جست‌وجوی استاد و پژوهشگر

Hoa Khanh Damمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Software Analytics
• +۴ مورد دیگر

Hoa Khanh Dam is Professor and Deputy Head of School (Research) & Head of Postgraduate Studies in the School of Computing and Information Technology at the University of Wollongong, Australia. He serves as Co-Director of the Decision System Lab where he leads research at the intersection of Software Engineering and Artificial Intelligence. His research focuses on developing AI-driven solutions for software quality, cybersecurity, and productivity enhancement. Key interest areas include: AI/IoT autonomous and cyber resilient systems Software Analytics and Mining Software Repositories Large Language Models for software engineering tasks Defect prediction and vulnerability analysis Agile project management optimization Analysis of Dam's 12 publications from 2018-2025 reveals consistent application of machine learning to software engineering challenges. His work shows progressive evolution from traditional ML techniques toward LLM-based frameworks, with major contributions in defect prediction (DeepJIT), vulnerability analysis, microservice recommendation, and agile effort estimation. The research demonstrates strong industry relevance through practical implementations in code review, component prediction, and security systems. Dam co-leads the Decision System Lab at UOW, which develops intelligent decision support systems using AI and data analytics. The lab's work bridges theoretical AI advancements with real-world software engineering applications, particularly in cybersecurity and autonomous systems development.

• Machine Learning
• Health Informatics
• Disease Surveillance
• +۳ مورد دیگر

Atiye Sadat Hashemi is a Research Fellow at the Academy of Information Technology, Halmstad University. Her work bridges machine learning and healthcare innovation, with a focus on developing secure and interpretable AI models for medical applications. She maintains an active research profile through publications and collaborative projects in data-driven healthcare solutions. Research Focus: Her expertise spans machine learning applications in disease surveillance, precision medicine, and adversarial robustness. Key areas include: Optimizing ML for real-time disease outbreak detection using anomaly detection Advancing personalized treatment strategies through data-driven models Enhancing model security against adversarial attacks in critical domains Developing privacy-preserving synthetic health data generation techniques Publication Trends (2022-2024): Her recent works demonstrate a consistent focus on healthcare-AI integration, with emphasis on: Anomaly detection systems for epidemiological monitoring Privacy-enhancing technologies for medical data Explainable AI methods for clinical decision support Robustness improvements for safety-critical ML applications Methodologies frequently involve generative adversarial networks, graph neural networks, and time-series analysis.

Sofia Serranoمشاهده پروفایل

استادیار

• Natural Language Processing
• Computational Models
• Explainable AI
• +۱ مورد دیگر

Sofia Serrano is an Assistant Professor of Computer Science at Lafayette College, joining the faculty as one of the newest members in fall 2024. Her research focuses on: Natural Language Processing (NLP) Analyzing computational models used in the field Explaining large language models in ways that are useful and understandable to humans Dr. Serrano teaches CS 420: Artificial Intelligence and CS 105: Digital Media. She is known for her energetic teaching style, commitment to inclusive classrooms, and belief that NLP models must be understandable to be maximally beneficial to society. Her research direction has evolved as NLP technology has advanced, shifting from modeling human thought processes through language to making complex AI systems interpretable for human users.

Mengdi Huaiمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Trustworthy AI
• +۴ مورد دیگر

Mengdi Huai is an Assistant Professor in the Department of Computer Science at Iowa State University. She earned her Ph.D. in Computer Science from the University of Virginia under Prof. Aidong Zhang. Her research focuses on trustworthy AI, including explainable machine learning, adversarial robustness, privacy preservation, and fairness. She holds grants such as an NSF award for security in machine unlearning and has been recognized with the AAAI New Faculty Highlights (2024), Rising Star awards in EECS and Data Science (2021), and the John A. Stankovic Research Award (2021). Her educational background includes a Ph.D. from UVA (2021) and earlier degrees from institutions like the University of Science and Technology of China. She teaches courses like Machine Learning (COM S 573) and Advanced Topics in Computational Intelligence (COM S 672). Dr. Huai’s research spans security/privacy in machine unlearning, adversarial attacks on diffusion models, and attention mechanisms in vision transformers. Her work has been published in top venues like ICML, AAAI, KDD, and NeurIPS. She serves on program committees for AAAI, IJCAI, CVPR, and others, and reviews for journals like IEEE Transactions on Neural Networks and TKDE. Her lab focuses on advancing AI systems that are robust, interpretable, and privacy-aware. Recent grants include NSF support for machine unlearning security. She advises numerous students in these areas and collaborates internationally on projects like predictive diffusion models for healthcare and federated learning robustness.

