جست‌وجوی استاد و پژوهشگر

Natthakan Iam-Onمشاهده پروفایل

مدرس

• Machine Learning
• Data Clustering
• Computer Vision
• +۴ مورد دیگر

Natthakan Iam-On is a Lecturer in the Department of Computer Science at Aberystwyth University. She specializes in machine learning, data clustering, and their applications in cybersecurity, astronomical data analysis, and healthcare informatics. Her research focuses on developing advanced algorithms for fraud detection, transient event detection in astronomy, and improving data imputation techniques. Her work spans interdisciplinary collaborations, including projects on privacy-preserving data fusion for social media analysis and cultural heritage documentation using convolutional neural networks. She also explores cybersecurity through fuzzy logic-based attack tree analysis and has contributed to enhancing AI-driven solutions for food safety in UK-Vietnam collaborations. Recent publications highlight her focus on applying machine learning to solve complex problems such as imbalance data classification, transient event detection in sky surveys, and optimizing clustering algorithms for gene expression analysis. Her research trends emphasize interdisciplinary applications, including medical decision support systems and improving fraud detection in banking sectors. She has been a Co-Investigator in a British Council-funded project (2025–2026) to strengthen UK-Vietnam food safety research through AI and data science. Her collaborative networks include Trenten Roberts and Teerapong Boongoen, extending to international partnerships in computational intelligence and data-driven solutions.

• Machine Learning
• Data Mining
• Big Data
• +۶ مورد دیگر

Mehrdad Almasi is affiliated with the University of Luxembourg and Tarbiat Modares University, Tehran, Iran. His research focuses on Machine Learning , Data Mining , and Big Data applications. Key Research Areas : Associative classification, sentiment analysis, and scalable data processing. Technical Domains : Pattern recognition, distributed computing, and Android malware detection. Published Work : Collaborated on graphical models for database joins, large-scale classification systems, and multi-objective evolutionary algorithms.

Xi Tongمشاهده پروفایل

پژوهشگر ارشد

• Relativity and Gravitation
• Machine Learning Applications
• Theoretical Physics
• +۳ مورد دیگر

Dr. Xi Tong is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) within the University of Cambridge's Faculty of Mathematics. His research focuses on two primary areas: Relativity and Gravitation, with contributions to black hole dynamics and cosmological observables, and Machine Learning Applications, particularly in healthcare diagnostics and urban infrastructure analysis. He is affiliated with the Relativity and Gravitation research group at DAMTP. Dr. Tong's work bridges theoretical physics and applied machine learning. In theoretical physics, he explores phenomena like black hole superradiance termination and cosmological parameter mapping. In machine learning, he develops uncertainty-aware models for healthcare diagnostics, federated learning frameworks for imbalanced data, and geospatial analysis tools for urban infrastructure. HIGHLIGHTED RESEARCH THEMES: Cosmological simulations and gravitational wave dynamics Audio-based disease detection (e.g., respiratory and cardiac health) Federated learning for privacy-preserving healthcare analytics Urban mobility modeling through spatiotemporal data PUBLICATIONS HIGHLIGHTS: Pioneering work on black hole superradiance modulation (2024-2025) Development of RespLLM for respiratory health prediction (2024) FLea framework for federated learning optimization (2024) GUI dataset for global urban infrastructure analysis (2024) LAB AFFILIATION: Active contributor to DAMTP's Relativity and Gravitation group, collaborating on gravitational physics and astrophysical simulations.

Eric Brownمشاهده پروفایل

عضو هیئت علمی

• Data Science
• Operations Research
• Power Systems Optimization
• +۳ مورد دیگر

Eric Brown is an Assistant Professor of Practice and Director of the Undergraduate Program in the Department of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University. His academic roles include teaching undergraduate and graduate courses in data science, information systems, and operations management. Dr. Brown holds a PhD in Industrial Engineering from Texas A&M University (2020), where his dissertation focused on efficient algorithms for contingency response in electric power systems. He also earned an MEng in Industrial Engineering from Texas A&M (2011) and a BS in Mathematics from Abilene Christian University (2008). His research interests are centered on the intersection of optimization and machine learning, with applications in power systems and medical treatment. Specifically, he focuses on: Data mining theory, particularly imbalanced data classification and medical applications Mathematical modeling of linear and nonlinear methods in power systems optimization Development of algorithms for discrete nonlinear optimization His recent publications (2018-2023) emphasize advancements in power systems optimization and medical data science. Key contributions include developing heuristic methods for post-contingency power flow violations and undersampling techniques for imbalanced medical datasets. He has received the DHS HS-STEM Career Development Grant Fellowship (2010-2011), supporting his research in Homeland Security-related fields. In teaching, he contributes to courses in data science and operations research. His research has been supported by grants such as the DHS fellowship. He collaborates with institutions like the National Security Agency and Teledyne Brown Engineering through past professional roles.

