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Frank KarglView profile

Professor

• Network Security
• Vehicular Ad-Hoc Networks
• Embedded Systems Security
• +5 more

Prof. Frank Kargl is a full professor and director of the Institute for Distributed Systems at the University of Ulm. He holds a PhD (2003) and habilitation (2009) from Ulm University. Previously, he was an Associate Professor at the University of Twente (Netherlands, 2009–2012) and later an adjunct professor. He has served as vice dean (2013–2016) and dean (2016–2018) of the Faculty of Engineering, Computer Science, and Psychology. His research focuses on network security, vehicular ad-hoc networks, embedded systems, and blockchain privacy. He is a member of ACM, IEEE, and GI. Research interests include mobile/self-organizing networks (e.g., VANETs), security/privacy in IT systems, secure cloud computing, and crypto-currencies. Key projects intersect vehicular networks with privacy/security, embedded system security, and distributed computing frameworks. Publications span network security, zero-trust architectures, automotive cybersecurity, and privacy-preserving systems. Notable contributions include VeReMi datasets for VANET misbehavior detection and frameworks like ZTSFC for secure service chaining. Prof. Kargl chairs the GUC committee at Ulm University and serves on the KIZ committee. His work bridges academic research with real-world applications in automotive systems, IoT, and decentralized technologies.

• Federated Learning
• Machine Learning
• Cybersecurity
• +3 more

Yan Xiao is an Adjunct Professor at the University of Illinois. His research focuses on federated learning, cybersecurity, and distributed systems, with an emphasis on adversarial robustness, privacy-preserving techniques, and optimization in machine learning frameworks. He has contributed to foundational tools like FedML and explores challenges in federated learning security, energy efficiency, and causal mechanisms. Key research interests include federated learning architectures, adversarial defense mechanisms, and the integration of reinforcement learning for automated feature selection. His work spans theoretical advancements (e.g., invariant aggregators for backdoor attacks) and practical applications (e.g., thermal management in HPC systems). Recent publications highlight trends in federated learning security, distributed optimization, and causal discovery. Notable contributions address straggler-free coreset-based learning and Bayesian-optimized energy efficiency in federated setups. No scientific awards or grants are explicitly mentioned in the provided texts. Advising details are not listed, though collaborations with research groups in federated learning and HPC are implied through publication topics.

• Computer Security
• Hardware Security
• Programming Languages
• +3 more

Klaus Freiherr von Gleissenthal is an Assistant Professor at the Faculty of Science and the Network Institute at Vrije Universiteit Amsterdam. His research focuses on computer security, hardware security, and programming languages. He teaches courses on compiler construction and verification for security. His work addresses challenges in embedded systems, microcontroller-based devices, and open-source processor verification. Collaborations span international research networks, emphasizing practical applications in cybersecurity and hardware-software co-design. Research Interests: Hardware-Triggered Security Mechanisms Type Systems and Static Analysis Side-channel Attack Mitigation Compiler Optimization Impacts on Security His recent publications explore topics such as data integrity in microcontrollers, refinement type systems, and leakage contracts for open-source processors. He actively contributes to conferences like CCS and PLAS, fostering interdisciplinary approaches to security challenges.

• Cybersecurity
• Data Protection
• Artificial Intelligence
• +4 more

Dr. Chaminda Hewage serves as Interim Associate Dean for Research and Reader (Associate Professor) in the Cardiff School of Technologies at Cardiff Metropolitan University. He leads the Computer Security Programme and holds a Ph.D. in Multimedia Communications from the University of Surrey (2009) and a B.Sc. in Electrical and Information Engineering from the University of Ruhuna (Sri Lanka). His research focuses on cybersecurity, data protection in AI/ML systems, blockchain applications, and multimedia communication security. He has authored over 50 peer-reviewed publications and edited major books on emerging technologies' privacy challenges. Education Background: B.Sc. (Hons) Electrical & Information Engineering, University of Ruhuna (2004) Ph.D. in Multimedia Communications, University of Surrey (2009) Postgraduate Certificate in HE Teaching & Learning, Kingston University (2014) Research Interests: Data protection mechanisms in AI/ML Cybersecurity frameworks for Industry 4.0 Blockchain for public and private sector applications Metaverse privacy and security Edge AI and embedded system security Regulatory compliance in digital transformation Recent Contributions: Co-edited the 2024 Springer volume Data Protection: The Wake of AI and Machine Learning , published influential works on Kubernetes security, V2X communication cryptography, and metaverse privacy. Active in international conferences including C3AI and ICCS. Awards & Recognition: Gold Medal for Academic Excellence (2004), University of Ruhuna ORS Scholarship (2005), HEFCE Fellow of the Higher Education Academy Grants & Collaborations: Leads research projects on cybersecurity for SMEs, smart city infrastructure, and digital transformation. Engages with industry partners through Cardiff Met's innovation networks. Advises on UK government cyber policy initiatives. Labs/Teams: Oversees the Cybersecurity Research Group and collaborates with the ZERO2FIVE Food Industry Centre for IoT applications. Active in multidisciplinary teams addressing Industry 5.0 challenges.

