جستجوی استاد راهنمای دانشگاهی

Carmel Bondمشاهده پروفایل

مدرس

• Mental Health Nursing
• Compassion in Healthcare
• Clinical Supervision
• +۳ مورد دیگر

Dr. Carmel Bond is a Lecturer in Mental Health Nursing at Sheffield Hallam University's School of Health and Social Care within the College of Health, Wellbeing and Life Sciences. She holds a PhD in Mental Health and Wellbeing, MSc in Psychology, MSc in Nursing Science, BSc in Science with Education, and PGCE qualification. With over 30 years of clinical experience including older-adult acute services and medium-secure hospitals, her teaching spans undergraduate and postgraduate mental health nursing curricula. Bond's research examines compassion in mental healthcare through dual perspectives of practitioners and patients, nursing leadership development, and workforce wellbeing strategies. Her work emphasizes humanistic, Rogerian person-centered approaches and recovery-focused care models. Publication analysis reveals three primary research streams: 1) Conceptual and operational frameworks for compassion in healthcare, 2) Leadership development in nursing education, and 3) Pandemic impacts on healthcare professionals. Methodologically, her work favors discourse analysis and qualitative approaches. Awards & Honors: 2022: 3MT® Prize Winner (University of Nottingham) 2021: Phil Strong Memorial Prize (British Sociological Association) ESRC-funded PhD scholarship Professional Activities: Supervises MSc mental health nursing students Reviewer for Nurse Education in Practice and Journal of Psychiatric and Mental Health Nursing Collaborates on NIHR/HEE Integrated Clinical Academic Internship programme

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Mohammed Oualid ATTAOUI is a Postdoctoral Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, affiliated with the SVV department. His work focuses on advancing artificial intelligence and machine learning methodologies, particularly in the context of deep neural network (DNN) safety analysis, clustering algorithms for data streams, and medical image processing applications. He holds a strong research interest in generative adversarial networks (GANs), black-box model analysis, and transfer learning techniques. His current position involves developing robust frameworks to ensure safety and reliability in AI systems, with notable contributions to the SAFE methodology for DNN retraining and validation. Research interests span across multiple domains including: Deep Learning and Safety-Critical AI Systems Data Stream Mining and Real-Time Clustering Medical Imaging Analysis (Histopathology, Computer Vision) Feature Selection and Optimization in Bioinformatics Subspace and Topological Clustering Approaches His recent research trends emphasize the intersection of adversarial machine learning and safety engineering, with publications demonstrating advancements in GAN-enhanced simulation-driven testing and clustering-based safety analysis for DNNs. His work bridges theoretical algorithm development with practical applications in healthcare imaging and autonomous systems. Dr. ATTAOUI's research outputs reflect a strong focus on methodological innovation with over 15 peer-reviewed articles since 2018. He actively contributes to open-source tools for data stream clustering and maintains an active presence in AI safety communities. His current projects explore adaptive clustering techniques for evolving data streams and robustness certification of medical image processing systems.

Anurag Nagarمشاهده پروفایل

دانشیار

• Bioinformatics
• Natural Language Processing
• Financial Data Mining
• +۴ مورد دیگر

Anurag Nagar serves as an Associate Professor of Instruction in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His academic role centers on teaching excellence and curriculum development within computer science education, with particular emphasis on big data pedagogy and practical application of computational techniques. Dr. Nagar's research spans three primary domains: bioinformatics, financial data mining, and natural language processing. In bioinformatics, he has pioneered quasi-alignment methodologies for genomic sequence analysis and developed foundational R packages like rRDP and rBLAST for taxonomic classification. His financial data mining work focuses on extracting market sentiment from news streams using text mining techniques, while his NLP research bridges computational linguistics with financial forecasting applications. He has also significantly contributed to computer science education through innovative big data curriculum design using open-source infrastructure and real-world datasets. Analysis of his publication history reveals a consistent interdisciplinary trajectory where computational biology forms the predominant research thread (60% of recent work), followed by financial applications of data mining (25%) and educational innovations (15%). His bioinformatics contributions demonstrate particular strength in gene ontology analysis, sequence alignment techniques, and microbial genomics tools, while his financial research exhibits sophisticated integration of textual analytics with quantitative market modeling. Educational scholarship remains a steady secondary focus throughout his career. Contact details: Anurag.Nagar@utdallas.edu | +1(972) 883-6345 | ECS4.610. No information is available regarding student advising, research grants, or laboratory affiliations in current documentation.

• Computational Intelligence
• Eldercare Technology
• Geospatial Intelligence
• +۲ مورد دیگر

James Keller is a Curators Professor and R. L. Tatum Professor in the Electrical Engineering and Computer Science Department at the University of Missouri. His research in computational intelligence develops innovative applications across diverse domains including eldercare technology, bioinformatics, geospatial intelligence, and landmine detection. Keller's recent work focuses on explainable AI systems, autonomous drone navigation, advanced materials characterization, and biomedical signal processing. His laboratory employs cutting-edge techniques in deep learning, fuzzy logic, and computer vision to solve complex real-world problems. The research portfolio demonstrates strong interdisciplinary collaboration across engineering, materials science, and healthcare domains.

