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Paloma LiuView profile

Senior Lecturer

• Cybersecurity
• Connected and Autonomous Vehicles
• Data Analytics
• +12 more

Dr. Paloma Liu is a Senior Lecturer in Project Management at Huddersfield Business School, University of Huddersfield. She serves as course leader for BSc programs in Business Data Analytics, Logistics & Supply Chain Management, and Business with Project Management. She is a core member of the Future Mobility Lab and the Sustainable Living Research Centre, contributing to interdisciplinary research in mobility and sustainability. Her research expertise lies at the intersection of cybersecurity, connected and autonomous vehicles (CAVs), data analytics, and public perception. She investigates the security risks of CAVs, keystroke biometrics, and urban mobility patterns using AI and computer vision. Her work aligns with UN Sustainable Development Goals, particularly in sustainable cities and responsible consumption. The recent publications highlight a strong trend in cybersecurity for intelligent transportation systems, biometric authentication, and data-driven mobility solutions. Her work combines technical depth with policy relevance, often involving collaboration with industry and government stakeholders. Fellow of the Higher Education Academy (FHEA) Certified Project Management Professional (PMP) by PMI Dr. Liu is actively supervising PhD students and currently offers an EPSRC-funded PhD position in Post Quantum Cybersecurity, jointly supervised with Professor Simon Parkinson. She also serves as an External Examiner at Queen Margaret University (2025–2029), MSc Dissertation Supervisor at University College London (since Apr 2023), and Visiting Lecturer at Montpellier Business School (since Mar 2023), reflecting her growing academic influence. She leads the Future Mobility Lab, a research team focused on next-generation transport systems, where she mentors students and collaborates on projects involving AI, cybersecurity, and sustainable urban development.

• Smart Grid Security
• Privacy-Preserving Techniques
• Federated Learning
• +5 more

Mohamed I. Ibrahem is an active academic researcher specializing in cybersecurity applications for smart grid infrastructure and Internet of Things systems. His work bridges computer science, electrical engineering, and artificial intelligence with a particular focus on privacy-preserving techniques and secure machine learning applications. Dr. Ibrahem's research interests center on smart grid security , where he has developed innovative approaches for electricity theft detection, false data injection prevention, and privacy-preserving monitoring systems. His work in federated learning security addresses critical vulnerabilities in distributed machine learning systems, particularly against Trojan attacks and evasion techniques. Additional research areas include explainable AI applications in healthcare diagnostics, IoT security mechanisms, and deep learning optimization for resource-constrained environments. His recent publication trends reveal a growing emphasis on practical security implementations for real-world energy infrastructure, with increasing attention to explainability and robustness in AI-driven security systems. The interdisciplinary nature of his work connects cybersecurity principles with specific domain challenges in power systems and healthcare applications. Dr. Ibrahem has secured research funding for multiple projects focused on smart grid protection systems and privacy-preserving machine learning frameworks, though specific grant details are not visible in the publication metadata. His collaborative research network spans multiple international institutions with strong connections to Middle Eastern universities.

• Computational Thinking Education
• Virtual Machine Interpreters
• Eye-tracking Analytics in Education
• +4 more

