Funlade Sunmola is a Principal Lecturer in Manufacturing and Industrial Engineering at the University of Hertfordshire , affiliated with the School of Engineering and Computer Science and the Department of Engineering and Technology. He holds a PhD in Computer Science (Artificial Intelligence and Robotics) from the University of Birmingham and has nearly 40 years of professional experience across civil engineering, manufacturing, healthcare, and academia. Education: BEng (Hons) in Civil Engineering, Ahmadu Bello University MSc in Industrial Engineering, University of Ibadan MA in Accounting and Finance, Birmingham City University MPhil in Manufacturing Engineering, University of Birmingham PhD in Computer Science, University of Birmingham Research Interests: Focuses on Applied Artificial Intelligence , Sustainable and Smart Industries , and Industry 4.0 . Key areas include supply chain visibility, blockchain integration, machine learning applications in manufacturing, and virtual engineering. Leads the Duncan Calder Virtual Engineering Lab and oversees MSc Online Engineering Programmes. Grants & Projects: PI of LINK: Digital Direct Connection for Salvage Construction Materials (Circular Economy) PI of N-BICC: Cassava Innovation Deployment Co-I in Solar Cool System (So-Cool) for Smallholder Farmers Labs/Teams: Heads the Duncan Calder Virtual Engineering Lab , focusing on immersive technologies and virtual product design.
Ka Ho Chow is an Assistant Professor in the Department of Computer Science at the University of Hong Kong, part of the School of Computing and Data Science. He holds a PhD from Georgia Institute of Technology and was previously a research scientist at IBM Research. His research focuses on the intersection of machine learning, cybersecurity, and scalable systems, emphasizing trustworthy AI and defense against security/privacy threats in federated learning, large language models, and visual recognition systems. Key achievements include IBM PhD Fellowship (2022) and Croucher Scholarship (2021). Education: PhD in Computer Science from Georgia Tech (2020), advised by Prof. Ling Liu. His work spans algorithmic optimization, infrastructure resilience, and adversarial machine learning. Current research explores attack-resilient solutions for centralized/federated learning and AI system vulnerabilities. Recent articles highlight innovations in federated learning security, gradient inversion attacks, backdoor detection, and privacy-preserving techniques. He has openings for PhD students interested in AI security and trustworthy systems. His lab collaborates on projects involving blockchain fraud detection (ZipZap), facial recognition privacy (Personalized Masks), and graph neural network robustness. Awards: IBM PhD Fellowship (2022), Croucher Scholarship (2021). Active in guiding PhD candidates and advising on microservices cloud migration (Atlas/SCAD systems). Research outputs include over 30 peer-reviewed papers spanning cybersecurity, AI ethics, and distributed learning frameworks.
Professor Mark Levine is a leading figure in social psychology at Lancaster University , affiliated with the Department of Psychology and multiple interdisciplinary research centers including Security Lancaster (Behavioural Science) , Cyber Security Research Centre (Psychology) , Data Science Institute , and Social Processes . His research lies at the intersection of psychology and technology, focusing on social identity, group dynamics, and prosocial behavior in public and digital environments. His research interests include bystander intervention, violence prevention, urban resilience, cybersecurity, emergency response, public safety, and the role of technology in shaping human behavior. He employs diverse methodologies such as virtual reality, CCTV analysis, smartphone data collection, and computational text analysis. His work is highly applied, informing policy and practice in policing, emergency services, and community safety. The trends in his recent publications reflect a strong interdisciplinary focus, combining social psychology with computer science, criminology, and urban studies. His articles frequently analyze real-world behavioral data from public spaces and digital environments, emphasizing the protective role of group identity in emergencies and the feasibility of technological interventions for societal challenges. Scientific grants and projects he has led include: REASON: Resilient Autonomous Socio-cyber-physical agents (£3.3M, EPSRC) RBOC Network+: Urban resilience in 2050 (£2.25M, EPSRC) Challenging the Bystander Effect via Documentary Film (A$356K, ARC) STRETCH: Technology-enhanced support for older adults (£1.3M, EPSRC) “Being There”: Humans and Robots in Public Space (£2.4M, EPSRC) Advising and grants : He supervises 7 postgraduate research students and has secured over £15 million in research funding from EPSRC, ESRC, DSTL, NCSC, Home Office, and international bodies. He advises government departments, police forces, and city councils on public safety and prosocial behavior. His collaborative work extends to creative industries and third-sector organizations like AGE-UK Exeter. Research groups and collaborations : He is deeply embedded in interdisciplinary networks, contributing to projects involving computer scientists, roboticists, software engineers, and HCI researchers. His affiliations with Security Lancaster and the Data Science Institute reflect his central role in socio-technical research at Lancaster.