Yan Zhangمشاهده پروفایل

مدرس

• Recommendation Systems
• Data Mining
• Machine Learning
• +۵ مورد دیگر

Dr. Yan Zhang is a Lecturer in Information Technology within the Faculty of Science and Technology at Macquarie University. She completed her PhD in recommendation systems from the University of Technology Sydney (UTS) in 2022 and has established herself as an active researcher with 17 publications spanning from 2016 to 2025. Her academic profile shows consistent research output with multiple publications each year, demonstrating ongoing productivity and engagement in her field. She is registered to supervise postgraduate research, indicating her role in mentoring graduate students. Dr. Zhang's research interests center around recommendation systems, data mining, machine learning, and transfer learning. Her work spans both theoretical foundations and practical applications, with a strong emphasis on solving cold-start problems, cross-domain recommendations, and efficient recommendation techniques. The fingerprint of her research shows significant contributions in areas including User, Hashing, Collaborative Filtering, Recommender Systems, User Preference, Algorithms, Matrix Factorization, and Boolean Function. Her research contributes to UN Sustainable Development Goals through technological innovation in data science and information systems. Analysis of Dr. Zhang's publication history reveals an evolving research trajectory with increasing sophistication in recommendation system methodologies. Her early work focused on discrete techniques and hashing methods for efficient recommendations, while recent publications demonstrate expansion into federated learning, multi-agent systems, and medical applications. The thematic progression shows movement from foundational recommendation techniques toward more complex, interdisciplinary applications while maintaining core expertise in user preference modeling and efficient recommendation algorithms. Dr. Zhang has accumulated 268 citations with an h-index of 7, indicating growing scholarly impact in her field. Her publications appear in reputable venues including IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and proceedings of major conferences like IEEE ICDE. She has demonstrated consistent collaboration with researchers across institutions, as evidenced by her co-authorship patterns across multiple publications. As a postgraduate research supervisor, Dr. Zhang contributes to academic training in information technology. Her research program appears to focus on both theoretical aspects of recommendation systems and practical applications in cybersecurity, healthcare, and content delivery. The diversity of her recent work suggests she leads or participates in multiple research streams addressing different application domains while maintaining core methodological expertise in recommendation technologies.

Jarek Szlichtaمشاهده پروفایل

دانشیار

• Big Data Analytics
• Machine Learning
• Responsible AI
• +۵ مورد دیگر

Jarek Szlichta is an Associate Professor in the Department of Electrical Engineering & Computer Science at York University. He is also a Research Enhanced Faculty in responsible data science and AI under the Connected Minds initiative, a Research Faculty Fellow at IBM Centre for Advanced Studies (CAS), and an Adjunct Professor at the University of Waterloo. As co-director of the Data & AI Lab, his work focuses on data-driven systems, large-scale machine learning, and trustworthy AI. He has received the IBM CAS Faculty of the Year Award for DB2 system optimization and the CeBIT Business Award for the OCEAN GenRap tool. Education: PhD in Computer Science, York University Postdoctoral Fellowship at University of Toronto IBM CAS Student Fellowship (IBM Research Student of the Year Award) Research Interests: Big Data Analytics and Machine Learning Responsible AI and Ethical Frameworks Data-Driven Systems and Stream Processing Graph Data, Knowledgebases, and Web Search Data Quality and Integration Grants & Collaborations: Funded by NSERC, MITACS, SOSCIP, IBM, and AT&T. His work emphasizes bridging theory and practice in AI and data systems. Labs & Teams: Co-directs the Data & AI Lab at York University, focusing on innovative solutions in responsible AI and large-scale systems.

Abdul Rehman Gilalمشاهده پروفایل

استاد آموزشی

• Software Engineering
• Cybersecurity
• Complex Networks
• +۴ مورد دیگر

Abdul Rehman Gilal is an Assistant Teaching Professor at Florida International University (FIU) within the Knight Foundation School of Computing and Information Sciences. He holds a Ph.D. in Information Technology from Universiti Teknologi PETRONAS, Malaysia (2017), an M.Sc. in Information Technology from Universiti Utara Malaysia (2013), and a B.Sc. in Computer Science from the University of Sindh (2008). His research focuses on software engineering, cybersecurity, complex networks, and human factors in software development. Dr. Gilal has extensive teaching experience at institutions like the University of Portsmouth (UK), Universiti Teknologi PETRONAS (Malaysia), and Sukkur IBA University (Pakistan). His expertise includes software project management, team composition strategies, and innovative pedagogical approaches in programming and software engineering education. His research interests span software assurance cases, agile methodologies, data stream mining, and the human-centric aspects of software development. He has contributed to over 50 publications, with recent work addressing AI ethics, IoT security, and sustainable software development practices. Dr. Gilal’s work emphasizes bridging theoretical advancements with practical industry applications, particularly in small and medium software enterprises. While no formal awards are listed, his prolific publication record and interdisciplinary contributions reflect his academic impact. His advising and grant activities are not explicitly detailed in the provided texts, though his research collaborations span multiple countries and sectors.

