Find Academic Supervisors

Katie Paxton-FearView profile

Lecturer

• Cyber Security
• Data Science
• Artificial Intelligence (AI)/Machine Learning (ML)
• +4 more

Dr. Katie Paxton-Fear is a Lecturer in Cyber Security at Manchester Metropolitan University, specializing in the intersection of data science, AI, and security. Her research focuses on applying data science and AI to cybersecurity challenges while addressing security concerns within AI/ML systems. She is an active web security researcher and educator, contributing free resources via YouTube. Her work includes vulnerability discovery in organizations like Verizon and the Department of Defense, and she explores ancient language decipherment as a side interest. Paxton-Fear holds a PhD in Defence and Security from Cranfield University and a BSc in Computer Science from Salford University. She teaches undergraduate and postgraduate courses on Security Fundamentals, Penetration Testing, and IoT Security. Her research interests span interdisciplinary topics such as secure development practices, API/web security for cyber-physical systems, and leveraging NLP for insider threat analysis. She advocates for collaboration between developers and security professionals, emphasizing proactive security measures. Paxton-Fear is also a media commentator featured in outlets like BBC, ZDNet, and Wall Street Journal, discussing bug bounty programs and diversity in cybersecurity. Her recent publications highlight forward-looking cybersecurity frameworks, prototyping for secure systems, and computational methods for ancient language analysis. She emphasizes data-driven approaches in security research and promotes accessibility to advanced tools like NLP for defense applications. Beyond academia, she actively engages in bug bounty hunting and maintains a YouTube channel dedicated to demystifying cybersecurity concepts for beginners.

Lawrence ChungView profile

Associate Professor

• Software Engineering
• Requirements Engineering
• Non-Functional Requirements
• +3 more

Dr. Lawrence Chung is an Associate Professor of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD in Computer Science from the University of Toronto (1993) and has been a faculty member since 1994. His research focuses on software engineering, requirements engineering, and non-functional requirements, with notable contributions to goal-oriented approaches, cloud architecture simulation, and blockchain applications in collaborative systems. Professional Preparation: PhD (1993), M.S. (1981), B.S. (1984), all from the University of Toronto. Prior to academia, he worked in software development at the University of Toronto (1984–1986) and as a Visiting Scholar at Andersen Consulting (1994–1998). Research Interests: Non-functional requirements analysis, software architecture simulation, blockchain for enterprise collaboration, and requirements engineering methodologies. Notable projects include the SilverLining cloud architecture simulator (funded by Google) and frameworks for IoT security policy modeling. Awards: Four Achievement Awards from the World Academy of Sciences (2002–2005) for contributions to computer science research. Active in NSF-funded projects, including $498,323 for Model-Driven Requirements Engineering (2006–2009). Grants and Collaborations: Partnered with Keane Inc., Alcatel, and Texas Enterprise Fund on projects related to software quality and component-based systems. His work often integrates goal-oriented methods with machine learning and big data analytics.

• Computer Security
• Operating Systems Security
• Cloud Computing Security
• +3 more

Jidong Xiao serves as a Senior Lecturer in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), where he contributes to teaching and research in system security and virtualization technologies. His academic foundation includes a PhD from the College of William and Mary completed in 2016. Education: PhD in Computer Science, College of William and Mary (2016) Dr. Xiao's research spans computer security with specialized expertise in memory safety vulnerabilities, kernel-level security mechanisms, and hardware-assisted protection. His work addresses critical challenges in virtualization security, cloud infrastructure threats, and covert channel exploitation, frequently leveraging Intel platform features for innovative security solutions. Current investigations focus on fuzzing methodologies for use-after-free vulnerabilities and CPU-level covert communications. Analysis of his 15 most recent publications (2017-2024) reveals a sustained emphasis on low-level system security, with 73% of works addressing virtualization/cloud security and 60% exploring hardware-assisted techniques. His research demonstrates consistent progression from foundational virtual machine introspection (2017-2019) toward advanced topics including SGX-based patching (2020-2023) and cutting-edge CPU clock modulation exploits (2024). Scientific Awards: No scientific awards documented in source materials Available information does not specify graduate student supervision or research grant acquisitions. His technical contributions appear primarily through publications rather than advisory roles or funded projects. Laboratory affiliations and research team structures are not referenced in the provided documentation, though his work on Intel platform security suggests potential collaboration with hardware security groups.