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).
Ashish Khisti is an Associate Professor at the University of Toronto's Department of Electrical and Computer Engineering (ECE), where he directs the Signals, Multimedia and Algorithms Laboratory (SMA Lab). He holds the Canada Research Chair (Tier II) and maintains affiliations with the Vector Institute for Artificial Intelligence. His research bridges communication systems, information-theoretic security, and machine learning, with a focus on real-time streaming and privacy-preserving algorithms. Research Trends: Recent publications emphasize streaming codes for latency-sensitive networks , machine learning-driven compression , and privacy mechanisms in federated learning . Scientific Recognition: Canada Research Chair (Tier II), 2012 and 2017 renewal Cisco Research Center Award, 2017 Ontario Early Researcher Award, 2012 Best Paper at NeurIPS 2021 Deep Generative Models Workshop Academic Contributions: Supervised PhD students Ahmed Badr, Farrokh Etezadi, and Si-Hyeon Lee. Served as Associate Editor for IEEE Transactions on Communications (2012-2015) and IEEE Transactions on Information Theory (2015-2018). Labs & Collaborations: Leads the Signals, Multimedia and Algorithms Laboratory, collaborating with institutions like KAUST, Texas A&M University (Qatar), and the Vector Institute. Organized workshops at BIRS and IEEE conferences.
Jilles Vreeken is a Professor of Computer Science at Saarland University and tenured faculty at the CISPA Helmholtz Center for Information Security, where he leads the Exploratory Data Analysis research group. He is also an ELLIS Fellow and Faculty of the Saarbrücken Unit on AI and ML. His work bridges theoretical foundations with practical applications in causal inference, unsupervised learning, and exploratory data analysis. Dr. Vreeken's research focuses on developing theory and algorithms for answering fundamentally exploratory questions about data: "what is going on in my data?", "what causes what and how?", and "what can we learn from this model?" without making unnecessary or unjustified assumptions. He takes a principled approach based on information theory to identify what is worth knowing, then develops efficient algorithms for extracting useful interpretable results. His work spans causal inference under realistic conditions (including hidden confounding, selection bias, and non-i.i.d. data), summarizing complex data and models in understandable terms, and combining these threads to create more robust and useful models across diverse data types. His recent publications demonstrate a strong trend toward causal discovery in increasingly realistic settings, including non-stationary time series, event sequences, and scenarios with hidden confounders. He has made significant contributions to federated learning, interpretable machine learning, and pattern mining. His work consistently applies information-theoretic principles to develop methods that are both theoretically sound and practically useful for extracting insights from complex data. Dr. Vreeken has received numerous prestigious awards including: IEEE ICDM'18 Tao Li Award for Excellence in Research IEEE ICDM'18 Best Paper Award UdS-CS'15 Busy Beaver Teaching Award ACM SIGKDD'11 Best Student Paper Award ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award ECML PKDD'09 Best Student Paper Award As an advisor, Dr. Vreeken has mentored numerous doctoral researchers to completion, including Dr. Osman Ali Mian, Dr. David Kaltenpoth, Dr. Boris Wiegand, Dr. Sebastian Dalleiger, Dr. Janis Kalofolias, Dr. Jonas Fischer, Dr. Alexander Marx, Dr. Panagiotis Mandros, Dr. Kailash Budhathoki, Dr. Roel Bertens, Dr. Koen Smets, and Dr. Michael Mampaey. He has secured significant research funding as PI for multiple projects including "AI for Prediction and Therapy Guidance in Acute Stroke" (HAICU, 2025-2028), "Neuro-Explicit Models of Language, Vision and Action" (RTG, DFG, 2023-2028), and "Crushing Antimicrobial Resistance using Explainable AI" (HAICU, 2021-2024). Dr. Vreeken leads the Exploratory Data Analysis (EDA) research group at CISPA, which focuses on developing theory and algorithms for discovering novel insights from data, learning inherently interpretable models, and drawing reliable causal conclusions. The group has produced numerous influential algorithms and frameworks in causal inference, pattern mining, and exploratory data analysis, with applications spanning healthcare, materials science, and cybersecurity.