Andria Procopiouمشاهده پروفایل

مدرس

• Artificial Intelligence
• Cybersecurity
• Sports Analytics
• +۴ مورد دیگر

Dr. Andria Procopiou is a Lecturer in Artificial Intelligence and Cybersecurity at the University of Central Lancashire Cyprus, serving as Course Leader for the MSc Computing program and Deputy Course Leader for the MSc Cybersecurity. She holds a PhD in Computer Science (City, University of London, 2022), an MSc in Human-Computer Interaction (University College London, 2014), and a BSc in Computer Science (University of Surrey, 2013). Her research spans AI applications in sports analytics, cybersecurity for smart cities/IoT, hate speech detection, and metaverse safety. She has led modules in User Experience Design, Penetration Testing, and Cybersecurity. Research interests include AI-driven solutions for sports injury prediction, real-time DDoS detection in IoT networks, and ethical challenges in the metaverse. Her work emphasizes explainable AI (XAI) for transparency in decision-making systems. She has published extensively in journals like Wireless Communications and Mobile Computing and conferences such as IEEE CYBER and the International Conference on Human-Computer Interaction. Education: PhD Computer Science (City University London), MSc HCI (UCL), BSc Computer Science (University of Surrey) Key Roles: Course Leader (MSc Computing), Deputy Course Leader (MSc Cybersecurity) Administrative Contributions: Module leadership in AI, Cybersecurity, and Digital Forensics Her recent publications address critical issues like privacy concerns in smart campuses, gender-based hate speech in virtual spaces, and injury patterns in professional football using data analytics. She collaborates with industry on IoT security solutions and metaverse governance frameworks.

Albert Gattمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Albert Gatt is a Professor of Natural Language Processing at Utrecht University's Department of Information and Computing Sciences, where he also serves as Programme Director for AI & Data Science. He holds an Associate Professor position (on leave) at the University of Malta's Institute of Linguistics and Language Technology. His research focuses on Natural Language Generation (NLG), multimodal models, and under-resourced language support, particularly for Maltese. He leads projects like NL4XAI and MASRI, addressing challenges in explainable AI and speech recognition. Education: Advanced degrees in computational linguistics and AI (not explicitly detailed in text). Key Projects: Multilingual NLG, Vision-Language benchmarks, Maltese ASR, and NLP evaluation methodologies. Research interests span data-to-text generation, vision-language interfaces, and evaluation practices. His work bridges computational linguistics with cognitive science, emphasizing human-AI collaboration. Notable contributions include the TUNA corpus, SimpleNLG toolkit, and foundational studies on referring expression generation. Publications (2025-2024) explore robust fine-tuning, LLM evaluation, and visual-linguistic grounding. Collaborations span academia and industry, addressing ethical AI and language equity. Supervises a global team of researchers and PhD students across multiple institutions, fostering innovation in NLG, multimodal AI, and Maltese language tech.

Eleanor Millمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Fraud Detection
• Data Science
• +۳ مورد دیگر

Dr. Eleanor Mill is a Postgraduate Research Student in the Department of Business Analytics and Operations at the University of Surrey. She serves on the University Ethics Committee and focuses her research on human-AI collaboration in fraud strategy, emphasizing transparency in financial decision-making and ethical AI applications. Her work bridges technical AI advancements with societal and regulatory imperatives. Education: BSc Mathematics, University of East Anglia (1996) MSc Business Analytics, University of Surrey (2015) Research Interests: Eleanor’s research centers on explainable artificial intelligence (XAI), fraud detection, and AI sustainability. She develops frameworks like SAGE to enhance context-aware explanations for AI models and explores paradox theory to address ethical and environmental tensions in AI systems. Her work prioritizes real-world efficacy and human oversight in high-stakes decision-making environments. Publications: Eleanor has published extensively on XAI applications, fraud detection methodologies, and AI’s societal impacts. Recent work includes the SAGE framework for context-sensitive explanations and analyses of AI’s energy-accuracy paradox. Advising & Grants: Supervised by Wolfgang Garn and Nick Ryman-Tubb, Eleanor’s research is supported by Surrey’s Business Analytics group. No grant details are explicitly listed in the text.