• Computer Architecture
• Security
• Systems and Networking
• +3 more

Alex Richardson is a Lecturer in the Department of Computer Science and Technology at the University of Cambridge . His research focuses on Computer Architecture , Security , and Programming Languages , with emphasis on memory safety, capability-based systems, and software verification. Richardson's research spans critical areas in systems security, including pointer provenance, temporal and spatial safety for CHERI heaps, and capability enforcement for embedded devices. His work integrates formal methods with practical implementations to address vulnerabilities in software and hardware architectures. Richardson has contributed to advancements in Capability-Based Security , Embedded Systems , and Memory Management through collaborations on projects like CHERI and SOAAP. His publications highlight innovations in heap management, access control, and real-time operating systems for cyber-physical systems. He is associated with the Computer Architecture Group and maintains active involvement in improving secure system design. His contact details include the email alr48@cam.ac.uk and the office at GE15, William Gates Building , Cambridge.

Hisham DaoudView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)
• +3 more

Hisham Daoud is an Assistant Professor in the Department of Computer Science at the School of Computer and Cyber Sciences, Augusta University. He holds an MS and PhD in Computer Engineering from the University of Louisiana at Lafayette (2018). His research focuses on cybersecurity in IoT systems, machine learning applications for healthcare diagnostics, and deep learning techniques for real-time predictive systems. Recent work includes enhancing EV charger resilience via reinforcement learning and developing seizure prediction frameworks using neural networks. Teaching responsibilities include courses such as Principles of Scripting, Undergraduate Research, and Introduction to Cybersecurity Engineering. His scholarship emphasizes interdisciplinary approaches, spanning cyber-physical systems security, biomedical signal processing, and resilient infrastructure design. Publications from 2020-2023 reflect a strong emphasis on applying AI to healthcare challenges and securing emerging technologies. No scientific awards are explicitly listed in the provided materials. Research activities include collaborative projects on IoT-enabled healthcare systems and sustainable energy infrastructure solutions.

• Formal Languages
• Programming Language Semantics
• Cybersecurity
• +5 more

William Steingartner is an Associate Professor at the Faculty of Electrical Engineering and Informatics, Technical University of Košice (TUKE). He specializes in formal languages, programming language semantics, cybersecurity, and computer science education. His research focuses on advancing pedagogical tools for teaching formal semantics, compiler design, and secure software development. He actively contributes to academic governance, serving as Principal Investigator on projects like 'Application of new principles in the education of IT specialists' and participating in 'Evolving Architectural Knowledge in the Edge-to-Cloud Continuum.' Dr. Steingartner holds editorial roles, including guest editor for the Central European Journal of Computer Science (since 2011) and editor of proceedings for conferences like INFORMATICS 2019. He has organized major events such as the International Scientific Conference Informatics in 2013, 2015, 2017, and 2019, demonstrating leadership in academic event management. His teaching spans formal languages, logic for computer scientists, and domain-specific language design. Research highlights include developing tools like OnlineProver for formal logic education and RISCAL for semantics visualization. He explores cybersecurity challenges in hybrid threats, API security, and cyber resilience strategies, alongside innovations in VR applications for therapy and education. Key contributions include over 50 publications since 2015, with recent work addressing disinformation campaigns, smart orthosis design, and pedagogical software for programming semantics. He emphasizes interdisciplinary collaboration, bridging theoretical computer science with practical applications in medicine, robotics, and user experience design.

