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Eduardo AlonsoView profile

Professor

• Artificial Intelligence
• Reinforcement Learning
• Computational Neuroscience
• +2 more

Professor Eduardo Alonso is Director of the Artificial Intelligence Research Centre (CitAI) and Department Research Director at City St George's, University of London. His research bridges novel AI techniques with Explainable AI and Artificial General Intelligence, with significant focus on legal and ethical implications. Research spans: Computational neuroscience and evolutionary biology modeling Deep learning architectures for reinforcement learning Mathematical models of emergence in complex systems Industrial AI applications with societal impact Professor Alonso has secured over £1M in funding from Innovate UK, EU EIT-Digital, and US NSF grants. He currently supervises 13 PhD students working on ethical AI, reinforcement learning, and cybersecurity. Recent publications focus on transformer architectures for multi-agent systems, power grid optimization via GNNs, and adversarial robustness in security systems. Awarded the IEEE Computational Intelligence Society Spotlight Paper Award in 2013.

Marinko SarunicView profile

Adjunct Professor

• Biomedical Imaging
• Optical Coherence Tomography
• Microscopy
• +2 more

Marinko Sarunic is an Adjunct Professor at the School of Engineering Science , Simon Fraser University . He holds a PhD in Biomedical Engineering from Duke University and has been recognized as a Michael Smith Foundation for Health Research Scholar . His research focuses on biomedical imaging , particularly optical coherence tomography (OCT) , microscopy , and low-coherence interferometry , with applications in diabetic retinopathy , Alzheimer’s disease , and age-related macular degeneration . Dr. Sarunic's work spans adaptive optics , deep learning , and sensorless OCT systems , emphasizing clinical translation and open-source software development (e.g., OCTAVA ). His Google Scholar publications highlight multimodal imaging , vascular heterogeneity analysis , and AI-driven diagnostics for retinal diseases. His contributions include the Michael Smith Foundation for Health Research Scholar award. Though not currently teaching courses, his collaborations and leadership in retinal imaging and medical device innovation are pivotal for advancing non-invasive diagnostics in neurodegenerative and diabetic conditions .

• Statistical Relational Learning
• Machine Learning
• Probabilistic Graphical Models
• +5 more

Lise Getoor is a Professor in the Department of Computer Science at the University of California, Santa Cruz, within the Baskin School of Engineering. She previously held positions at the University of Maryland, College Park and has established herself as a leading researcher in statistical relational learning and neuro-symbolic artificial intelligence. Her research focuses on developing principled approaches to reasoning and learning with rich, relational data. She has made significant contributions to probabilistic soft logic, statistical relational learning, knowledge graph construction, and the integration of symbolic and neural approaches to AI. Her work bridges theoretical foundations with practical applications in areas including social network analysis, sustainable recommendations, cyberbullying detection, and fair machine learning systems. Analysis of her recent publications reveals a strong trend toward neuro-symbolic integration, with increasing focus on combining neural networks with logical reasoning frameworks. Her research demonstrates consistent innovation in developing scalable algorithms for structured prediction across diverse application domains while addressing critical issues of fairness and interpretability in AI systems. Professor Getoor has advised numerous PhD students who have become active contributors to the field, including Connor Pryor, Charles Dickens, Eriq Augustine, and Varun Embar. Her research has been supported by multiple grants from major funding agencies, though specific details aren't provided in the current data. She leads research in the Statistical Relational AI (StaRAI) Lab at UC Santa Cruz, where her team develops frameworks that combine logical reasoning with probabilistic modeling to address complex real-world problems requiring both structured knowledge and statistical learning.

