جست‌وجوی استاد و پژوهشگر

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

• 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.

• Data Mining
• Machine Learning
• Text Summarization
• +۶ مورد دیگر

Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.

Enayat Rajabiمشاهده پروفایل

دانشیار

• Machine Learning
• Knowledge Engineering
• Data Analytics
• +۴ مورد دیگر

Dr. Enayat Rajabi is an Associate Professor of Data Analytics at the Shannon School of Business , Cape Breton University , Canada. He also serves as an Adjunct Professor at Dalhousie University and is affiliated with Nova Scotia Health as a scientist. His academic journey included a Ph.D. in Information and Knowledge Engineering from the University of Alcalá, Spain, and he has contributed extensively to machine learning and semantic web domains. Education: Ph.D. in Information and Knowledge Engineering, University of Alcalá, Spain (2015) Master of Software Engineering, Ferdowsi University of Mashhad, Iran (2004) Bachelor of Software Engineering, Razi University, Iran (2001) Dr. Rajabi's research focuses on machine learning , knowledge engineering , and semantic web applications in healthcare and smart cities. His work explores explainable AI frameworks, knowledge graph construction, and data-driven solutions for sustainable transportation and clinical decision support systems. His recent publications highlight trends in knowledge graph integration with large language models for healthcare, graph neural networks , and predictive analytics in urban environments. He has secured significant grants, including the NSERC Discovery Grant and Mitacs Research Training Award , to advance these domains. Scientific Contributions: NSERC Discovery Grant (2020-2025) - Semantic Web Analysis over Nova Scotia Open Data ($156,000) New Health Investigator Grant (2022-2024) - Machine Learning for ALC Patients ($97,418) Mitacs Globalink ($4,250) - Graph Neural Networks CBU RISE grants for Explainable Clinical Decision Support Systems and Multi-Label Text Classification Dr. Rajabi has mentored numerous research assistants across projects and maintains active collaborations with institutions in Canada, Spain, and Iran. His technical expertise spans Python, Tableau, Databricks, and PySpark, with teaching responsibilities in Predictive Analytics , Data Visualization , and Quantitative Methods .

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۲ مورد دیگر

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۲ مورد دیگر

Chun-Liang Li is a Research Scientist at Apple MLR and an Affiliate Assistant Professor at the Allen School of Computer Science, University of Washington. He holds a Ph.D. in Machine Learning from Carnegie Mellon University (2014-2019) and a B.S./M.S. in Computer Science and Information Engineering from National Taiwan University (2008-2013). His research spans machine learning, computer vision, and natural language processing, with emphasis on representation learning, efficient model training, and multimodal understanding. Key focus areas include large language model optimization, contrastive learning techniques, document understanding systems, and domain adaptation methods. Recent publications demonstrate strong focus on LLM efficiency (dataset decomposition, distillation techniques), document AI (table reasoning, form extraction), and multimodal alignment (image-text retrieval, prefix conditioning). His work consistently appears in premier venues including NeurIPS, CVPR, ACL, and ICLR. Awards and Honors: IBM Ph.D. Fellowship (2018) Best student paper runner-up, IJCAI (2017) First Place in both tracks of KDD Cup (2011, 2013) He actively mentors students and collaborators, inviting contact for internship opportunities. Current industry-academic position bridges cutting-edge research with practical applications in machine learning systems.

Nikola Simidjievskiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +۴ مورد دیگر

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

Thanassis Tiropanisمشاهده پروفایل

استاد

• Decentralised information systems
• Decentralised information retrieval
• Data observatories
• +۷ مورد دیگر

Thanassis Tiropanis is a Professor at the University of Southampton's School of Electronics and Computer Science (ECS), specifically within the Web and Internet Science Group. He serves as enterprise champion for ECS and impact champion for computer science. His academic affiliations extend to the Institute for Life Sciences and the Southampton Marine and Maritime Institute, reflecting his interdisciplinary research approach. Professor Tiropanis specializes in decentralised information systems and infrastructures, with research interests spanning decentralised information retrieval, data and web observatories, bias in datasets, inaccessible algorithms, and IoT analytics. His work focuses on the technical and socio-technical aspects of the Web and Internet as evolving socio-technical artifacts, particularly examining how individuals can maintain control over their personal data through decentralised architectures. His recent publications reveal a strong research trajectory in decentralised search technologies, particularly through the ESPRESSO framework for personal online datastores. The publications demonstrate expertise in federated learning, privacy-preserving techniques, and bias detection in machine learning systems. His work bridges theoretical computer science with practical applications in health data, cybersecurity, and social linked data environments. Fellow of BCS Chartered IT professional with BCS Senior member of IEEE Fellow of the Higher Education academy in the UK Member of ACM Member of the Technical Chamber of Greece Professor Tiropanis currently supervises PhD students including Engin Kocak and Wenxuan Huang, while leading active research projects such as ESPRESSO (EPSRC-funded) focused on efficient search over personal repositories. His completed projects include significant EU-funded initiatives like NoBIAS (Artificial Intelligence without Bias) and IoT Lab. He is affiliated with multiple research centers including the Centre for Internet of Things and Pervasive Systems, Centre for Machine Intelligence, and Centre for Digital Finance.