Marcos Zampieriمشاهده پروفایل

استادیار

• Computational Linguistics
• Natural Language Processing (NLP)
• Machine Learning
• +۷ مورد دیگر

Marcos Zampieri is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's School of Computing. He holds a PhD from Saarland University, where he was affiliated with the German Research Center for Artificial Intelligence (DFKI). His research focuses on computational linguistics and natural language processing (NLP), emphasizing robust NLP system development through large-scale text analysis from diverse sources like social media and news. Key research areas include multilingual benchmarking for code generation (e.g., mHumanEval), offensive language detection across languages, and lexical simplification (ALEXSIS-PT). He has co-edited influential volumes such as Similar Languages, Varieties, and Dialects: A Computational Perspective . Over 100 peer-reviewed publications and multiple conference proceedings underscore his prolific output. Recent work explores ethical implications of AI in education, cross-lingual NLP challenges, and leveraging large language models (LLMs) for tasks like mental health analysis and educational tools. Collaborations include projects on code-mixed emotion detection (EmoMix-3L) and privacy-preserving offensive language identification via federated learning. Zampieri advises on shared tasks like the BEA 2024 Lexical Simplification Pipeline and leads workshops such as VarDial. His datasets (e.g., Sold for Sinhala offensive language) and benchmark frameworks have become industry standards. Despite no explicitly listed grants or labs, his work spans interdisciplinary collaborations in computing, linguistics, and healthcare.

Bernardo Canovas Seguraمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Medical Informatics
• Data Modeling
• +۴ مورد دیگر

Bernardo Canovas Segura is a Researcher at the Department of Computer Science and Systems Engineering, Faculty of Informatics, University of Murcia. He holds a PhD in Computer Science from the same institution (2019), focusing on medical informatics approaches for decision support in antimicrobial stewardship, supervised by Dr. Manuel Campos Martínez and Dr. Jose M. Juarez. His research group, AIKE: Artificial Intelligence and Knowledge Engineering, focuses on AI applications in healthcare, particularly in infection control and antimicrobial resistance management. Key areas of research include spatial-temporal modeling for epidemiological data, clinical decision support systems, and visual analytics for hospital infection outbreaks. He contributed to developing tools like OBViz (a visualization tool for infection analysis) and GRALENIA (an AI-driven system for antimicrobial resistance surveillance). His work integrates machine learning, graph databases, and subgroup discovery for patient phenotyping and predictive modeling. Notable projects include modeling Clostridioides difficile spread in hospitals, analyzing seasonality impacts on infection predictions, and fusing AI with semantic web technologies for antibiotic stewardship. He has collaborated on agent-based and epidemiological hybrid models for infection spread simulation. His research emphasizes interpretable models for high-dimensional clinical data and interdisciplinary approaches to healthcare challenges. Labs/Teams: Member of AIKE research group and previously affiliated with Applied Engineering group. Active in developing visualization tools and decision support frameworks for hospitals. Future directions include advancing real-time outbreak detection systems and AI-driven antimicrobial management strategies.

Antonio Guillen Teruelمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Data Analysis
• +۳ مورد دیگر

Antonio Guillen Teruel is a Researcher at the University of Murcia , affiliated with the Department of Information and Communication Engineering within the Faculty of Informatics . His research focuses on Artificial Intelligence , Machine Learning , and their applications in Environmental Modeling and Health Informatics . He is part of the Intelligent Systems and Telematics research group. Key research interests include AI-driven solutions for streamflow prediction, long-term health outcomes analysis (e.g., long COVID), and addressing imbalanced datasets in machine learning. Recent work highlights include developing frameworks for imbalanced learning and predictive models for public health crises like the pandemic. No scientific awards or grants are explicitly listed. He has no recorded advisees. His work is anchored in interdisciplinary collaborations, leveraging telematics and data science for real-world challenges.

• Computer Vision
• Emotion Recognition
• Paralinguistic Information
• +۲ مورد دیگر

Kavisha Jayathunge is a Lecturer at Bournemouth University, teaching in the MSc Animation Software Engineering unit. His Ph.D. research focuses on unsupervised emotion categorization from audiovisual inputs using computer vision. He investigates transformer-based architectures to mitigate biases in emotion detection datasets. Holds an M.Eng. in Electronics and Software Engineering from the University of Glasgow. Research addresses multimodal information fusion and scalable emotion classification systems for public service applications.