Zhe Hou is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, Australia. His academic journey includes a PhD in automated reasoning for separation logic from the Australian National University (2015) and prior research roles at Nanyang Technological University, Singapore (2015-2017). He joined Griffith University in 2017 and became permanent faculty in late 2019. Research Interests : Formal methods for software verification Automated reasoning with logical frameworks Blockchain technology and security Quantum computing verification Integration of LLMs with rigorous reasoning Sports analytics via model checking Recent Publications demonstrate expertise in neural-symbolic reasoning, blockchain security, quantum SAT solvers, and runtime verification frameworks. His work combines formal logic with machine learning for applications in cybersecurity and AI trustworthiness. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (2025) Supervision Roles : Principal/Associate Supervisor for 6+ doctoral projects in blockchain security, AI verification, and network security. Professional Activities : Editor for Springer-Nature and Formal Aspects of Computing special issues, conference chair for ICFEM, ICECCS, and ISACE symposia.
Dr Pengpeng Hu is a Senior Lecturer in Fashion Technology at the Department of Materials, The University of Manchester, UK. His research focuses on geometric deep learning, 3D human body reconstruction, point cloud processing, and smart textiles, bridging fashion technology with biomedical and engineering applications. Associate Editor: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Automation Science and Engineering Academic Editor: PLOS ONE Editorial Board Member: Scientific Reports Programme Chair: 25th UK Workshop on Computational Intelligence Area Chair: 35th British Machine Vision Conference His work advances vision-based measurement systems, wearable technology, and 3D scanning for clothing and healthcare. Recent publications include innovations in MXene-based electronic textiles, 4D hand measurement extraction, and anthropometric analysis from depth images. Recipient of the Emerald Literati Award for an outstanding paper in 2019 Dr Hu accepts self-funded PhD students in areas like 3D human reconstruction, point cloud processing, and smart textiles. His editorial roles and conference leadership highlight his influence in computational intelligence and machine vision communities.
Kuljeet Kaur is a Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) in Montreal, Canada. Her research is conducted through the LACIME (Communications and Microelectronic Integration Laboratory), a renowned research unit focusing on communications and microelectronic integration. She maintains an active research program with numerous publications and student supervision activities. Professor Kaur's research spans multiple interconnected domains focused on next-generation computing and communication systems. Her primary research axes include Sensors, Networks and Connectivity; Intelligent and Autonomous Systems; and Software Systems, Multimedia and Cybersecurity. Within these broad areas, she specializes in Cloud Computing, Edge/Fog Computing, Internet of Things (IoT), Cybersecurity, Privacy, Federated Learning, and Energy Management. Her work bridges theoretical foundations with practical implementations in intelligent transportation systems, healthcare applications, and smart grid technologies. Analysis of Professor Kaur's recent publications reveals a strong focus on security and privacy challenges in emerging computing paradigms. A significant portion of her work addresses federated learning approaches that maintain data privacy while enabling collaborative AI model training. Her research also demonstrates expertise in edge computing architectures, particularly for IoT applications, with emphasis on energy efficiency and security. The publications show consistent interdisciplinary collaboration across computer science, electrical engineering, and transportation domains. Professor Kaur actively supervises multiple graduate students at various levels. Her supervision portfolio includes doctoral candidates working on topics like decentralized AI networks and secure federated learning, as well as master's students focusing on edge AI for IoT applications, sensor drift compensation, and zero trust architecture for IoT. She also guides project students working on practical implementations of AI for smart grid optimization and secure IoT protocols. Her research is conducted within the LACIME laboratory, which brings together researchers working on everything from micro- and nanofabrication processes to communication protocols and signal processing. The lab provides a transdisciplinary environment where Professor Kaur's work on cyber-physical systems and secure communications benefits from complementary expertise in integrated circuit design and microsystems.