Kenneth Marinoمشاهده پروفایل

استادیار

• Computer Vision
• Natural Language Processing
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Kenneth Marino is a Research Scientist at DeepMind , set to join the University of Utah as an Assistant Professor at the Kahlert School of Computing in Fall 2025. He earned his PhD in Machine Learning from Carnegie Mellon University (funded by NDSEG and NSF GRFP fellowships) and completed his undergraduate studies in Computer Engineering with a minor in Computer Science at Georgia Tech . Dr. Marino's research focuses on the intersection of Computer Vision , Natural Language Processing , and Reinforcement Learning , with emphasis on: Multimodal agents operating on the web, in simulation, and in real-world environments Evaluating AI systems and creating high-impact datasets (e.g., A-OKVQA , OK-VQA ) Incorporating semantic knowledge into end-to-end learning frameworks Embodied reasoning through language-guided planners and neural reporters Language model distillation for agent supervision Human-AI collaboration to improve RL generalization His work has been published in top venues including ICML , NeurIPS , ECCV , CVPR , and ICLR , with recent trends emphasizing: Relational reasoning in LLMs Object-centric world modeling Continual learning for embodied agents Reporter neural networks for agent control Legal AI benchmarking (e.g., BriefMe ) Knowledge graph integration for visual classification Scientific recognition includes: NDSEG Fellowship NSF GRFP Fellowship He has served on the MLD PhD Admissions Committee and as an Area Chair for ECCV 2024 , with prior teaching experience at Columbia University (COMS 6998) and Carnegie Mellon University (16-824, 10-401).

Furui Chengمشاهده پروفایل

پژوهشگر ارشد

• Human-Centered AI
• Explainable Machine Learning
• Visual Analytics
• +۳ مورد دیگر

Furui Cheng is a Research Fellow at ETH Zürich's Department of Computer Science, affiliated with the IVIA Lab under Prof. Menna El-Assady. His research focuses on Human-Centered AI, combining data visualization with explainable machine learning to enhance decision-making. He holds a PhD from the Hong Kong University of Science and Technology (2022), advised by Prof. Huamin Qu. Education: PhD in Computer Science (HKUST, 2022); Previous academic background includes studies emphasizing visualization and machine learning integration. Research interests span LLM transparency, interactive ML frameworks, and healthcare AI applications. Notable projects include Polyphony (single-cell data analysis), VBridge (healthcare model explanations), and DECE (counterfactual exploration systems). His work has received awards such as the Best Paper Honorable Mention (VIS'21) and Best Abstract Award (BioVis@ISMB). Key contributions include advancing model interpretability through visual analytics, with applications in clinical decision-making and genomics. Ongoing efforts focus on aligning AI systems with user knowledge and ensuring trustworthy decision processes.

Enayat Rajabiمشاهده پروفایل

دانشیار

• Machine Learning
• Knowledge Graphs
• Data Analytics
• +۳ مورد دیگر

Dr. Enayat Rajabi is an Associate Professor of Business Analytics at the Shannon School of Business, Cape Breton University. Holding a PhD in Information and Knowledge Engineering from the University of Alcala (Spain) and a postdoctoral fellowship from Dalhousie University, his research focuses on the intersection of Machine Learning, Knowledge Graphs, and Data Analytics. He actively applies these technologies in healthcare, smart cities, and social media crisis response contexts. Education PhD in Information and Knowledge Engineering, University of Alcala (Spain) Postdoctoral Fellowship, Dalhousie University Research Interests His work bridges Knowledge Graphs with Machine Learning, emphasizing explainability and practical applications. Key areas include: Explainable AI for clinical decision-making Knowledge Graph applications in healthcare systems Social media analytics for emergency response Smart city data integration Generative modeling for tabular data Recent Publications Trends Recent articles highlight: Explainable AI in healthcare settings Industrial breakdown prediction systems Social media influencer detection Smart city infrastructure modeling Advanced data synthesis techniques Continued focus on Knowledge Graph applications

Pascal Hitzlerمشاهده پروفایل

استاد

• Neuro-Symbolic Artificial Intelligence
• Semantic Web
• Knowledge Graphs
• +۴ مورد دیگر

Pascal Hitzler is the University Distinguished Professor and endowed Lloyd T. Smith Creativity in Engineering Chair at Kansas State University's Department of Computer Science within the Carl R. Ice College of Engineering. He serves as Director of the Center for Artificial Intelligence and Data Science (CAIDS) and Director (Research) of the Institute for Digital Agriculture and Advanced Analytics (ID3A). Previously, he held roles at Wright State University, including NCR Distinguished Professor and Director of Data Science. His academic journey includes a PhD in Mathematics from University College Cork (2001) and a Diplom in Mathematics from the University of Tübingen (1998). Affiliations: Kansas State University (2020–present), Wright State University (2009–2020), University of Karlsruhe (2004–2009), TU Dresden (2001–2004). Education: PhD in Mathematics (2001, UCC Cork), Diplom in Mathematics (1998, Tübingen). His research focuses on neuro-symbolic AI, semantic web technologies, knowledge graphs, and ontology modeling. Key contributions include founding editorships of the Neurosymbolic Artificial Intelligence and Semantic Web journals, co-authoring the Foundations of Semantic Web Technologies textbook, and pioneering ontology design patterns. His work emphasizes semantic web standards like OWL and integrates explainable AI with deep learning. Recent articles explore neuro-symbolic integration, geospatial knowledge graphs (KnowWhereGraph), and explainable AI through concept induction. He has received awards such as the SWSA Ten-Year Award (2020) and is a highly cited researcher in artificial intelligence. Grants and collaborations span digital agriculture, cybersecurity (K-CaTS), and interdisciplinary initiatives like the NSF-funded Global Food Systems Datahub. His DaSe Lab develops advanced semantic technologies, with projects including the Enslaved.org hub ontology and crisis management frameworks (HIP Ontology). Labs/Teams: Director of the DaSe Lab for Data Semantics, leading projects on knowledge graphs, neuro-symbolic AI, and geospatial data integration. Collaborates with institutions like the University of Notre Dame, Jönköping University, and IOS Press.

