جست‌وجوی استاد و پژوهشگر

Genya Ishigakiمشاهده پروفایل

استادیار

• 5G and Beyond Networking
• Network Slicing
• Reinforcement Learning
• +۵ مورد دیگر

Genya Ishigaki is an Assistant Professor in the Department of Computer Science at San José State University's College of Science. His research focuses on network slicing, combinatorial optimization, and reinforcement learning, addressing resource allocation challenges in next-generation telecommunications networks. Ph.D. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Computer Science, The University of Texas at Dallas, 2021 M.S. in Engineering, Soka University, Japan, 2016 B.S. in Engineering, Soka University, Japan, 2014 Dr. Ishigaki's work explores adaptive network control through machine learning and combinatorial optimization, including elastic network slices , explainable AI , and federated learning . His research addresses critical tradeoffs in resource utilization versus capacity reservation for future demands. Recent publications demonstrate his focus on network automation (2025), information diffusion (2025), federated learning platforms (2024), and DDoS attack detection (2024). Articles span network security , AI-driven optimization , and social network dynamics . NSF Student Travel Grant (2019) Shigeta Education Foundation Ph.D. Scholarship (2019-2021) Outstanding TA Award (2019) JASSO Ph.D. Scholarship (2016-2019) NEC C&C Foundation Travel Grant (2015) He leads the Interconnect Lab, which investigates accountability in autonomous network operations and edge computing-oriented federated learning. His grants include SJSU's RSCA Seed Grant (2022-2023) and University Grant Academy Award (2022).

• Quantum Safe Cryptography
• Threat Management
• Artificial Intelligence Security
• +۵ مورد دیگر

Dr. Marc Ph. Stoecklin is a Principal Research Staff Member and head of the Security Research Department at IBM Research Europe in Zurich, Switzerland. He co-leads IBM's global research strategy on Quantum Safe Cryptography and Migration, focusing on cryptographic artifact identification and migration implementation. Previously, he directed Threat Management research, applying AI and automation to threat detection, investigation, and response. Education: PhD in Computer, Communication and Information Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland His research spans quantum safe cryptography, threat management, AI security, threat detection, identity management, cyber deception, big data analytics, and security visualization. He pioneered AI-powered security operations, contributing to Watson for Cyber Security and QRadar Advisor with Watson, and analyzes AI/quantum computing misuse in cyber attacks. His 2016-2020 publications emphasize advanced threat detection, malware analysis, and AI security, featuring evasive malware deactivation, threat intelligence computing, and neural network watermarking. These works bridge research and product development, directly influencing IBM security offerings. Dr. Stoecklin led IBM's Cognitive Cyber Security Intelligence group (2014-2019) and the Security Research Department since 2019. As tech lead for IBM's COVID-19 task force (2020-2022), he developed IBM Digital Health Pass, deployed in New York's Excelsior Pass with over 1 million passes issued in two months. He heads the Security Research Department at IBM Research Europe in Zurich and has been integral to the Global Security Analysis Lab (GSAL) and Cognitive Cyber Security Intelligence (CCSI) group at IBM T.J. Watson Research Center.

• Machine Learning
• Sentiment Analysis
• Online Learning
• +۹ مورد دیگر

Aamir Anwar is a researcher affiliated with the University of West London , focusing on interdisciplinary applications of artificial intelligence, machine learning, and human-computer interaction. His work bridges technology with education, healthcare, and cybersecurity, as evidenced by his publications on topics like emotion-aware online learning , malware detection in IoT devices , and smart systems for people with disabilities . Research Interests : Machine Learning, Sentiment Analysis, Online Learning, EEG Signal Processing, Smart Systems, Healthcare Informatics. Key Collaborations : Co-authored studies with researchers in cybersecurity, nursing education, and neuromarketing. Publication Trends : Recent articles span 2021–2024, emphasizing AI in education (emotion detection), deep learning for cybersecurity , and health-focused technologies (frailty assessment, seizure prediction).

• Cybersecurity
• Data Privacy
• Machine Learning
• +۳ مورد دیگر

Dr. Dima Alhadidi is an Associate Professor in the School of Computer Science at the University of Windsor. His research focuses on Cybersecurity, Data Privacy, Machine Learning, and their applications in Health Informatics, Cloud Computing, and Smart Grids. He holds a PhD in Computer Science and Software Engineering from Concordia University (2010). His research interests include secure federated learning frameworks, privacy-preserving techniques for genomic and health data, and adversarial machine learning defenses. Notable contributions include Trustformer (2025), secure aggregation methods in federated learning, and hybrid malware classification using deep learning. Recent work emphasizes mitigating membership inference attacks and developing privacy-preserving analytics for distributed systems. Dr. Alhadidi actively advises graduate students on topics like social network clustering (NICASN 2022) and federated learning security. No scientific awards are explicitly listed. His research spans theoretical frameworks (e.g., λ_AOP calculus) to applied systems in smart grids and healthcare informatics.