Herna ViktorView profile

Professor

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +7 more

Herna Viktor is a Full Professor and Director of the School of Electrical Engineering and Computer Science at the University of Ottawa. She holds a Ph.D. and has extensive administrative leadership experience within the university's Faculty of Engineering. Research Interests : Dr. Viktor specializes in Artificial Intelligence (AI) with a focus on data-driven discovery across diverse domains. Her work emphasizes applications in healthcare (e.g., medical outcome prediction), cybersecurity (e.g., threat detection via semi-supervised learning), finance (e.g., stock market analysis with temporal Transformers), and computational biology (e.g., protein structure generation using diffusion models). She also explores advanced machine learning techniques like lifelong learning, ensemble methods, and interpretability measures. Her research integrates geometric deep learning, quantum computing, and hybrid models to address complex problems in structural proteomics and molecular dynamics. Recent Articles : Her recent publications (2023–2025) highlight trends in protein generation via diffusion models, adversarial robustness of NLP systems, and financial time series predictions. She consistently bridges theoretical AI advancements with real-world applications in medicine, cybersecurity, and quantum computing. Advising & Grants : Currently supervises Paul Kiyambu Mvula . While no grants are explicitly listed, her research demonstrates significant interdisciplinary impact. Her work on SCUT-DS and DynaQ reflects ongoing engagement with imbalanced data and online learning challenges. Labs/Teams : No specific lab or team is mentioned, but she collaborates on projects involving hybrid quantum neural networks and molecular dynamics simulations.

Benjamin C. M. FungView profile

Professor

• Data Mining
• Artificial Intelligence
• Cybersecurity
• +6 more

Benjamin C. M. Fung is a Full Professor at McGill University's School of Information Studies and holds the Canada Research Chair in Data Mining for Cybersecurity. He is also an Associate Member at the School of Computer Science and affiliated with MILA and the McGill Digital Humanities Initiatives. His research focuses on data mining, AI, cybersecurity, privacy protection, and their applications in crime investigation, health informatics, and smart cities. Education: Ph.D. in Computing Science, Simon Fraser University (2007) M.Sc. and B.Sc. in Computing Science, Simon Fraser University Professional Engineer (P.Eng.) in Software Engineering, Ontario Research Interests: Data mining techniques for cybersecurity, AI ethics, privacy-preserving technologies, malware analysis, and foreign interference detection. His work has been supported by NSERC, SSHRC, DRDC, and FRQNT. Key Contributions: Developed the Kam1n0 tool for malware analysis, featured in Montreal's technological innovations. Co-founded the McGill Data Mining and Security (DMaS) Lab. Authored over 180 refereed publications with an h-index of 62. Awards & Recognition: 2022 IEEE CSR Best Research Paper Award ACM & IEEE Senior Member (2013) Top 2% Global Scientists (2021–present) Media & Public Engagement: Frequently comments on cybersecurity, disinformation, and foreign interference for CBC, CBC Radio, OMNI TV, and international outlets. His op-eds address topics like AI ethics and national security.

Timo SpeithView profile

Lecturer

• Machine Ethics
• Machine Explainability
• Artificial Intelligence
• +2 more

Dr. Timo Speith is a fixed-term Lecturer at the University of Bayreuth's Faculty of Cultural Studies, affiliated with the Professorship for Philosophy, Computer Science and Artificial Intelligence. He holds a B.A. in Philosophy and M.Sc. in Computer Science from Saarland University, where he also completed his doctoral studies in Philosophy. His research focuses on machine ethics, machine explainability (XAI), and non-speculative AI systems, alongside interests in ancient philosophy and the philosophy of science. He has contributed to interdisciplinary work on XAI evaluation methods, fairness in AI systems, and explainable hardware (XHW). Speith has organized workshops on explainable systems requirements engineering and co-authored a manifesto outlining XAI 2.0 challenges. His work bridges philosophy, computer science, and ethics to address AI transparency and accountability. Education: B.A. Philosophy, Saarland University M.Sc. Computer Science, Saarland University Ph.D. in Philosophy, Saarland University (2023 dissertation) Research Interests: Machine Ethics, XAI Methodology, AI Transparency, Philosophy of Science, Ancient Philosophy. Grants and Collaborations: Active in international workshops (e.g., RE4ES), IEEE conferences, and collaborations with institutions like Saarland University and the University of Bayreuth's Philosophy Institute. His work addresses practical and theoretical challenges in AI explainability across hardware and software systems. Lab/Team Affiliation: Associated with the Philosophy, Computer Science, and Artificial Intelligence research group at the University of Bayreuth, focusing on interdisciplinary AI ethics and technical implementation.