Marie LangerView profile

Researcher

• Human-AI Collaboration
• Socio-technical Systems
• Digital Transformation
• +3 more

Marie Langer is a Researcher at the Chair of Information Systems, esp. AI Engineering in Companies at Otto-Friedrich-University of Bamberg. She holds a Bachelor's in Business Administration (University of Bremen, 2020) and a Master's in Economics (Technical University of Dortmund, 2022), focusing on socio-technical systems and algorithm aversion. Since 2021, she was a research assistant at Paderborn University’s Junior Professorship of Digital Society. Her research explores human-AI collaboration in organizational contexts, hybrid team dynamics, and AI acceptance. She contributes to teaching in Hybrid Intelligence and supervises theses. She developed digital teaching materials for sustainability and innovation through the INNO4S project. Key projects include PREVENT, CYLENCE, and ARTIMIS. Research interests emphasize socio-technical conditions for effective human-AI collaboration, trust-building in AI systems, and mitigating algorithm aversion. Teaching focuses on practical projects like Human-AI Collaboration and Digital Innovations. Projects like INNO4S highlight her commitment to embedding sustainability and digitalization in business education.

Levent ErtaulView profile

Professor

• Cyber Security & Privacy
• Cyber War
• Hacking Technologies
• +9 more

Levent Ertaul is a Professor and Chair of the Department of Computer Science at California State University, East Bay (CSUEB). He holds a PhD in Computer Networks Security from the University of Sussex, UK, and advanced degrees in Electrical and Electronics Engineering from Hacettepe University, Turkey. His expertise spans Cyber Security & Privacy, Cyber War, Cryptology, and Wireless Security. He has led research in IoT Security, Post-Quantum Cryptography, and Enterprise Security Planning. Ertaul has held leadership roles including CSUEB Department Chair and served on committees such as the California Cybersecurity 4 Year Education Program Workgroup. His professional development includes summer faculty positions at Lawrence Livermore National Laboratory (LLNL) focusing on CyberDefenders and Global Security programs. He has authored over 50 publications and presented globally at venues like RSA Conference and IEEE events. Awards include the Cisco Instructor Excellence Award and Keynote Speaker recognitions. He teaches advanced courses in Cyber Security, Cryptology, and Network Security Management. Notable contributions include inventing the L4NE protocol for location-based services and the ERTAUL Crypto Algorithm. His grants exceed $15M, including work on National Military Messaging Systems and Firewall Development. He actively participates in cybersecurity competitions (HackCSUEB, CTFs) and advises on policy matters through roles like Bay Area Council Cyber Security Representative.

Anna KoufakouView profile

Professor

• Natural Language Processing
• Machine Learning
• Text-Based Emotion Recognition
• +5 more

Dr. Anna Koufakou is a Professor in the Department of Computing & Software Engineering at Florida Gulf Coast University (FGCU), affiliated with the U.A. Whitaker College of Engineering. She joined FGCU in 2009 as an Assistant Professor of Computer Science and has since advanced to full Professor. Her research focuses on Natural Language Processing (NLP), Machine Learning, text-based emotion recognition, and abusive language detection. She has also contributed to interdisciplinary projects involving solar panel data modeling and law enforcement data analysis. Education: Dr. Koufakou holds a Ph.D. in Computer Engineering from the University of Central Florida (2009), an M.S. in Computer Engineering (2000), and a B.S. in Informatics/Computer Science from Athens University of Economics and Business (1997). Research Interests: Natural Language Processing (NLP) Machine Learning and Deep Learning Emotion Recognition in Text Offensive/Abusive Language Detection Data Mining and Anomaly Detection Cloud Computing Education Her work spans diverse applications, from legal data analysis to cybersecurity. Notable contributions include the development of lexicon-enhanced models like HurtBERT for detecting abusive language. She has been recognized with a faculty research fellowship at Oak Ridge National Lab (ORNL) and awards from CRA-W for advancing women in computing research. Teaching: She has designed and taught courses such as cloud computing and software engineering, emphasizing hands-on learning. Her educational initiatives include certification-guided cloud computing curricula and virtual reality-based programming education. Labs/Teams: While no specific lab names are mentioned, her research collaborations involve interdisciplinary teams addressing NLP and data-driven challenges.