• Signal Processing
• Machine Learning
• Computer Engineering
• +۳ مورد دیگر

Ehsan Namjoo serves as a Research Fellow in the Department of Electronics & Computer Engineering at the University of Limerick, with a primary affiliation at Lero – the Irish Software Research Centre. His multidisciplinary work bridges theoretical signal processing, machine learning applications, and hardware implementation for real-world systems. His research spans signal processing (DOA estimation under non-ideal noise conditions, EEG source imaging), machine learning (explainable AI for medical diagnostics, feature selection in cancer detection), and computer engineering (polar code decoders, visible light communication systems). Notable biomedical applications include breast cancer diagnosis and epileptic source analysis, while cybersecurity work focuses on intrusion detection in cloud environments. Recent publications (2021-2025) reveal a consistent trajectory toward developing lightweight, interpretable models for tabular medical data alongside robust signal processing frameworks for communication systems. His work increasingly integrates hardware-aware algorithms, particularly in polar coding implementations, and emphasizes practical validation through experimental demonstrations in visible light networks. As an active Lero researcher, Namjoo participates in Ireland's national software research ecosystem, contributing to interdisciplinary projects that connect theoretical innovations with healthcare, communications, and security applications through collaborative frameworks.

• Database Systems
• Data Management
• Machine Learning
• +۴ مورد دیگر

Tova Milo is a leading academic in database systems and data management at Tel Aviv University. Her research focuses on advancing automated data analysis, machine learning, and efficient data management techniques. She has contributed to systems like LINX (language-driven data exploration), TabEE (tabular embeddings explanations), and DPClustX (differentially private clustering explanations). Her work bridges database theory and practical applications, addressing challenges in fraud detection, crowdsourcing, and large language model utilization. Key contributions include: Development of systems for automated data exploration (e.g., ATENA, LINX) Foundations for explainable AI in databases (FEDEX, TabEE) Algorithms for category tree construction and cost-effective data processing Techniques for photo archiving under storage constraints She has received the 2022 TCDE Impact Award and actively contributes to top venues like SIGMOD, VLDB, and ICDE. Her research emphasizes balancing theoretical rigor with real-world applicability.

Larry P. Heckمشاهده پروفایل

استاد

• Artificial Intelligence
• Conversational Systems
• Machine Learning
• +۵ مورد دیگر

Larry P. Heck is a Professor at the Georgia Institute of Technology with joint appointments in the Schools of Electrical and Computer Engineering and Interactive Computing. He holds the Rhesa S. Farmer Distinguished Chair of Advanced Computing Concepts and is a Georgia Research Alliance Eminent Scholar. His career spans academia, industry, and research leadership, with notable roles at Microsoft, Google, Samsung, and SRI International. BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD in Electrical Engineering, Georgia Institute of Technology (1991) His research focuses on Artificial Intelligence , particularly in Conversational Systems , Machine Learning , and Natural Language Processing . Key subfields include dialogue systems, speech/speaker recognition, multimodal interaction, and large language model (LLM) governance. Recent publications emphasize dataset creation, intent modeling, reinforcement learning, and ethical AI practices. Scientific awards include IEEE Fellow , National Academy of Inventors Fellow (2025) , and multiple accolades from Texas Tech and Georgia Tech. He has led groundbreaking work in deep learning for speech processing and holds over 50 U.S. patents.

• Trustworthy Artificial Intelligence
• Cybersecurity
• Natural Language Processing
• +۱ مورد دیگر

K.P. Subbalakshmi is a Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology, where she directs research on trustworthy AI, cybersecurity, and NLP applications in mental health. A Fellow of the National Academy of Inventors and Jefferson Science Fellow, she develops interpretable AI systems for healthcare and security domains. Her research bridges fundamental AI principles and practical applications, with recent work on causal synthetic data generation, Alzheimer's detection from speech, suicide prediction from social media, and explainable rumor detection. These projects focus on model transparency and real-world reliability. Awards and Honors: Fellow, National Academy of Inventors (2018) Jefferson Science Fellowship (2016) AFOSR Summer Faculty Fellow (2024, 2025) New Jersey Inventors Hall of Fame Innovator Award (2012) Dr. Subbalakshmi founded the Stevens Institute for Artificial Intelligence and holds leadership roles in IEEE COMSOC's Cognitive Networks committee. She has commercialized technologies through startups Dynamic Spectrum LLC and Jaasuz.