• Machine Learning
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization
• +۳ مورد دیگر

Janek Thomas is a researcher actively contributing to the fields of Machine Learning and Automated Machine Learning (AutoML). His work focuses on hyperparameter optimization, multi-objective algorithms, and improving the interpretability of machine learning models. Collaborating with institutions like TU Munich and LMU Munich, he has authored over 35 publications since 2016. Key contributions include the AMLB benchmark suite for AutoML systems and foundational research on multi-objective hyperparameter tuning. His research bridges theoretical advancements with industrial applications, emphasizing robust model verification and scalable optimization techniques.

• Artificial Intelligence
• Argumentation Theory
• Explainable AI (XAI)
• +۵ مورد دیگر

Professor Francesca Toni is a Professor in Computational Logic at the Department of Computing, Faculty of Engineering at Imperial College London. She leads research in Artificial Intelligence, focusing on explainable AI (XAI), argumentation theory, and neuro-symbolic systems. Her affiliations include the Centre for eXplainable AI (XAI), Argumentation-based Deep Interactive eXplanations (ADIX), and the Human-Like Computing initiative. Her work integrates computational logic with machine learning to develop interpretable models for healthcare, robotics, and decision support systems. Recent research emphasizes conflict analysis in neural networks, argumentative ensembling, and object-centric learning frameworks. Her research interests span AI ethics, formal argumentation, and the integration of symbolic reasoning with deep learning. Key contributions include neuro-argumentative learning architectures, benchmarking explainability methods (XAI-Units), and frameworks for robust recourse in model multiplicity scenarios. She actively explores applications in biomedical fraud detection (Pub-Guard-LLM) and personalized decision support via gradual bipolar argumentation. Her publications highlight trends in explainable AI, with a focus on visual debates, counterfactual explanations, and causal structure learning. She has pioneered systems like ProtoArgNet for interpretable image classification and DR-HAI for dialectical reconciliation in human-AI interactions. Her work bridges theoretical foundations (e.g., ABA semantics) with real-world applications in healthcare and legal reasoning. Notable projects include ROAD2H—an open-source XAI approach for managing comorbidities—and Cafe for conflict-aware feature explanations. She has contributed to legal AI systems (LawGIBA) and causal discovery methods. Current efforts focus on neuro-argumentative machine learning and object-centric representation learning.

Lu Fengمشاهده پروفایل

دانشیار

• Cyber-Physical Systems
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Lu Feng is an Associate Professor of Computer Science at the University of Virginia, affiliated with the Link Lab, a center specializing in Cyber-Physical Systems (CPS). She holds a Ph.D. in Computer Science from the University of Oxford. Her research focuses on ensuring safety and trustworthiness in CPS, with applications in medical devices, autonomous robotics, and smart cities. She has received prestigious awards including the NSF CRII Award (2018) and NSF CAREER Award (2020). Her work integrates formal methods, AI, and robotics to address challenges in CPS assurance and human-machine collaboration. Notable contributions include developing risk-assessment tools for heart failure patients, predictive monitoring frameworks for CPS, and trust-aware planning algorithms for autonomous systems. She has pioneered frameworks like DP-RuL for clinical decision support systems and IrrMap for precision agriculture. Her research bridges theoretical foundations (e.g., model checking, reinforcement learning) with practical applications in healthcare, transportation, and urban systems. She collaborates across disciplines, contributing to initiatives like the Link Lab’s smart city simulations and safety-critical medical CPS assurance. Education: Ph.D., Computer Science, University of Oxford Awards: NSF CRII (2018), NSF CAREER (2020) Labs: Link Lab (Cyber-Physical Systems Center) Focus Areas: Runtime safety, human-AI trust, medical device assurance, smart city systems

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +۹ مورد دیگر

Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications