computing, distributed adaptive beamforming, mobile crowdsourcing, and secure spontaneous device pairing. His work bridges wireless communication, machine learning, and ubiquitous systems to develop context-aware solutions for IoT environments. Publication Trends (2019-2025): Recent works demonstrate a strong emphasis on RF-based sensing (WiFi/mmWave radar/RFID) for human activity recognition, 5G-integrated sensing, privacy-preserving AI (federated learning), and healthcare applications. Multimodal approaches combining radar, video, and physiological signals are prominent. Awards: Summa cum laude dissertation honors in Computer Science (2008) VDI-Nordhessen Outstanding Dissertation Prize (2009) Research Leadership: Leads the Ambient Intelligence group exploring RF sensing, edge AI, and human-computer interaction. Collaborates extensively with industry on 5G/6G applications and IoT security. Supervises doctoral candidates in wireless systems and ubiquitous computing.

• Statistics
• Applied Statistics
• Geostatistics
• +۳ مورد دیگر

Albrecht Gebhardt is an Assistant Professor in the Department of Statistics at Alpen-Adria-Universität Klagenfurt. He serves as deputy director of the Institute for Statistics and is a member of the Mathematics Curriculum Commission. His research focuses on applied statistics, geostatistics, and methodological innovations in statistical analysis. He actively contributes to academic administration, curriculum development, and interdisciplinary research projects. Recent work spans topics like algorithmic targeting in political communication, multi-robot systems, and healthcare workforce dynamics. His publications reflect a broad engagement with data-driven methodologies across disciplines such as computer science, social sciences, and engineering. Current research emphasizes practical applications of statistical methods in complex systems analysis.

Timo Minssenمشاهده پروفایل

استاد

• Intellectual Property Law
• Competition and Regulatory Law
• Biotechnology and Pharmaceutical Law
• +۸ مورد دیگر

Timo Minssen is Professor of Law at the University of Copenhagen and Founding Director of the Center for Advanced Studies in Bioscience Innovation Law (CeBIL) . He holds affiliations as an Inter-CeBIL Research Affiliate at the Petrie-Flom Center (Harvard Law School) and an LML Research Affiliate at the University of Cambridge. His career spans roles such as TUM Global Visiting Professor (Technical University of Munich), Visiting Scholar at Harvard, Oxford, and Cambridge, and PI for the Novo Nordisk Foundation’s Inter-CeBIL research program. Education: German Staatsexamen (Göttingen), Swedish LL.M., LL.Lic., and LL.D. (Lund & Uppsala Universities). Specializations: Intellectual Property, Competition & Regulatory Law in health/life sciences; focuses on genome editing , AI , big data , and quantum technologies . His research bridges legal, ethical, and commercial challenges in biomedical innovation, emphasizing sustainable R&D regulation , open innovation , and technology transfer . He has published over 220 articles in journals like Science , Nature Biotechnology , and JAMA , with themes spanning pharmaceutical patent law, bioethics, and AI governance. Scientific awards : King Oscar Award (2014), Jorcks Foundation Prize (2017), Awapatent Research Prize (2009). Grants : Novo Nordisk Foundation (50 million DKK), EU Horizon, SSHRC, Wallenberg Project. He advises the WHO , WIPO , and EU Commission on digital health, AMR, and AI policy, and supervises PhD students in areas like CRISPR licensing , antimicrobial resistance , and pharmaceutical regulation . His work appears in media outlets such as The Economist , Politico , and The Lancet Digital Health .

• Networking
• Machine Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

S. Boumerdassi is a researcher at the CEDRIC Laboratory of Conservatoire National des Arts et Métiers (CNAM) . He has been actively involved in interdisciplinary research spanning networking , machine learning , image encryption , and IoT systems since the early 2000s. His work has focused on security protocols, energy efficiency in data centers, and anomaly detection frameworks. Key Research Areas: Networking: VANETs, LoRaWAN, network anomalies, and edge computing. Security: Blockchain oracles, cryptographic protocols, and wireless sensor network security. Machine Learning: Applications in network optimization and sustainable agriculture. Image Encryption: Chaotic maps, DCT coefficients, and grain algorithms. Recent Publications highlight his contributions to federated learning, anomaly detection, and mobility models. Leadership and Collaboration He has co-organized conferences like Mobile, Secure, and Programmable Networking and contributed to edited volumes on machine learning and IoT. His collaborations include researchers from Springer, IEEE, and institutions across France, Italy, Spain, and Canada.