• Data Mining
• Machine Learning
• Biomedical Informatics
• +۴ مورد دیگر

Michael Steinbach is a Researcher in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities, working in the research group of Prof. Vipin Kumar. He holds B.S. in Mathematics, M.S. in Statistics, and M.S./Ph.D. in Computer Science from the University of Minnesota. His research focuses on data mining, machine learning, biomedical informatics, and statistics, with applications in environmental science, healthcare, and engineering. He co-authored the widely used textbook Introduction to Data Mining , translated internationally. Previously, he held software engineering roles at Silicon Biology, Racotek, and NCR. Research interests emphasize integrating scientific knowledge with machine learning for real-world systems, such as climate modeling, flood forecasting, and healthcare analytics. His work spans causal inference, physics-guided AI, and interpretable predictive models. Collaborations include environmental monitoring, wildfire management, and clinical decision support systems in healthcare. Publications highlight innovations in knowledge-guided learning, multi-scale modeling, and causal discovery. His contributions bridge theory and practice, advancing AI applications in complex systems. The lab engages in interdisciplinary projects, combining computational methods with domain expertise to address societal challenges.

Venet Osmaniمشاهده پروفایل

استاد

• Clinical AI
• Machine Learning
• Healthcare Analytics
• +۷ مورد دیگر

Professor Venet Osmani is a leading academic in Clinical AI and Machine Learning at Queen Mary University of London (QMUL), where he heads the Osmani Lab within the Digital Environment Research Institute (DERI). He concurrently serves as a Visiting Professor at the University of Sheffield, previously directing the Health Informatics Research Group and contributing to initiatives like the Healthy Lifespan Flagship Institute (HELSI). His interdisciplinary research focuses on analyzing large-scale clinical and behavioral data to optimize healthcare strategies, leveraging methodologies like generative architectures (GANs, VAEs), explainable AI, and sample complexity. Key areas include predictive modeling in chronic diseases (e.g., neurodegenerative conditions, cancer), mental health, and critical care, with a strong emphasis on mitigating health inequities and integrating wearable device data. Funded by major bodies including UKRI’s MRC, EPSRC, and NIHR, his work has been featured in high-impact journals and media outlets like MIT Technology Review and Forbes. Notable achievements include a Best Paper Award at NeurIPS 2022 for synthetic health data research and recognition as a World’s top 2% scientist (Elsevier, 2023) . Collaborations: Mayo Clinic, Cleveland Clinic, Mount Sinai Hospital, Great Ormond Street Hospital, and Insigneo Institute. Grants: Over £10M secured from UK and EU research councils. Key projects include the NeuroArtP3 initiative for Parkinson’s disease and a mental health research hub studying borderline personality disorder. His lab also explores synthetic clinical data to democratize access and address algorithmic bias.

Catarina Pinto Moreiraمشاهده پروفایل

دانشیار مدعو

• Quantum Probabilistic Models
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Catarina Pinto Moreira is an Adjunct Associate Professor in the School of Computer Science at Queensland University of Technology (QUT). She holds a PhD in Information Systems and Computer Engineering from the University of Lisbon and specializes in quantum probabilistic models, machine learning, and explainable AI. Her research focuses on developing non-classical probabilistic graphical models for decision-making, particularly in medical and cognitive contexts. She has been recognized with awards such as the Dean's Award for Excellence in Teaching (2018) and the Centre for Data Science 2020 Excellence Award. Dr. Moreira is an Associate Editor for BMC Bioinformatics' 'Artificial Intelligence in Bioinformatics' section, emphasizing applications of machine learning in biological data. She actively supervises PhD students in areas like interpretable AI and predictive process analytics. Her academic roles include teaching at QUT and the University of Leicester, where she contributed to courses in information systems, finance, and artificial intelligence. Her work bridges quantum cognition, medical decision support, and human-centered AI, with publications spanning journals like Behavioral and Brain Sciences and Entropy . She has secured grants totaling $20,000 for research in Explainable AI and causality. Dr. Moreira's contributions to AI ethics, multimodal learning, and adversarial attacks reflect her commitment to advancing trustworthy AI systems.