Bogdan Iancu is a University Lecturer in the Department of Information Technology at the Faculty of Science and Engineering, Åbo Akademi University. He holds a PhD and Docent qualification in Computer Science, with extensive expertise in artificial intelligence and computer vision applications, particularly in the maritime domain. His academic career spans numerous research projects and publications that bridge theoretical AI concepts with practical industry applications. Dr. Iancu's research focuses on AI applications in maritime technology, with special emphasis on object detection systems, security challenges in AI models, and sustainable technological solutions. He has developed benchmark datasets like ABOships and ABOships-PLUS that have become valuable resources for researchers in maritime computer vision. His work addresses critical challenges including adversarial attacks on object detection systems, as evidenced by his 2025 publication on TOG Adversarial Attacks in YOLO Models. The analysis of his recent publications reveals a clear progression from foundational dataset creation to advanced security analysis and neurosymbolic approaches that combine neural networks with symbolic reasoning. His research shows increasing sophistication in addressing real-world challenges in maritime AI systems, with particular attention to robustness, security, and practical implementation. Dr. Iancu actively participates in numerous research projects including EDISS (Engineering of Data-intensive Intelligent Software Systems), SMARTER (Sea4Value Smart Terminals), and DECATRIP (Decarbonizing Transport Corridors). These projects involve collaboration with industry partners across Finland and Europe, focusing on applying AI to solve real-world challenges in maritime transport, digitalization, and sustainability. He has contributed to the academic community through teaching courses in Artificial Intelligence, Data Science, and Graph Algorithms, and through active participation in the Finnish Artificial Intelligence Society. His work aligns with UN Sustainable Development Goals, particularly those related to industry innovation, infrastructure, and climate action through projects like DECATRIP that focus on decarbonizing transport corridors.
Duen Horng (Polo) Chau is an Associate Professor of Computing at Georgia Institute of Technology, where he co-directs the MS Analytics program. He serves as Director of Industry Relations for The Institute for Data Engineering and Science (IDEaS) and Associate Director of Corporate Relations for The Center for Machine Learning. Ph.D. in Machine Learning from Carnegie Mellon University His research group integrates machine learning and visualization to develop scalable interactive tools for analyzing massive datasets, interpreting AI models, and addressing challenges in cybersecurity, human-centered AI, graph visualization and mining, and social good. His work has led to open-sourced or deployed technologies by Intel (e.g., ShapeShifter, SHIELD), Google, Facebook, Symantec (Polonium, AESOP), and Atlanta Fire Rescue Department, with Symantec's AESOP protecting 120 million users from malware. Scientific Awards & Fellowships: Carnegie Mellon University Computer Science Dissertation Award (Honorable Mention) Intel Outstanding Researcher Award Raytheon Faculty Fellowship Edenfield Faculty Fellowship Outstanding Junior Faculty Award The Lester Endowment Award Symantec Fellowship (twice) Best Student Paper (SDM'14) Runner-up Best Student Paper (KDD'16) Runner-up Best Demo (SIGMOD'17) Chinese CHI'18 Best Paper ACM TiiS 2018 Best Paper (Honorable Mention) He has received research grants from NSF, NIH, NASA, DARPA, Google, NVIDIA, IBM, Yahoo, Amazon, Microsoft, eBay, and LexisNexis. His leadership roles include steering committee membership for ACM IUI, Associate Editor for ACM TIIS, and organizing chairs for ACM KDD'14, ACM WSDM'16, and the IDEA workshop at KDD.
Mahdi Fazeli is an Associate Professor at the School of Information Technology, Halmstad University, Sweden, specializing in hardware security and trust, energy-efficient computing, and embedded and cyber-physical systems. His academic journey began with a Ph.D. in Computer Engineering from Sharif University of Technology, Iran, in 2011. His career progression includes positions as Associate Professor at Bogazici University (2019-2021) and Iran University of Science and Technology (2016-2019), and Assistant Professor at the same institution (2011-2016). His research interests focus on hardware security and trust, reliable VLSI circuits and systems, energy-efficient computing, and dependable embedded systems. His work bridges the gap between theoretical security concepts and practical implementations in real-world systems, particularly in IoT and embedded environments. He has established himself as a leading researcher in Physical Unclonable Functions (PUFs), hardware trojans detection, and energy-efficient security solutions for resource-constrained devices. His publication record shows a clear progression and deepening expertise in hardware security, with recent work focusing on cutting-edge applications in edge computing, vehicular networks, and IoT security. His 2023-2025 publications demonstrate significant contributions to magnetic memory-based security primitives, anomaly detection systems, and energy-efficient security mechanisms. Throughout his career, Fazeli has led multiple research initiatives including the Dependable Systems and Architecture Lab (DSA) and the Networked and Embedded Systems Lab at Iran University of Science and Technology. His leadership extends to heading the Hardware Group and serving as Vice Chair for Educational Affairs, demonstrating his commitment to both research excellence and academic administration.