Hai Dongمشاهده پروفایل

مدرس ارشد

• Service-Oriented Computing
• Edge Intelligence
• Blockchain
• +۴ مورد دیگر

Dr. Hai Dong is a Senior Lecturer at the School of Computing Technologies, RMIT University in Melbourne, Australia, with promotion to Associate Professor scheduled for 2026. He serves as the Founding Director of the CloudTech-RMIT Green Cryptocurrency Joint Research Laboratory (GreenCryptoLab) and Leader of the Smart Sensing and Services Research Area. Previously, he held research fellow positions at both RMIT University and Curtin University. Dr. Dong is a Senior Member of IEEE and chairs the IEEE Computational Intelligence Society Task Force on Deep Edge Intelligence. Dr. Dong's research spans several cutting-edge domains including Service-Oriented Computing, Edge Intelligence, Blockchain, AI Security, Cyber Security, and Machine Learning. His work bridges theoretical foundations with practical applications, particularly in secure and efficient computing systems. He has developed innovative approaches for smart contract security, edge computing optimization, federated learning, and blockchain applications with a strong focus on sustainability and real-world impact. His publication record demonstrates consistent high-impact contributions across top venues including AAAI, ASE, ICML, TSE, and TSC. Dr. Dong's research shows a clear trajectory toward increasingly sophisticated integration of AI with edge computing and blockchain systems, with growing emphasis on security, privacy, and resource efficiency. Recent work highlights his leadership in addressing emerging challenges in LLM-generated smart contracts and secure federated learning systems. Best Research Paper Award at ICSOC 2016 Best Paper Award at IEEE ICBC 2025 2023 RMIT Award for Research Engagement and Impact - Industry Engagement in Graduate Research Dr. Dong has successfully supervised numerous PhD and Master's students to completion, with many going on to prestigious positions. He has secured over $5 million in research funding as Chief Investigator from sources including ARC, CRC, QNRF, and industry partners like ANZ, CloudTech, and Telstra. His GreenCryptoLab research facility represents a significant industry-academic partnership focused on sustainable blockchain technologies. Dr. Dong maintains active collaborations with researchers worldwide and serves on committees for over 100 international conferences.

Linga Reddy Cenkeramaddiمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Autonomous Systems
• Robotics and Automation
• +۷ مورد دیگر

Linga Reddy Cenkeramaddi is a **Professor** in the Department of Information and Communication Technology at the University of Agder (UiA), Norway. He holds a PhD in Electrical Engineering from NTNU (Trondheim, Norway) and Master’s degrees from IIT Delhi and IIT Chennai. His research focuses on Cyber-Physical Systems (CPS), Autonomous Systems, Robotics, AI/ML, Wireless Sensor Networks, and 5G/6G-enabled technologies. He leads projects such as LUCAT (UAV communication tracking) and INCAPS (Indo-Norwegian collaboration in CPS). **Education**: PhD (NTNU), MSc in Physics (IIT Chennai), MSc in Integrated Electronics (IIT Delhi). **Research Interests**: Energy-efficient wireless communication, autonomous vehicles, edge intelligence, mmWave sensing, and smart cities. **Teaching**: Courses include embedded systems, mmWave sensing, and wireless prototyping. **Key Contributions**: Developed low-power embedded systems, contributed to UAV communication protocols, and pioneered AI-driven healthcare solutions. **Grants**: Co-PI in the INMOST offshore mechatronics project. **Labs/Teams**: Autonomous and Cyber-Physical Systems (ACPS), Electronics and IoT (E&IOT), Intelligent Mechatronics (iTron). His work bridges academia and industry, impacting healthcare, smart agriculture, and environmental monitoring. Future research includes multi-modal sensing, multi-agent systems, and 6G communication integration with AI.

• Computer Science
• Wireless Sensor Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Muhammad Khalil Afzal is a faculty member at COMSATS University Islamabad within the Department of Computer Science . His career focuses on Internet of Things (IoT) , Wireless Sensor Networks , and Machine Learning applications in network optimization. Key collaborations with researchers like Byung-Seo Kim and Sung Won Kim Published extensively in IEEE Access , Sensors , and Future Generation Computer Systems Research spans data-driven intelligence , QoS in IoT , and smart transportation systems : Developed graph convolutional GRU frameworks for traffic prediction Explored NOMA for low-latency communications in 5G Innovated blockchain-based privacy preservation in vehicular networks