Farhana Ferdousi Lizaمشاهده پروفایل

مدرس

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

Dr. Farhana Ferdousi Liza is a Lecturer in Computing Sciences at the University of East Anglia (UEA), affiliated with the School of Computing Sciences. She holds positions as an Associate Member of Cyber Intelligence and Networks and a member of Data Science and AI groups. Her research focuses on Trustworthy Artificial Intelligence, Machine Learning, Natural Language Processing, and Data Science applications in healthcare, public sector, and social media. She leads the national AI project working group under Research Libraries UK and chairs UEA’s Disability Liaison Officer role for inclusive pedagogy. Education: PhD in Computing Sciences from the University of Kent (2020), funded by JILP Endowment Scholarship. Prior roles include research at the ESRC Business and Local Government Data Research Centre (University of Essex) and an internship at The Alan Turing Institute. She has taught in the UK, USA, and Bangladesh, with expertise in courses like Information Retrieval and Database Systems. Key projects include BRA(AI)N (resilience in AI navigation), MHR-DAIDAI (mental health AI analytics), and ASSIST (AI in mental health triage). Awards include the Wellcome Mental Health Data Prize. Her work spans AI ethics, systemic safety, adversarial robustness, and NLP for public sector decision-making. Recent activities include peer reviews for journals like Expert Systems with Applications and participation in conferences such as UEA Strategy 2030. She advocates for AI security, workforce well-being, and interdisciplinary collaborations in computational social science.

Victor Kebandeمشاهده پروفایل

استادیار

• Cybersecurity
• Digital Forensics
• IoT/IIoT Security
• +۵ مورد دیگر

Dr. Victor Kebande is an Assistant Professor of IT Security in the Secure Distributed Systems (SDS) group at Blekinge Institute of Technology's Department of Computer Science (DIDA). He holds a PhD in Cyber and Information Security from 2018 and has held postdoctoral and visiting researcher roles at institutions like Luleå University of Technology and Colorado State University. His research focuses on cybersecurity, digital forensics, IoT/IIoT security, blockchain, and critical infrastructure protection. He is a member of IEEE, ACM, and IITPSA, and serves on the editorial board of Forensic Science International: Reports . Education: PhD in Computer Science (Cyber and Information Security & Digital Forensics) - 2018 Research Interests: Cybersecurity, digital forensics, IoT/IIoT security, cloud security, blockchain, threat modeling, privacy-preserving techniques, and AI integration with immersive realities. Projects: dAIEDGE: Horizon Europe-funded Network for trustworthy AI at the Edge DISS: Threat detection in IoT systems Symphony: Robust distributed service exposure Awards: None explicitly mentioned, but he holds editorial and reviewer roles in top journals. Grants & Advising: Supervised numerous theses on topics like IoT security, blockchain forensics, and cloud forensics. Active in funding projects like HINTS and Eclipse ArrowHead. Labs/Teams: Part of SDS group, ATLAS Institute (CU Boulder), and collaborations with industry partners like Axis Communications.

Song Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Aerospace Engineering
• +۵ مورد دیگر

Song Fu is a Professor and Associate Chair for Research in the Department of Computer Science and Engineering at the University of North Texas. His research focuses on interdisciplinary applications of artificial intelligence in aerospace engineering, fault diagnosis systems, radiation belt dynamics, and autonomous vehicle technologies. He leads initiatives in predictive maintenance for mechanical systems, data privacy in vehicular edge computing, and cooperative perception frameworks for connected autonomous vehicles. Dr. Fu’s work bridges machine learning with domain-specific challenges, such as optimizing shield machine designs via deep reinforcement learning and developing fault diagnosis frameworks for aeroengines using transformer networks and large language models. His contributions extend to space physics, analyzing radiation belt electron dynamics and Martian magnetosphere interactions with solar wind. He has pioneered privacy-preserving techniques for vehicle-edge networks and contributed to additive manufacturing through generative modeling of microstructure simulations. Key research themes include: AI-driven predictive maintenance and system reliability Cooperative autonomous systems and edge computing Radiation belt modeling and space weather prediction Privacy-preserving machine learning architectures His publications emphasize real-world applications, such as improving aircraft brake monitoring, enhancing flash storage reliability in automotive systems, and optimizing laser-based additive manufacturing processes. Current projects explore multi-agent collaboration in fleet maintenance strategies and next-generation object detection frameworks for autonomous vehicles.