Norbert JungView profile

Professor

• Cybersecurity
• Embedded Systems
• Biometrics
• +4 more

Prof. Dr. Norbert Jung is a Professor of Secure Embedded Systems at the Bonn-Rhein-Sieg University of Applied Sciences (Department of Computer Science), where he also served as Founding Director of the Institute for Security Research (2011–2024). He is a core member of the NRW Promotionskolleg for Applied Research and leads the Biometrics Evaluation Center (BEZ) at the university’s Sankt Augustin campus. His expertise spans cybersecurity, embedded systems, and optoelectronic sensor technology, with a focus on biometric security, real-time signal processing, and safety-critical machine systems. Prof. Jung holds a Diplom-Ingenieur in Communications Engineering from RWTH Aachen University, where he also earned his Dr.-Ing. in optoelectronics and signal processing. His career includes R&D roles at Philips Electronics in medical imaging systems. Since 1999, he has led numerous national and industrial projects, including Biolab II (biometric system evaluation), ROSE3D (robust 3D camera systems), and SMOOTH (safety switches for industrial machinery). His work addresses challenges in anti-spoofing biometrics, industrial robot safety, and sensor-based material classification. Key research themes include: Biometric system resilience against presentation attacks Safety-critical embedded systems for manufacturing Near-infrared (NIR) and multispectral imaging for skin detection Optoelectronic sensor innovation for industrial and medical applications He has spearheaded projects such as the BEZ center, which evaluates biometric systems for government and industry. His lab infrastructure includes optoelectronics and laser labs (H008/H010) and collaborates with the German Federal Office for Information Security (BSI). Prof. Jung is also the laser safety officer for his department.

Suk Jin LeeView profile

Associate Professor

• Internet of Things
• Network Protocols
• Computer Vision
• +3 more

Suk Jin Lee is an Associate Professor of Information Technology at James Madison University's College of Integrated Science & Engineering. His expertise spans IoT, network protocols, computer vision, machine learning, signal processing, and target tracking. He teaches a wide range of undergraduate and graduate courses, including computer networks, IoT, and algorithms. Lee holds a Ph.D. in Electrical & Computer Engineering from Virginia Commonwealth University (2012), an M.E. and B.E. from Pukyong National University, South Korea. His research focuses on IoT security protocols, embedded vision systems, medical imaging, and biomedical applications. Notable projects include IoT authentication frameworks, MRI super-resolution methods, and automated insulin delivery systems. He has contributed to nanosensor networks and real-time streaming solutions using Raspberry Pi. His work integrates machine learning into medical diagnostics and tumor motion prediction. Lee's teaching innovations include hands-on learning designs for computer organization and curriculum development in IoT education. He has held academic positions at Columbus State University (2016-2023) and Texas A&M University-Texarkana (2013-2016), and conducted postdoctoral research at the University of Maryland Medical Center. His publications highlight interdisciplinary approaches, combining IoT, blockchain, and medical technologies. Current research trends emphasize energy-efficient protocols, AI-driven medical imaging, and IoT security for smart devices. Lee has advised numerous projects in senior design and independent studies, focusing on practical implementations of theoretical concepts. His lab work involves developing frameworks for IoT appliance control and embedded vision systems.

Yashar DeldjooView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Recommender Systems
• +7 more

Yashar Deldjoo is an Assistant Professor specializing in artificial intelligence, machine learning, and their applications in healthcare, user modeling, and recommender systems. His research focuses on explainable AI, ethical AI practices, multimodal learning, and brain-computer interfaces. He has contributed to projects like WeBIUM (Wearable Devices and Brain-Computer Interfaces for User Modelling) and ARIEL (emotional support via BCI and LLMs). His work bridges technical advancements with societal impacts, addressing challenges in healthcare diagnostics (e.g., Alzheimer’s disease prediction), environmental sensing (real-time pollution-aware recommendations), and cybersecurity (data poisoning attacks on recommendation systems). He also explores fairness in large language models and the integration of EEG-based emotion recognition systems. Notable contributions include frameworks like BRAINEX (CNN model evaluation for brain age prediction), Ducho (multimodal feature extraction for recommendation systems), and KGUF (knowledge-aware graph-based recommenders). He chairs conferences like RecSys and contributes to industry-relevant standards for trustworthy AI systems. His research emphasizes interdisciplinary collaboration, combining neural networks, ethical considerations, and human-computer interaction. Current projects involve neurorehabilitation tools, quantum security for smart cities, and AI-driven usability testing.