Maria De-ArteagaView profile

Assistant Professor

• Algorithmic Fairness
• Human-AI Complementarity
• Machine Learning
• +1 more

Maria De-Arteaga is an Assistant Professor at the Information, Risk and Operation Management Department within the McCombs School of Business at the University of Texas at Austin. She holds joint appointments as a core faculty member in the Machine Learning Laboratory and as a researcher for Good Systems. She earned her PhD in Machine Learning and Public Policy from Carnegie Mellon University. Her research focuses on algorithmic fairness, human-AI complementarity, and the societal impacts of machine learning. Key areas include characterizing how historical biases are reproduced in ML systems, developing bias mitigation techniques, and improving human-AI collaboration frameworks. Her work bridges technical ML methods with public policy considerations. Recent publications demonstrate trends in fair decision-making systems, including studies on label indeterminacy problems, expert consistency modeling, and toxicity detection. Her interdisciplinary work spans healthcare, social computing, and human-AI interaction. She advises multiple graduate students including Terry Neumann (IROM), Yunyi Li (IROM), and Soumyajit Gupta (CS), along with undergraduate student Riya Cyriac. Previously advised researchers include postdoc Jakob Schoeffer and undergraduate Jennifer Mickel.

Mateo Espinosa ZarlengaView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Explainable AI
• +3 more

Mateo Espinosa Zarlenga is a Lecturer in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on Explainable Artificial Intelligence (XAI), interpretable deep learning architectures, and the application of these techniques in healthcare. He explores concepts such as concept-based explainability, representation learning, and the mitigation of bias in AI systems. His work emphasizes the design of learning algorithms that produce human-understandable explanations and their practical deployment in critical domains. He has supervised multiple MPhil/Part III projects, including studies on concept intervention policies, iterative concept discovery, and the ethical implications of AI systems. His research has been recognized through prestigious awards, including a Best Paper Award nomination at ECCV 2024 and spotlight presentations at NeurIPS workshops and AAAI conferences. Mateo collaborates with experts in healthcare and computational biology to advance interpretable AI models for real-world applications. Key achievements include the development of Concept Embedding Models (CEMs) and TabCBM, which bridge the gap between model accuracy and explainability, particularly in tabular data. His work often involves designing interventions to improve model performance through expert feedback, as seen in projects addressing concept leakage and bias mitigation without privileged information.

• Traditional Chinese Medicine (TCM)
• Data Mining
• Medical Informatics
• +4 more

Associate Professor Josiah Poon is affiliated with the School of Computer Science at the University of Sydney. His research focuses on applying data mining and IT techniques to Traditional Chinese Medicine (TCM), particularly analyzing herbal combinations for effective treatments. He collaborates with institutions in China to improve TCM evidence and has contributed to clinical data analysis, medical informatics, and multimodal AI systems. Teaching includes courses such as INFO1003 (Foundations of IT) and INFO9003 (IT for Health Professionals). Current research students are Rina CABRAL (Multimodality Representation), Yan LI (Long Document Comprehension), and Xiaobin LU (Financial Decisions). Research highlights include developing algorithms to quantify TCM efficacy, analyzing complementarity in herbal combinations, and applying machine learning to healthcare data. Notable projects include a randomized controlled trial on pneumococcal vaccination (2021) and a Google-funded multimodal health detection system (2020). Key areas of expertise span TCM informatics, medical data analytics, and AI-driven healthcare solutions. His work bridges Eastern/Western medicine through computational methods, emphasizing evidence-based practices in TCM.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.

Lorenz LinhardtView profile

Researcher

• Explainable AI
• Robust Machine Learning
• Machine Learning for Medical Applications
• +3 more

Lorenz Linhardt is a Researcher and PhD candidate at the Machine Learning Group of Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on robustness of deep neural networks, spurious correlations, and representation learning, with applications in explainable AI and medical domains. Educational Background: M.Sc. in Computer Science, 2019 – ETH Zürich B.Sc. in Computer Science, 2016 – University of Vienna Research Interests: Robust Machine Learning : Addressing vulnerabilities in neural networks caused by spurious correlations and adversarial examples. Human Alignment : Bridging gaps between AI outputs and human judgments via representation learning and similarity metrics. Medical Applications : Leveraging machine learning for counterfactual inference in healthcare and dose-response modeling. Article Trends: Recent work emphasizes alignment between human judgments and AI systems, with studies on latent diffusion models, adversarial robustness, and forensic analysis of malware classifiers. Earlier contributions span astronomy surveys and decision tree optimization. Labs/Teams: Active in BIFOLD and the TU Berlin Machine Learning Group, focusing on foundational AI research.