Xiufeng Liuمشاهده پروفایل

پژوهشگر

• Energy Systems
• Data Analytics
• Renewable Energy
• +۵ مورد دیگر

Xiufeng Liu is a Senior Researcher at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on smart meter data analytics, big data, and energy systems, contributing to UN Sustainable Development Goals related to affordable and clean energy. He holds a PhD from Aalborg University (2012) and has held positions at IBM Canada, the University of Waterloo, and Åbo Akademi University. Dr. Liu’s expertise spans energy economics, climate policy modeling, and data-driven methodologies. His work integrates machine learning with energy systems, addressing challenges such as wind power forecasting, solar cell optimization, and heat load prediction. Key projects include OPTIX (optimizing positive-energy districts) and ANSWER (wind-solar energy prediction models). His research outputs emphasize sustainable energy solutions, including federated learning frameworks for privacy-preserving data sharing and anomaly detection in energy grids. Collaborations span global institutions, reflecting his commitment to interdisciplinary energy solutions. Dr. Liu has supervised numerous projects and contributed to 148 publications. His work bridges technical innovation with policy implications, aiming to advance climate-compatible energy strategies globally.

• Artificial Intelligence
• Machine Learning
• Biomedical Informatics
• +۷ مورد دیگر

Xin Chen is an active academic researcher affiliated with the University of Alabama at Birmingham and other institutions, including universities in China, USA, UK, and Singapore. With expertise in Artificial Intelligence , Machine Learning , and Biomedical Informatics , Chen contributes to diverse areas such as autonomous vehicle systems, medical imaging, and cybersecurity. Key Research Areas : Deep Learning for Autonomous Systems Biomedical Data Analysis Network Security and Fault Diagnosis Cross-Domain AI Applications Recent Publications (2025): Chen has published extensively in journals like IEEE Access , Briefings in Bioinformatics , and Neurocomputing , focusing on applications of AI in healthcare, industrial systems, and data science.

• Machine Learning
• Data Mining
• Evolutionary Computation
• +۴ مورد دیگر

Sotiropoulos Dionisios is an Assistant Professor at the Department of Informatics, University of Piraeus. His research focuses on bio-inspired computing and machine learning methodologies, with a specific emphasis on social media analysis. He has held postdoctoral positions at the Athens University of Economics and Business (Department of Management Science and Technology) and serves as a visiting researcher at the Norwich Business School, University of East Anglia. He is a member of the SocioMine research group. Education: PhD in Informatics (2011), University of Piraeus; BSc in Informatics (2003), University of Piraeus His research spans Machine Learning , Data Mining , Evolutionary Computation , Signal Processing , and Intelligent Software Systems . Recent work explores hybrid AI architectures combining knowledge graphs and large language models for cultural analytics, boundary-aware classification, and transparent financial modeling using genetic programming. His publications reveal trends in applying machine learning to social media analytics, healthcare diagnostics, cryptocurrency trading, and cultural domains like music and political speech. Key methodologies include LSTM networks , artificial immune systems , BERT models , and reinforcement learning . As part of the SocioMine research group , he develops user modeling techniques for personalized recommendation systems and investigates opinion formation dynamics in social networks.

Kamran Shaukatمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Cyber Security
• Health Informatics
• +۵ مورد دیگر

Dr. Kamran Shaukat is a Senior Lecturer at Torrens University Australia's Faculty of Business and Design, where he serves as a Senior Learning Facilitator and researcher in the Centre for Artificial Intelligence Research and Optimisation (AIRO). With over 13 years of academic experience, he specializes in artificial intelligence applications across cybersecurity, medical informatics, and educational data mining domains. His research interests focus on Artificial Intelligence , Cyber Security , Health Informatics , Machine Learning , and Medical Imaging . Key research areas include developing anomaly detection frameworks for Industrial IoT, explainable AI for cardiovascular risk assessment, and deep learning approaches for neurological disorder diagnosis. Analysis of his recent publications reveals a strong trend toward applied AI in healthcare (Alzheimer's detection, skin cancer diagnostics, intradialytic hypotension prediction) and cybersecurity innovation (anomaly detection, malware classification, imbalanced data handling). His work consistently bridges theoretical machine learning with practical implementations in critical infrastructure and medical systems. Award highlights include: Recognition in the top 2% of researchers globally (Stanford University ranking) Dr. Shaukat actively supervises PhD students with current projects focusing on pattern recognition applications in cyber security, medical informatics, medical imaging, and educational data mining. His research contributes to UN Sustainable Development Goals through AI-driven solutions for health and security challenges. He maintains active collaborations across international research networks as evidenced by his co-authorship patterns and citation metrics.