• Wireless Networks
• Cybersecurity
• IoT and Sensor Networks
• +5 more

Pierpaolo Loreti is an Adjunct Professor at the Department of Electronic Engineering and a Researcher at the University of Rome Tor Vergata. He holds a Ph.D. in Telecommunications and Microelectronics (2002) and a Degree in Electronic Engineering (1998), both from University of Rome Tor Vergata. His research focuses on wireless/mobile networks, framework design, analytic modeling, and performance evaluation through simulation and test-bedding. He has led projects on IoT, cybersecurity (e.g., medical device security, satellite networks), blockchain in healthcare, and wildlife tracking systems for endangered species like the Galápagos pink iguana. He collaborates with CNIT and coordinates initiatives in distributed computing, edge AI, and federated testbeds. Education: Ph.D. in Telecommunications and Microelectronics, University of Rome Tor Vergata, 2002 Electronic Engineering Degree (cum laude), University of Rome Tor Vergata, 1998 Research Interests: Wireless networks, IoT sensor systems, cybersecurity for healthcare and satellite infrastructure, blockchain applications in energy grids, and in-network machine learning. His work addresses challenges in privacy preservation, resource-sharing protocols, and energy-efficient designs for critical environments like wildlife monitoring and smart grids. Notable Projects: Developed miniaturized tracking systems for Galápagos pink iguanas Designed cybersecurity frameworks for medical devices and satellite networks Advanced eBPF program composition tools (HIKe/eCLAT) Co-led OpenSatRange and Cyber Range initiatives for space system training Awards: None explicitly mentioned in texts. Advising/Grants: Coordinated European/national projects since 1998 Supervised multiple research initiatives in IoT, blockchain, and network security Labs/Teams: Active member of Netgroup and collaborates with CNIT on interdisciplinary projects.

Alec F. DialloView profile

Research Fellow

• Computational Intelligence
• Adversarial Robustness
• Explainable AI
• +4 more

Alec F. Diallo is a Postdoctoral Research Associate at the University of Edinburgh's School of Informatics, affiliated with the Mobile Intelligence Lab and Institute for Computing Systems Architecture. He concurrently serves as a Machine Learning Engineer at Net AI, bridging academic research with industry applications in secure networked systems. His educational background includes a joint Computer Science and Electrical Engineering degree from École Supérieure d'Ingénieurs en Électronique et Électrotechnique (ESIEE Paris) and Mundiapolis University, followed by a PhD in Computer Science at the University of Edinburgh under Dr. Paul Patras. Dr. Diallo's research centers on computational intelligence with emphasis on adversarial robustness , explainable AI , and optimization algorithms applied to network security. His work develops novel frameworks like Amoeba for censorship circumvention and Sabre for neural network defense, addressing critical vulnerabilities in edge intelligence and secure communications through advanced machine learning techniques. His publication trajectory reveals consistent innovation in applying clustering methods and spectral analysis to cybersecurity challenges, with increasing focus on real-world deployment at network edges. Recent work demonstrates exceptional performance in malicious traffic classification (100% accuracy) and robust adversarial defense across diverse domains including image recognition and network intrusion detection. Notable recognitions include: SICSA 2024 Best Dissertation Award for "Active Intelligence for Secure Systems" Brendan Murphy Memorial Prize at the 33rd Next Generation Networking workshop (2021) He actively contributes to the academic community as reviewer for AISTATS and IEEE journals, Program Committee Member for IEEE S&P 2024, and frequent invited speaker at institutions including Cambridge, Imperial College London, and Glasgow. His research is supported by Arm funding and features strong industry collaboration through Net AI. As core member of Edinburgh's Mobile Intelligence Lab, he advances edge computing security through projects spanning network traffic analysis, censorship resistance, and robust neural architectures, with ongoing work focusing on scalable clustering validation and causal explainability frameworks.