• Cybersecurity
• Biometrics
• Artificial Intelligence
• +4 more

Lin Li is a Lecturer in IT (Cybersecurity) at the Faculty of Science and Engineering, Southern Cross University. Previously, he served as a Research Fellow at RMIT University's Enterprise AI and Data Analytics hub. He holds a PhD in Computer Science from Swinburne University and an MSc from the University of Melbourne. His research focuses on cybersecurity, particularly physiological signal-based biometrics (e.g., PPG methods), AI-driven security analysis, software vulnerability detection, and the integration of large language models (LLMs) with cybersecurity frameworks. His work has been recognized in top-tier venues across Computer Security and Machine Learning. Teaching responsibilities include courses such as Blockchain Technology and Applications, Computer Forensics, and Programming Fundamentals. He advocates for interdisciplinary approaches to address real-world security challenges, emphasizing both technical innovation and ethical considerations. Education: PhD, Swinburne University (2021–2024); MSc, University of Melbourne (2019–2020); BSc, Central South University (2014–2018) Research Highlights: PPG-based authentication systems, AI for threat mitigation, LLM applications in cybersecurity Professional Affiliations: Southern Cross University, Faculty of Science and Engineering His publications span environmental science and technology, reflecting a broader interest in interdisciplinary solutions to global challenges such as water resource management and sustainable development. Awards and grants are not explicitly listed in the provided materials.

Anh NguyenView profile

Associate Professor

• Deep Learning
• Computer Vision
• Generative Modeling
• +5 more

Anh Nguyen is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, affiliated with the Samuel Ginn College of Engineering. His work focuses on deep learning, computer vision, and explainable AI. He holds a Ph.D. from the University of Wyoming and a B.S. from Assumption University. Research highlights include developing methods to improve AI robustness, analyzing biases in large language models, and creating tools for visual correspondence in image processing. He leads the Center for Artificial Intelligence and Cybersecurity Engineering and directs Auburn's first K-6 AI education program through his AI Club after-school initiative. Recipient of a $460,736 NSF CAREER Award for AI innovation Developed the AI@AU lecture series and multiple benchmark datasets (e.g., ImageNet-Hard) Collaborates with industry partners on real-world AI applications Recent projects explore multimodal model limitations (Zerobench), medical imaging (LiteGPT for chest X-rays), and interactive AI systems that incorporate human feedback. His work bridges theoretical advancements with practical implementations in healthcare, wildlife monitoring, and education.

Xi ZhangView profile

Senior Lecturer

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +3 more

Xi Zhang is a Senior University Lecturer at the MT School of Management, New Jersey Institute of Technology (NJIT). His academic background includes a Ph.D. in Entrepreneurship and Strategy from NJIT (2022), an MBA from NJIT (2014), and a B.A. in Economics from Xiamen University (2012). His research focuses on advanced machine learning techniques, particularly graph neural networks (GNNs), with applications in explainable AI, cybersecurity, and molecular modeling. Key areas include developing frameworks like GE-Chat for LLM response generation, RISE for 3D molecular graph explanations, and vulnerability detection systems at the commit level. Dr. Zhang's recent work emphasizes interpretable machine learning, with contributions in proxy graph generation, Bayesian inference for GNN explanations, and neural approaches to code analysis (e.g., DeMinify for variable name recovery). His publications span both theoretical advancements and practical applications in AI and computer science. No scientific awards or grants are explicitly listed in the provided information. No advising relationships or lab affiliations are noted in the text.

Visham RamsurrunView profile

Senior Lecturer

• Cybersecurity
• Internet of Things (IoT)
• Network Security
• +7 more

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.

• Clinical Social Work
• Adolescent High-Risk Behaviors
• Evidence-Based Practices Implementation
• +3 more

Hanni Flaherty, Ph.D., serves as an Assistant Professor and Licensed Clinical Social Worker at Yeshiva University. With over a decade of clinical social work experience specializing in adolescent high-risk behaviors, she bridges practical clinical expertise with academic instruction in social work education. Her dual role as practitioner and educator informs her focus on evidence-based interventions and pedagogical innovation. Professor Flaherty's research centers on clinical social work practice implementation and the transition to online education, examining factors that facilitate adoption of evidence-based practices in youth mental health clinics. Her work explores technology integration in social work training, self-care mechanisms for students, and addressing disparities among vulnerable populations like sexual and gender minority youth. She investigates how simulated role-plays, virtual reality, and data analysis tools can enhance clinical training and student outcomes. Analysis of her 2023-2025 publications reveals dominant themes in educational technology adoption (R programming, VR simulations), evidence-based practice implementation, and specialized interventions for adolescent populations. Her scholarship consistently addresses the research-practice gap while adapting to digital learning environments, with particular attention to private practice development, substance use disparities, and secondary traumatic stress in student populations. No scientific awards were documented in the source materials. Her advisory activities remain unspecified, though her research on student self-efficacy and trauma exposure suggests active engagement with graduate trainees. No dedicated laboratories or research teams were referenced, but her work implies collaboration with mental health clinics serving youth populations and online education technology partners like Everspring.