Marina FiedlerView profile

Professor

• Artificial Intelligence in Organizations
• Information Systems and Organizational Behavior
• Sustainable Management Practices
• +3 more

Prof. Dr. Marina Fiedler holds the Chair of Management, People and Information at the University of Passau since 2010. She is an Associate Editor at the Schmalenbach Journal of Business Research (SBUR) and heads the Passau experimental laboratory PAULA . Her academic journey includes a PhD (2004) and habilitation (2008) from the University of Munich, with visiting professorships at Corvinus University and State University of Management.

Lydia Bouzar-BenlabiodView profile

Assistant Professor

• Hardware-based Machine Learning
• Privacy-Preserving Machine Learning
• Explainable AI
• +3 more

Dr. Lydia Bouzar-Benlabiod is an Assistant Professor at the Jodrey School of Computer Science, Acadia University, Nova Scotia, Canada. Her research focuses on hardware-based machine learning, privacy-preserving techniques, and explainable AI. She holds a Ph.D. in Computer Science from Université d’Artois, France, and an M.Eng from Ecole nationale Supérieure d’Information (ESI), Algiers, Algeria. Her work bridges theoretical advancements with practical applications in cybersecurity, medical imaging, and intelligent systems. Research interests include adversarial machine learning, neural architecture search, and AI-driven healthcare solutions. Her lab explores cost-effective hardware implementations for medical diagnostics and anomaly detection systems. Key projects involve NeuroMem® chip integration for breast cancer detection and RNN-VED models to reduce false positives in cybersecurity. Dr. Bouzar-Benlabiod’s publications span 2013–2024, emphasizing data-driven modeling, case-based reasoning, and AI ethics. She contributes to special issues on heuristic data science and reuse in intelligent systems. Her work is accessible via CILS Lab and personal page .

• Artificial Intelligence
• Machine Learning
• Spatial Reasoning
• +3 more

Dr. Paulo Santos is a Senior Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence with a focus on explainable AI systems. He holds a PhD from Imperial College London (2003) and has over 20 years of research experience in spatial reasoning, machine learning, and robotics. His work bridges knowledge representation with deep learning to enhance transparency in AI decision-making. Dr. Santos has led research groups in Brazil, collaborated internationally, and secured funding from organizations like the British Council and EU. His expertise spans robotics, computer vision, and cognitive science. Notable achievements include the British Computer Science Machine Intelligence Prize (2004) and the Santander Prize for Science and Innovation (2006). Research interests include reinforcement learning for autonomous underwater vehicles (AUVs), scene graph generation in computer vision, and spatial reasoning for multi-robot systems. Recent work emphasizes sim-to-real transfer learning and fault recovery in underwater robotics.

David TowersView profile

Professor

• Optical Sensing Technologies
• Fluid Mechanics
• Structural Assessment
• +6 more

Professor David Towers is the Head of the School of Engineering at the University of Warwick and holds the position of Professor of Mechanical Engineering. With over 30 years of combined academic and industry experience, he leads a dynamic research group focused on optical sensing technologies, fluid mechanics, and structural assessment. His work bridges engineering, biology, and clinical practice, with significant contributions to malaria control strategies through mosquito behavior analysis. Education: BSc (1st Class) in Mechanical Engineering Science from the University of Warwick PhD in Optical Engineering from the University of Warwick Royal Society Fellowship at ETH Zürich Research Interests: Professor Towers’ research spans three core areas: fluid mechanics (turbulent flows, sprays, multi-phase mixing), structural assessment (optical interferometry for deformation and stress measurement), and clinical optical systems (mosquito tracking for malaria intervention). His multi-disciplinary approach emphasizes collaboration with end-users, particularly in developing novel optical instruments for industrial and healthcare applications. Grants & Projects: AI for Mosquito Trajectory Understanding (£X, 2022–Present) Bill & Melinda Gates Foundation: Next-Gen LLIN Development (£X, 2019–2023) MRC: Indoor Mosquito Behavior Mapping (£X, 2019–2022) Awards: Athena SWAN Silver Award (2021) for promoting gender equality in STEM. Labs & Teams: Leads the Applied Optics Laboratory and collaborates with the Vector Control Research Group, focusing on AI-driven solutions for vector-borne disease control.