Kevin Casey is a Lecturer in the Department of Computer Science , Faculty of Science & Engineering , Maynooth University , associated with the Hamilton Institute . Since 2015 he has taught and researched at Maynooth, focusing on computational thinking education, learning analytics, virtual-machine interpreters, and educational technology innovation. Education PhD in Computer Science – Trinity College Dublin MSc (by Research) Computer Science – University College Dublin BSc (Hons) Computer Science – University College Dublin Postgraduate Diploma in Arts in Training and Education – Griffith College Dublin Diploma in Training and Education – Griffith College Dublin Research Interests Dr Casey’s research integrates computer-science education with advanced analytics and systems design. He investigates how computational-thinking curricula can be co-created with teachers and scaled across diverse communities, from primary schools to nursing homes. He employs eye-tracking and keystroke analytics to model learner behaviour and predict academic performance, and explores immersive virtual-reality environments for collaborative programming. On the systems side, he optimises virtual-machine interpreters and studies the impact of JVM configuration on performance and energy efficiency. Projects & Funding CoCoA II (2023–2024) – Principal Investigator, €299,976 SFI Discover Programme Mob-programming in an Immersive VR Environment (2016–2017) – Co-supervisor, €96,000 Eye See, You Understand – Co-supervisor, €96,000 (eye-tracking analytics) InSPECT – Collaborator, €272,895 (computational-thinking outreach to DEIS schools) Professional Service Local Organising Committee Member, ITiCSE 2022 Steering Committee Member, International Conference on Engaging Pedagogy (ICEP) 2008–2018 Joint-publication Chair, ICEP 2015–2016

Ritwik BanerjeeView profile

Assistant Professor

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +3 more

Ritwik Banerjee serves as a Research Assistant Professor in the Department of Computer Science at Stony Brook University, where he conducts cutting-edge research at the intersection of natural language processing, computational linguistics, and healthcare informatics with applications in deception detection and misinformation analysis. His educational background includes: PhD in Computer Science from Stony Brook University, focusing on stylometry, biomedical language extraction, and healthcare informatics M.Sc. in Computer Science from Chennai Mathematical Institute, India B.Sc. in Mathematics and Computer Science from Chennai Mathematical Institute, India Banerjee's research centers on statistical NLP and computational linguistics with emphasis on linguistic semantics and implicit knowledge extraction from natural language. His work spans stylometric analysis for authorship verification, relation extraction in biomedical contexts, and keystroke dynamics for detecting deceptive intent in digital communications, demonstrating exceptional versatility across theoretical and applied domains. Analysis of his 15 most recent publications (2021-2025) reveals two dominant research thrusts: healthcare NLP applications (including kidney disease diagnosis from ultrasound reports and medication event extraction from clinical text) and misinformation detection systems (particularly for pandemic-related content on social media). His methodological innovations frequently combine deep learning architectures with domain-specific ontologies and pragmatic language understanding techniques to address complex real-world challenges. His notable recognition includes: Computer Science Chairman Fellowship (2009-2010) While his publication record demonstrates significant research impact, publicly available information does not specify graduate student advising activities or externally funded research grants. No dedicated laboratory or research team structure is referenced in the provided materials, though his work clearly intersects with Stony Brook's broader AI and healthcare informatics initiatives.

• Neural Machine Translation
• Computational Linguistics
• Natural Language Processing
• +6 more

Antonio Toral is a leading researcher at the University of Groningen, focusing on Neural Machine Translation (NMT) , human evaluation , and parallel corpus curation . His work spans low-resource language modeling, lexical diversity enhancement, and multilingual figurative language detection. Affiliations: University of Groningen, MaCoCu Project, CREAMT Consortium Key projects: MaCoCu (Massive collection of under-resourced language data) CREAMT (Creativity in literary translation) Research interests include: Improving NMT naturalness and lexical richness Document-level evaluation of machine translations Character-level modeling and downsampling techniques Reproducibility challenges in human NLP evaluation Cross-lingual formality transfer without parallel data His recent articles (2021–2025) demonstrate expertise in: Reinforcement learning for naturalness preservation Statistical analysis of translationese effects Dependency-based reordering models Pivot translation for Catalan→Chinese Domain-specific corpus creation for EU Digital Service Infrastructures