Jagath Samarabanduمشاهده پروفایل

استاد

• Artificial Intelligence
• Explainable AI
• Machine Learning
• +۵ مورد دیگر

Jagath Samarabandu is a Professor in the Department of Electrical and Computer Engineering at Western University. He holds a Ph.D. and M.S. in Electrical Engineering from SUNY Buffalo, and a B.Sc. in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka. His academic career spans since joining Western University in 2000, with prior post-doctoral experience at SUNY Buffalo and industry work at Life Imaging Systems Inc. Education: Ph.D. Electrical Engineering, SUNY Buffalo M.S. Electrical Engineering, SUNY Buffalo B.Sc (Eng) Electronics and Telecommunication, University of Moratuwa His research focuses on Artificial Intelligence, Machine Learning, Image Analysis, and Cyber Security , with applications in biomedical imaging, network intrusion detection, and civil infrastructure monitoring. He has supervised numerous graduate students working on topics ranging from chromosome analysis to smart grid security. Recent publications highlight his work in medical AI applications (auditory processing disorder diagnosis), industrial time-series analysis (using contrastive predictive coding), and network security frameworks (INSecS system development). He has contributed to 3D ultrasound segmentation, prostate motion compensation algorithms, and synthetic aperture radar systems. Key projects include NSERC-funded intelligent home monitoring systems for elderly care and low-cost synthetic aperture radar development for search-and-rescue applications.

Lars Dittmannمشاهده پروفایل

استاد

• Software-Defined Networking (SDN)
• Network Function Virtualization (NFV)
• 5G Networks
• +۶ مورد دیگر

Lars Dittmann is a Professor at the Department of Electrical and Photonics Engineering at the Technical University of Denmark (DTU), leading the Networks Technology and Service Platforms section. His work bridges advanced networking technologies with real-world applications in healthcare and transportation. Academic Role: Professor, Head of Section University: Technical University of Denmark (DTU) Department: Networks Technology and Service Platforms Research Interests: Professor Dittmann specializes in Software-Defined Networking (SDN) , 5G and IoT technologies , and energy-efficient network design , with a focus on applications in telemedicine and transportation systems . His work integrates machine learning for privacy-preserving traffic analysis and explores optical networks for high-bandwidth scenarios. Scientific Contributions: His recent publications emphasize green cellular networks using SDN/NFV/C-RAN, IoT benchmarking for coverage and mobility, and secure edge architectures for railways. Collaborative projects like the Future Patient telerehabilitation program highlight his interdisciplinary impact. Supervision: He supervises PhD candidates such as Radheshyam Singh, focusing on SDN-based IoT security and 5G network optimization. Labs & Projects: Leads initiatives like EXplorative network PLAnnINg and Broadband Trial Integration , addressing challenges in network reliability , emergency communication , and optical data center scaling .

John Clarkمشاهده پروفایل

استاد

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

John Clark is a Professor of Computer and Information Security at the University of Sheffield since 2017 and Director of the Siemens Digital MINE. Previously, he held roles as Professor of Critical Systems at the University of York (1992–2017) and worked at Logica in security R&D. He studied Mathematics and Applied Statistics at the University of Oxford. His research focuses on cybersecurity, software engineering, and AI applications, particularly in threat modeling, intrusion detection, quantum cryptanalysis, and secure autonomous systems. Clark leads the Security of Advanced Systems research group and has secured grants totaling over £36 million. Notable projects include the EPSRC-funded DAASE (2012–2019) and the Active Building Centre (2018–2022). His work on phishing detection (e.g., analyzing user behavior) and malware analysis has been widely recognized. He has been awarded the Royal Society Wolfson Merit Award (2013), GEECO medals (2005, 2013), and multiple best-paper prizes. Clark’s research spans theoretical and applied domains, including evolutionary computation for cryptanalysis, robotic system security, and smart grid protection. His labs explore areas like digital twin authentication and privacy-aware energy theft detection. He has supervised numerous grants and maintains active collaborations with industry and academia.

• Network and Service Management
• Cybersecurity
• Distributed Systems
• +۵ مورد دیگر

Aiko Pras is a Professor at the University of Twente in Enschede, Netherlands. His research spans network and service management, cybersecurity, and distributed systems, with a focus on DNS security, DDoS mitigation, IPv6, and software-defined networking. Research Interests: Network and Service Management Cybersecurity and DDoS Mitigation DNS Security and IPv6 Software-Defined Networking (SDN) Internet Measurement and Performance Critical Infrastructure Protection (ICS/SCADA) Article Trends: Pras's recent work emphasizes collaborative DDoS defense, DNS security, and IPv6 vulnerabilities. His studies often involve large-scale measurements and propose practical solutions for internet resilience and security. Collaborations: He frequently collaborates with Anna Sperotto, Roland van Rijswijk-Deij, and other experts in network security and measurement.