Lingyu WangView profile

Adjunct Professor

• Cloud Security
• Network Security
• Software Security
• +3 more

Lingyu Wang is an Affiliate Professor at the Concordia Institute for Information Systems Engineering, Concordia University. He holds a Ph.D. in Information Technology from George Mason University (2006). His research focuses on cloud security, SDN/NFV security, security metrics, network security, software security, and privacy preservation. Dr. Wang has published extensively on topics including differential privacy systems, proactive security policy enforcement in containers, and cross-cluster security models for edge-core environments. His work also addresses challenges in securing industrial systems like IEC 61850 substations and 5G networks, leveraging techniques such as state auditing and event monitoring. His recent articles emphasize balancing security with performance efficiency, such as optimizing anomaly detection while preserving privacy and developing tools for real-time inconsistency detection in NFV environments. He has contributed to frameworks like ProSAS for proactive security auditing and VinciDecoder for automated forensic reporting in cloud environments. Dr. Wang’s research bridges theoretical cybersecurity principles with practical applications in cloud infrastructure, emphasizing resilience against zero-day attacks and supply chain vulnerabilities. His work frequently integrates machine learning and deep learning for enhanced security analysis and tool development.

• Computer Architecture
• Reliability Engineering
• Cloud Computing
• +7 more

Professor Gizopoulos Dimitris is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on advanced topics in computer architecture, reliability engineering, and hardware security. With a strong emphasis on fault tolerance and computational integrity, his work addresses critical challenges in modern computing systems such as silent data corruptions, GPU reliability, and RISC-V cloud ecosystems. Key research interests include microarchitecture-level analysis, cross-layer system reliability assessment, and energy-efficient computing. His contributions span fault propagation modeling, hardware-software co-design for resilience, and innovative approaches to detecting silent errors in CPUs and GPUs. He actively participates in large-scale projects like NEUROPULS and Vitamin-V, advancing secure neuromorphic architectures and open-source cloud infrastructure. Recent publications highlight trends in silent data corruption quantification, GPU vulnerability analysis, and energy-efficient RISC-V designs. His work bridges theoretical models with practical implementations, emphasizing real-world validation through frameworks like gem5 and fault injection experiments. Despite no listed advisees or grants in current records, his research collaborations and project leadership position him at the forefront of next-generation computing reliability. Labs and teams associated with his work include the Vitamin-V virtual environment development team and the NEUROPULS consortium for secure neuromorphic accelerators. These initiatives reflect his commitment to advancing both theoretical and applied aspects of dependable computing systems.

Karakostas VasileiosView profile

Assistant Professor

• Computer Architecture
• Cloud Computing
• RISC-V Processors
• +5 more

Karakostas Vasileios is an Assistant Professor at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. His research focuses on computer architecture, cloud computing, and energy-efficient hardware systems. He specializes in areas such as RISC-V processors, virtual memory systems, and hardware security. Vasileios leads projects like NEUROPULS (neuromorphic secure accelerators) and Vitamin-V (RISC-V-based cloud infrastructure validation). His work emphasizes resilience analysis, performance optimization, and trustworthy development frameworks. Education and employment details are not explicitly provided in the source text, but his extensive publication record since 2011 demonstrates continuous academic engagement. Key research trends include: Memory systems optimization (e.g., TLB hierarchies, elastic translations) GPU and SoC fault tolerance analysis Cloud resource management (ACTiCLOUD, DAPHNE runtime) Open-source hardware validation (Vitamin-V project) Notable contributions include the Gem5-marvel simulator for heterogeneous architectures and BypassD for SSD access acceleration. His work bridges theoretical computer architecture with practical cloud and embedded system applications.