• Biometrics
• Privacy
• Authentication
• +3 more

Denis Migdal is an Associate Professor (Maître de Conférence) at the University of Clermont-Ferrand and a member of the LIMOS laboratory . His research focuses on privacy-compliant biometric authentication systems, particularly leveraging keystroke dynamics for secure online transactions. He holds a PhD from 2019 titled "Contributions to keystroke dynamics for privacy and security on the Internet." Education: PhD in Computer Science, University of Clermont-Ferrand (2019) Internships at the University of Oslo (2016-2017) on the OffPAD project , resulting in 6 demonstrators presented at ACM CCS 2016. Research Interests: Privacy-preserving authentication protocols, keystroke dynamics anonymization, synthetic dataset generation for behavioral analysis, and trusted device architectures (e.g., OTDP, TLS Switching). He collaborates with institutions like the University of Reggio Calabria on social network identity verification. Key Contributions: Proposed Privacy Identity Code frameworks respecting user privacy. Developed methods to anonymize keystroke data and generate synthetic datasets (published in FGCS Q1). Co-designed the OffPAD trusted device architecture and TLS Switching protocols to mitigate malware threats in client devices. Collaborations: OffPAD consortium, University of Oslo, University of Reggio Calabria. His work addresses vulnerabilities in client-side authentication and enhances cybersecurity for critical sectors like healthcare and banking.

Mark GrimesView profile

Professor

• Human Computer Interaction
• Conversational Agents
• Deception Detection
• +3 more

Mark Grimes is a Professor of Practice in the Decision and Information Sciences Department at the University of Houston's Bauer College of Business. His academic background includes a PhD in Management Information Systems from the University of Arizona (2015), an MBA from Belmont University, and a BBA from the University of Mississippi. He has conducted research at the National Center for Border Security and Immigration (BORDERS), a DHS center of excellence, focusing on deception detection, human-computer interaction, and cybersecurity. His research interests center on analyzing human-computer interaction behaviors (e.g., mouse movements, typing patterns) to detect cognitive/emotional states, conversational agent applications in security, and judgment/decision-making processes. He has secured over $220,000 in research funding and presented work to groups like the 111th Military Intelligence Brigade and the International Information Systems Security Certification Consortium. Grimes' publications emphasize conversational AI ethics, chatbot design, and cybersecurity applications. His work bridges technical systems with human behavioral analysis, addressing challenges in deception detection and adversarial thinking. He teaches courses in database management, information systems administration, and quantitative methods.

Elif ŞenerView profile

Lecturer

• Interaction Design
• User Experience (UX)
• Virtual Reality (VR)
• +6 more

Dr. Elif Şener is a Lecturer at the School of Design, University of Leeds, with expertise in interaction design, UX of Virtual Reality (VR), immersive typography, and affective computing. Her academic journey began at İzmir University of Economics (BFA in Visual Communication Design) and continued at Sabancı University (MFA in Visual Arts & Visual Communication Design), before completing her Ph.D. in Design at North Carolina State University as a Fulbright Scholar. Education: BFA in Visual Communication Design, İzmir University of Economics MFA in Visual Arts & Visual Communication Design, Sabancı University Ph.D. in Design, North Carolina State University Şener’s research focuses on the intersection of design, technology, and user experience, particularly in virtual reality. Her master’s project MoodFace explored keystroke dynamics visualization, while her doctoral work investigated 3D typographic layouts in VR and their impact on memory recall. She emphasizes the democratization of XR technologies for educational and everyday contexts. Scientific Awards: Fulbright Scholarship (2018) At the University of Leeds, Şener teaches MA Design and conducts research into immersive analytics, typographic innovation, and user-centered design practices. She welcomes inquiries from prospective PhD students interested in her work.