• Cyber Defence
• Reverse Engineering
• Malware Analysis
• +۵ مورد دیگر

Geir Olav Dyrkolbotn is an Associate Professor at NTNU's Center for Cyber and Information Security (CCIS) and a Major in the Norwegian Armed Forces, serving at the Norwegian Defence Cyber Academy (NDCA). He leads the NTNU Malware Lab and the cyber defence research group at CCIS. He holds a PhD in Information Security from Gjøvik University College and a MSc in Computer Science from NTNU. With over 25 years in the military, his work focuses on tactical communication systems, defensive cyber operations, and operational security. His research emphasizes cyber defence, reverse engineering, malware analysis, side-channel attacks, and machine learning applications. Education: PhD in Information Security, Gjøvik University College (HiG) MSc in Computer Science, NTNU Research Interests: Geir Olav's work bridges theoretical cybersecurity research and practical military applications. He explores innovative methods for hardware reverse engineering, malware detection/classification using low-level features, and forensic acquisition techniques. His contributions include analyzing USB power delivery vulnerabilities, NTFS cluster allocation behavior, and secure chip exploitation for digital forensics. Teaching: Courses include IIKG6500/IMT4213 Cyber-taktikk, IMT4214 Cyber-etterretning, IIKG6501 Cyber Intelligence, and IMT4116 Malware Analysis & Reversing. Labs & Teams: Heads the NTNU Malware Lab and leads the cyber defence research group at CCIS, collaborating on projects like the Digital Forensic Acquisition Kill Chain and hardware security vulnerability assessments.

Marcus Botacinمشاهده پروفایل

استادیار

• Malware Analysis
• Hardware-Assisted Security
• Antivirus Technology
• +۴ مورد دیگر

Marcus Botacin is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU), USA. Previously, he held positions as Visiting Assistant Professor (2022-2024) and Lecturer at the Federal University of Paraná (Brazil). His research focuses on malware analysis, hardware-assisted security, and antivirus technology. He earned his Ph.D. in Computer Science from UFPR (2021), M.Sc. from UNICAMP (2017), and B.Sc. in Computer Engineering from UNICAMP (2015). Education: Ph.D., Computer Science, Federal University of Paraná, Brazil (2021) M.Sc., Computer Science, University of Campinas, Brazil (2017) B.Sc., Computer Engineering, University of Campinas, Brazil (2015) Research Interests: Botacin's work addresses challenges in malware detection (static/dynamic methods, sandboxing), hardware-enhanced security mechanisms (e.g., branch monitoring), and antivirus internals. He emphasizes practical solutions and ethical considerations in cybersecurity, advocating for culturally aware threat models (e.g., Brazilian financial malware studies). Publications & Trends: His recent work explores adversarial ML attacks on malware detectors, HPC-based detection frameworks, and automated malware generation risks. He frequently publishes in top venues like ACM CCS, USENIX Security, and IEEE TDSC. Awards: Top-3 Best Ph.D. Thesis in Computer Security (Brazilian Computer Society, 2022) Best PhD Thesis Award from UFPR (2022) MLSec Evasion Challenge 1st place (2021/2020) Advising & Grants: Supervises 15+ students at TAMU and leads NSF-funded projects (e.g., $523K grant for HPC malware detection frameworks). Serves on 21+ conference program committees and reviews for over 50 journals. Lab & Projects: Develops Corvus (public malware analysis sandbox) and explores hardware-accelerated AV solutions like Terminator and HEAVEN.

• Networking
• Cybersecurity
• Machine Learning

Dr. Ahmad Azab is a Lecturer at the University of Sydney’s School of Computer Science, specializing in Networking, Cybersecurity, and Machine Learning. He holds a PhD and industrial certifications including CISSP, CCSP, and CCNA. His teaching spans networking, cybersecurity, ethical hacking, and machine learning applications. Research interests focus on network traffic classification, IoT integration in cognitive radio networks, malware analysis, and cybersecurity countermeasures. His work bridges academic research with industrial applications, emphasizing practical solutions for emerging threats. Publications highlight innovations in botnet detection, malware classification, and cybersecurity frameworks. Supervision of bachelor and master’s projects underscores his commitment to mentoring future technologists. Professional affiliations include IEEE and ISC².