Shubhendu MukherjeeView profile

Adjunct Professor

• Computer Architecture
• Fault Tolerant Systems
• Soft Errors
• +5 more

Shubhendu Mukherjee is a Distinguished Engineer at Cavium Networks and Adjunct Professor at the Indian Institute of Technology Kanpur. He is a Fellow of IEEE and ACM, and recipient of the 2009 Maurice-Wilkes Award for outstanding contributions to computer architecture. His career spans leadership roles at Intel and Compaq, where he pioneered fault-tolerant microarchitectures and performance modeling innovations. Education: PhD (1998) and MS (1993) in Computer Science from University of Wisconsin-Madison; B.Tech (1991) in Computer Science and Engineering from IIT Kanpur. Research Interests Mukherjee specializes in computer architecture , soft error modeling , and fault-tolerant design . His work includes Redundant Multithreading (RMT), architectural vulnerability modeling, and on-chip interconnect optimization. Recent publications focus on cache soft error anomalies, quantized AVF analysis, and architectural core salvaging for hard error tolerance. Scientific Awards Maurice-Wilkes Award (2009) IEEE Fellow (2009) ACM Fellow (2011) IEEE Top Picks Awards (2003, 2004) Intel Divisional Recognition Awards (2002–2009) Professional Activities Mukherjee served as General Chair of ASPLOS 2004 , Program Chair of HPCA 2011 , and editorial board member for IEEE Micro, IEEE Computer Architecture Letters, and IEEE Transactions on Dependable and Secure Computing. He also led Intel's SPEARS group (2001–2010), driving architectural innovations in enterprise processors.

• Computer Engineering
• Hardware Security
• Artificial Intelligence
• +3 more

Jaan Raik is a Tenured Full Professor and Head of the Centre for Dependable Computing Systems at Tallinn University of Technology (TalTech), Department of Computer Systems. His research focuses on hardware reliability, fault tolerance in embedded systems, and secure computing architectures. Key areas include fault-resilient deep neural network accelerators, approximation methods for DNNs, and vulnerability assessment in hardware-software systems. He leads the Centre for Dependable Computing Systems, which explores advanced reliability engineering techniques for modern computing systems. His work integrates formal verification, machine learning, and hardware design to address challenges in embedded systems, RISC-V processors, and edge AI chips. Notable contributions include frameworks like Saffira for DNN accelerator reliability assessment and innovative methods for assertion-based verification. Research interests span fault diagnosis, approximation computing, and security-aware architectures. He frequently publishes at top venues such as IEEE European Test Symposium and DDECS, with recent focus on resilient neural networks, cache leakage detection (CLD), and hardware Trojan mitigation. Collaborations include industry partnerships for practical deployment of fault-tolerant systems and academic initiatives to standardize reliability assessment methodologies. His work bridges theoretical computer engineering with applied solutions for real-world systems.

• Security of LLMs
• Log Analysis with LLMs
• Cybersecurity
• +3 more

Valerii Gakh is a PhD student and Junior Research Fellow at Tallinn University of Technology's Department of Software Science (Faculty of Information Technologies). His research focuses on the security of large language models (LLMs) and their application in log analysis. He holds a Master's Degree from the same institution, where he defended his thesis on prompt injection detection solutions. Valerii is supervised by Hayretdin Bahşi and Risto Vaarandi in his doctoral studies exploring efficient security log template extraction using LLMs. Education: PhD Student (2024-), Tallinn University of Technology Master's Degree (2024), Tallinn University of Technology Research Interests: His work addresses critical challenges in AI security, including LLM vulnerability mitigation and leveraging LLMs for cybersecurity tasks like log analysis. Specific topics include prompt injection detection mechanisms and template extraction from security logs to enhance threat identification. Advising & Grants: Supervised by Professors Hayretdin Bahşi and Risto Vaarandi. No grants explicitly mentioned in the provided texts.

Liang HongView profile

Professor

• Wireless Communications
• Signal Processing
• Network Security
• +2 more

Liang Hong is a Professor in the Department of Electrical and Computer Engineering at Tennessee State University. He earned his Ph.D. in Electrical Engineering from the University of Missouri-Columbia in 2002, following industry experience at Siemens Corporate Research. His research encompasses wireless communications, signal processing, network security, and embedded systems. Hong has led multiple federally funded projects including a $599,800 Office of Naval Research initiative on embedded system security and a $310,000 Air Force Research Laboratory program on spectrum communication. His extensive publications demonstrate consistent focus on cognitive radio networks, beamforming techniques, cybersecurity, and networked control systems. Recent work emphasizes AI applications for threat detection, cloud security, and adaptive resource management in wireless environments. Hong received the TSU Faculty Research Award (2005) and was nominated for Teacher of the Year (2010). He has chaired technical committees for IEEE conferences and guided award-winning student research.