Marjory Da Costa AbreuView profile

Associate Professor

• Applied Artificial Intelligence
• Biometrics
• Health Informatics
• +9 more

Marjory Da Costa Abreu is an Associate Professor in Ethical Artificial Intelligence and Transforming Lives Fellow at Sheffield Hallam University's School of Computing and Digital Technologies, within the College of Business, Technology and Engineering. She holds a PhD, MPhil, BSc, and is a Senior Fellow of the Higher Education Academy (SFHEA). Research Interests: Applied AI, biometrics (face analysis, keystroke dynamics, speech recognition), health data analytics, forensic technologies, digital law, and ethical AI policy. Her work emphasizes decolonial approaches to museum data, transparency in gaming user behavior, and affordable biometric solutions. Projects: Ongoing projects include medieval document analysis for neurological disorder insights, signal processing for medical diagnostics, forensic keystroke analysis for social media accountability, and AI fairness in judicial systems. Winner of the Newton Research Collaboration Programme Award Collaborates with University of Kent, University of York, and Brazilian institutions Formerly a Reader in Artificial Intelligence at UFRN (until 2019) Teaching: Leads the MSc Artificial Intelligence program and the Artificial Intelligence Seminar Series. Focus areas include programming, machine learning, and research methods. Service Roles: External Examiner, Journal Associate Editor, UKRI reviewer. Advocate for women and underrepresented groups in STEM.

Simon ParkinsonView profile

Professor

• Cyber Security
• Artificial Intelligence
• Access Control
• +5 more

Simon Parkinson is a Professor of Cyber Security at the University of Huddersfield's School of Computing and Engineering, serving as Director of the Centre for Cybersecurity and Computer Science Department Research Coordinator. His active research program focuses on securing digital systems through AI-driven approaches, with leadership roles in multiple research centers and acceptance of new PhD students. His research expertise spans Cyber Security and Artificial Intelligence, with concentrated work on identity management, biometric authentication, and access control systems. Key contributions include developing novel methods for detecting security weaknesses in enterprise IT systems and leveraging behavioral biometrics (e.g., keystroke dynamics, fitness device data) for intrusion prevention. This work directly supports UN Sustainable Development Goals through secure technology development. Recent publications (2024-2025) reveal strong interdisciplinary trends, particularly at the intersection of machine learning security frameworks, biometric spoofing countermeasures, and transportation system vulnerabilities. A notable pattern involves applying human factors research (eye tracking, situational awareness) to enhance cybersecurity in augmented reality and connected vehicle environments. Professional recognition includes multiple teaching awards: School Supervisor of the Year (2018, 2019, 2024) University Supervisor of the Year (2020) His supervision excellence is evidenced by 20 completed research degrees (15 PhD, 5 Master by Research) and examination of 10 international PhD theses. Major grant leadership includes £2.6m+ in funding from EPSRC, DSTL, and Innovate UK for cybersecurity initiatives: Centre for Biominetic Societal Futures (2021-2024) Digital Catapult EPSRC Fellowship AI-driven precision manufacturing projects As Centre for Cybersecurity Director, he leads initiatives in threat detection frameworks and collaborates with the Centre for Planning, Autonomy and Representation of Knowledge on AI applications for societal resilience, including biometric security systems and critical infrastructure protection.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +5 more

Mahesh Banavar is an Associate Professor in the Department of Electrical and Computer Engineering at Clarkson University, affiliated with the Coulter School of Engineering & Applied Sciences and the Institute for STEM Education. He directs the CoSiNe Lab and focuses on research in signal processing, machine learning, complex networks, and STEM education. His work addresses cybersecurity, behavioral biometrics, and continuous authentication. Education: B.E. in Telecommunications Engineering from Visvesvaraya Technological University (2005) M.S. and Ph.D. in Electrical Engineering from Arizona State University (2007, 2010) Research interests span signal processing applications in security and biometrics, machine learning for healthcare and autonomous systems, and innovative STEM education strategies. Notable awards include the Clarkson University Outstanding Advisor Award (2019), HKN Outstanding Teaching Award (2015-2016), and a Graduate Teaching Excellence Award (2008-2009). His publications emphasize localization algorithms, biometric authentication, and educational technology innovations. Labs/Teams: Director of the CoSiNe Lab, developing interdisciplinary research in signal processing and education.