• Cybersecurity
• Computer Security
• Information Security
• +۶ مورد دیگر

Matthew Allen Bishop is a Professor in the Department of Computer Science at the University of California, Davis. His primary affiliation is with the College of Engineering. Bishop's research focuses on cybersecurity, including secure programming, insider threat detection, malware analysis, and cybersecurity education. He has contributed extensively to curricular guidelines (e.g., CSEC 2017) and frameworks for cyber defense. His work spans theoretical advancements (e.g., intrusion detection models) and applied systems (e.g., secure voting platforms). Notable research areas include: Cybersecurity Education: Developing curricula and pedagogical frameworks for secure coding and ethical practices. Insider Threat Mitigation: Declarative approaches and behavioral analysis for detecting and preventing attacks. Malware Mitigation: Techniques leveraging uncertainty principles and defensive programming. Election Security: Analyzing vulnerabilities and designing secure voting systems. Bishop has collaborated with institutions like the Department of Homeland Security (DHS) and National Security Agency (NSA) on critical infrastructure protection. His publications span conferences like IEEE Security & Privacy, HICSS, and NSPW, emphasizing real-world applications of cybersecurity principles.

JIANG Lingxiaoمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Professor Lingxiao Jiang is a full-time faculty member at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU) , where he serves as Director of the Centre for Research on Intelligent Software Engineering (RISE) . His research and teaching focus on software engineering , program analysis , code search & reuse , and deep learning of code to enhance software quality, development productivity, and security. PhD, University of California, Davis (2009) Professor Jiang's research explores context-aware software deep learning , code clone detection , and security & privacy through tools like DECKARD (code clone detection), EqMiner (functionally equivalent code), and SmartEmbed (smart contract security). His work leverages distributed computing , symbolic execution , and neural program models for large-scale code analysis. Recent publications highlight trends in deep learning-based code analysis , smart contract vulnerabilities , and automated program transformation . Key themes include code clone detection , semantic patches , and cross-language API mappings . Scientific Awards ACM SIGSOFT Impact Paper Award (2018) for scalable code clone detection As advisor, Professor Jiang has mentored 14 graduate students, including Lucia (Ph.D. 2014) , Shaowei Wang (Ph.D. 2015) , and current advisees in intelligent software engineering. He leads multiple grants on code mining , security of digital platforms , and AI systems governance . RISE Lab at SMU develops tools like MANDO (smart contract analysis), iTiger (issue title generation), and TreeCaps (code processing with capsule networks). The lab actively recruits researchers in software engineering and AI.

• Software Security
• IoT Security
• Software Supply Chains
• +۲ مورد دیگر

Mojtaba Moazen is a PhD student and researcher at the Division of Theoretical Computer Science , part of KTH Royal Institute of Technology in Sweden. He is actively involved in the WASP – Wallenberg AI, Autonomous Systems and Software Program and the NEST CyberSecIT project, focusing on securing IoT applications and software supply chains. His work bridges academic research with critical real-world cybersecurity challenges. Education: PhD (ongoing) in Theoretical Computer Science, KTH Royal Institute of Technology Master of Science in Information Technology, Sharif University of Technology Bachelor of Science in Computer Engineering, K. N. Toosi University of Technology Research Interests: Mojtaba specializes in software security , IoT security , and software supply chains , with a focus on developing robust security mechanisms for emerging technologies. His recent publications highlight advancements in Android malware detection and continuous integration testing. Courses Assisted: Applied Cryptography (DD2520) Computer Security (DD2395) Internet Programming (DD1386) Language-Based Security (DD2525) Programming Techniques (DD1310)

Olga Kurasovaمشاهده پروفایل

استاد

• Machine Learning
• Data Visualization
• Cybersecurity
• +۳ مورد دیگر

Dr. Olga Kurasova is a Professor and Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies, where she leads research in the Cognitive Computing Group. Her work focuses on developing advanced computational methods for real-world applications. Her primary research explores machine learning paradigms including deep learning for cybersecurity (keystroke dynamics, adversarial attacks), medical image analysis (pancreatic cancer detection), industrial monitoring, and explainable AI. She maintains strong collaborations across disciplines, particularly in healthcare and security domains. Analysis of her recent publications (2023-2025) reveals three dominant themes: (1) Advanced biometric authentication systems using behavioral analysis and deep learning, (2) Medical AI applications focusing on pancreatic cancer detection through CT image analysis, and (3) Theoretical advancements in explainable AI methodologies for high-stakes domains. Significant scientific recognition includes: 2021 Lithuanian Science Prize for the cycle 'From Data Science to Artificial Intelligence Technologies' 2024 Vilnius University Rector's Science Prize She has led multiple national research projects, including a 2024-2027 LMT-funded initiative on 'adversarial machine learning for cybersecurity' and coordinated interdisciplinary teams for projects on cognitive computing capabilities and optimal data mining solutions. She directs research within the Cognitive Computing Group, focusing on developing intelligent systems for data analysis, visualization, and decision support across healthcare, cybersecurity, and industrial applications.