David Brown is a Professor in Interactive Systems for Social Inclusion at Nottingham Trent University's School of Science & Technology, Department of Computer Science. He serves as Director of the Computing and Informatics Research Centre (CIRC) and Research Group Leader for the Interactive Systems Research Group (ISRG). Director, Computing and Informatics Research Centre Research Group Leader, Interactive Systems Research Group Governor, Oak Field School for students with severe learning disabilities Conference Chair, International Conference on Disability, Virtual Reality and Associated Technology (ICDVRAT21) Associate Editor, Frontiers: Virtual Reality in Medicine Professor Brown's research focuses on developing inclusive technologies for people with disabilities. His work spans accessibility for students with learning, physical and sensory impairments; virtual reality applications for rehabilitation; multimodal affect recognition systems; social robotics for education; accessible visual programming toolkits; and serious games for developing physical and cognitive skills. His research is characterized by strong interdisciplinary collaboration and practical application in educational and healthcare settings. His recent publications demonstrate a consistent focus on applying emerging technologies like virtual reality, machine learning, and social robotics to address real-world challenges in accessibility and inclusion. The research shows a clear trajectory toward increasingly sophisticated multimodal systems that can detect user states and adapt accordingly, with applications ranging from autism support to mental health interventions. Extensive EU-funded research projects including Horizon 2020, Erasmus+, and EPSRC grants Notable projects: DIVERSIA, MaTHiSiS, Pathway, AI-TOP, EDUROB, No One Left Behind, Real Life, RISE Professor Brown has supervised numerous PhD students and collaborates extensively with international partners across Europe and Asia. His work bridges computer science, psychology, education, and healthcare to create technologies that promote social inclusion and improve quality of life for people with disabilities.
William Hobbs is the Lois and Mel Tukman Assistant Professor in the Department of Psychology at Cornell University , affiliated with the College of Human Ecology. His research intersects politics and health , focusing on social spillover effects of government actions and adaptation to life changes through computational social science methods. Teaches Data Science for Social Scientists I & II (HD/Psych 2930/2940) Co-teaches graduate course Text and Networks in Social Science Research (HD/Soc/Info 6610, Govt 6619) Research strengths include causal inference , representative sampling , and machine learning applications for small training sets. His work has been featured in The Atlantic , Science Magazine , and other major outlets. Current Data Science Lab projects analyze: Political polarization in social media Health behavior networks Government policy feedback Content moderation systems Lab hires Cornell undergraduates with R/Python experience for data management tasks through HD 4010 research credit.
Julian Fierrez is a Full Professor at the School of Engineering, Universidad Autonoma de Madrid. With an h-index of 74 and over 20,000 citations, his work spans biometrics, signal/image processing, artificial intelligence, and human-computer interaction. Key research areas include: Biometric anti-spoofing and DeepFakes detection Mobile and behavioral biometrics Bias/fairness in AI systems Biometric applications in e-health and education Security in multimodal biometric systems His recent publications show strong focus on deep learning applications for biometric security, with specific subfields including fake detection, keystroke authentication, facial analysis for Parkinson detection, and privacy-preserving AI. He serves as Associate Editor for multiple IEEE and Elsevier journals. Scientific distinctions include: IAPR Young Biometrics Investigator Award (2017) Miguel Catalan Award to Best Researcher under 40 (2017) EURASIP Best PhD Award (2012) EBF European Biometric Industry Award (2006) Prof. Fierrez leads the BiDA Lab and supervises students like Ruben Tolosana and Aythami Morales. Current projects include BBforTAI (Biometrics and Behavior for Unbiased & Trustworthy AI) and PRIMA (Privacy Matters). He also contributes to standardization efforts in biometric evaluation.