• Deep Learning
• Cybersecurity
• Biomedical Engineering
• +5 more

Faraz Hussain is an Associate Professor at Clarkson University's Coulter School of Engineering & Applied Sciences, with joint appointments in the Departments of Electrical & Computer Engineering and Computer Science. His research focuses on deep learning, cybersecurity, and biomedical engineering, with particular emphasis on applications in biometrics, edge computing, and healthcare technology. Dr. Hussain's work spans theoretical advancements and practical implementations, including explainable AI, fingerprint segmentation systems, and hazard detection in retail environments. He has contributed to interdisciplinary projects such as automated visualization of flow cytometry data and parameter estimation for biological models using Bayesian methods. His publications highlight a trajectory toward solving real-world challenges through machine learning and computational methods, with notable contributions in cybersecurity (e.g., keystroke dynamics for APT prevention), medical imaging (pulmonary disease classification), and autonomous robotics (ROS-based deep learning deployment). While no formal awards are listed, his active research portfolio demonstrates sustained innovation in AI-driven solutions across multiple domains. His advising and grants activities remain unspecified in the provided materials.

Hooman OroojeniView profile

Senior Lecturer

• deep learning
• neuroevolution
• tensor decomposition
• +2 more

Hooman Oroojeni is a Senior Lecturer in Data Science at the University of Greenwich's School of Computing and Mathematical Sciences. He holds a PhD in Computer Science (AI) from Goldsmiths, University of London, complemented by an MSc in Computer Networking and BSc in Software Engineering. His research integrates deep learning with evolutionary methods, including neuroevolution and dispersive flies optimisation. Applications span healthcare (Parkinson's detection, ICU monitoring) and high-dimensional data analysis using tensor decomposition techniques. Recent publications (2018-2022) demonstrate cross-disciplinary approaches combining neural networks, swarm intelligence, and medical informatics.

Alexander DemosView profile

Associate Clinical Professor

• Music Cognition
• Social Dynamics
• Dynamical Systems
• +4 more

Dr. Alexander Demos is a Clinical Associate Professor in the Department of Psychology at the University of Illinois Chicago (UIC), affiliated with the College of Liberal Arts and Sciences. He leads the Music, Action, and Dynamics laboratory, focusing on understanding cognitive and social mechanisms through musical performance and social interactions. His work integrates dynamical systems theory and time-series analysis to study how music facilitates social/emotional experiences and uses smartphone data for mental health monitoring via the BiAffect project. He holds a PhD from the University of Connecticut (2013), an MA (2006) and BA (2003) in Cognitive Psychology from New York University. Research interests include musical neurodynamics, synchronization in human-machine systems, and digital phenotyping using passive smartphone data. He has pioneered studies on keystroke dynamics as biomarkers for cognitive function in mood disorders, and developed novel methods for analyzing interpersonal coordination during music performance. His lab actively seeks graduate students for projects in music cognition and BiAffect-related research. Key contributions include modeling social dynamics in musical ensembles, validating smartphone-based cognitive assessments, and exploring the neurophysiological basis of musical expression. His work bridges computational modeling with empirical studies, emphasizing practical applications in clinical psychology and human-computer interaction.

• Cybersecurity
• Authentication Systems
• Machine Learning
• +4 more

Javier Junquera Sánchez is an Associate Professor at the University of Alcalá's Department of Computer Science. He holds a PhD in Computer Science from the same institution, awarded in 2023 for his thesis on 'Deep Defense in Access Control Systems via Continuous Authentication.' His research focuses on cybersecurity, authentication systems, machine learning applications for security, IoT privacy, and cryptographic protocols for quantum-resistant systems. Education: PhD in Computer Science, University of Alcalá (2023) His work spans topics like GNSS security, port knocking systems (C-Lock), distributed e-voting frameworks (Diversec), and mobile continuous authentication using keystroke dynamics. Recent publications address quantum-resistant cryptography for Galileo OSNMA and vulnerabilities in data-over-sound IoT healthcare devices. No scientific awards or grants are explicitly mentioned. He has not listed any advisees or lab affiliations in the provided texts.