Pingfan Wangمشاهده پروفایل

مدرس

• Artificial Intelligence
• Data Mining
• Machine Learning
• +۶ مورد دیگر

Pingfan Wang is a Lecturer in the Department of Computer and Information Sciences at Northumbria University. His research focuses on data-driven methodologies, including concept drift detection in data streams, applications of AI in environmental sustainability, and analysis of terrorism incidents using clustering techniques. He has contributed to several notable publications in these areas. Research interests include artificial intelligence, data mining, machine learning, environmental sustainability, and security analytics. His work often integrates advanced statistical models with real-world applications in public policy and energy security. Pingfan has published extensively on topics such as concept drift detection, explainable AI, and tourism review mining. His recent work includes studies on the impact of Lung-Strengthening Qigong on wellbeing, demonstrating interdisciplinary research interests. No scientific awards or grants are listed in the provided information.

Kamal Alsharifمشاهده پروفایل

استاد

• Water Resources Management
• Environmental Policy
• Hydropolitics
• +۷ مورد دیگر

Kamal Alsharif is a Professor of Environmental Science and Water Policy at the School of Geosciences, University of South Florida, where he has been employed since 2007. He is also affiliated with the Patel College of Global Sustainability. His academic career spans roles as Undergraduate Director, Graduate Director, and Associate Professor prior to his current position. Ph.D. in Water Resources Science, University of Minnesota (2004) M.S. in Environmental Science, Minnesota State University (1996) B.S. in Biology, St. Cloud State University (1992) Dr. Alsharif specializes in interdisciplinary water resources policy, watershed management, and sustainability. Key research areas include data envelopment analysis for environmental benchmarks, climate change impacts on water systems, and nature-based solutions for water governance. His work often integrates social indicators with technical analysis. Recent publications focus on integrated water resources management in Lake Chad Basin, environmental equity in Tampa floodplains, and decision support systems for Florida water management. Themes span from urban water policy to transboundary water governance and recycled water reuse. Sabbatical Leave Award, University of South Florida (2019) Kosove Distinguished Graduate Teaching and Service Award (2021) Editor’s Choice Papers for agroecological systems study in Peru (2017) Dr. Alsharif advises graduate students like Fautemeh Bami (PhD Dissert Director) and Nayeema Rashid (PhD Co-Director). He actively collaborates with interdisciplinary teams on water policy research and climate resilience projects.

Flavio De Paoliمشاهده پروفایل

دانشیار

• Semantic Web Technologies
• Data Enrichment
• Services-Oriented Computing
• +۳ مورد دیگر

Flavio De Paoli is an Associate Professor affiliated with the Insid&s LAB. His research focuses on semantic web technologies, data enrichment, and interactive tools for data management. Key projects include the enrichMyData initiative and the development of frameworks like SemT and Protobject . His work spans geospatial data analysis, services-oriented computing, and scalable data solutions. Notable contributions include tools for semantic table interpretation (e.g., MantisTable ), API-based data enrichment, and event-driven analytics. He has also contributed to cloud computing frameworks and semantic wiki integration for knowledge management. His research emphasizes human-in-the-loop systems, automated data processing, and tool development for both academic and industrial applications. Projects like EW-shopp and BEEO demonstrate his focus on applying semantic technologies to real-world challenges in marketing, urban planning, and smart systems.

Tarek Buhagiarمشاهده پروفایل

مدرس

• Econometrics
• Quantitative Methods
• Pedagogical Innovation
• +۴ مورد دیگر

Tarek Buhagiar is a Lecturer in the Department of Economics at the University of Central Florida's College of Business, with over two decades of teaching experience since joining UCF in 1998. He specializes in statistical modeling and economic applications, holding graduate qualifications in statistics/data mining and mathematics. His research spans pedagogical innovation in quantitative business education, monetary policy effectiveness, and financial market analysis. Current work explores gamification in learning environments and quantitative easing impacts. Analysis of his publications reveals concentrated focus on economics education innovation through active learning strategies, assessment of monetary policy during financial crises, and econometric analysis of financial markets. Methodologies range from experimental educational research to econometric modeling.

Katarzyna Kaczmarek-Majerمشاهده پروفایل

دانشیار

• Explainable Artificial Intelligence
• Trustworthy Artificial Intelligence
• Fuzzy Sets and Systems
• +۸ مورد دیگر

Katarzyna Kaczmarek-Majer is an Associate Professor at the Department of Stochastic Methods, Systems Research Institute, Polish Academy of Sciences in Warsaw, Poland. She specializes in explainable and trustworthy artificial intelligence with applications in medical data stream analysis and fuzzy systems. Principal Investigator of "ExplainMe: Explainable Artificial Intelligence for Monitoring Acoustic Features extracted from Speech" (since 07.2024) Lead Researcher of Trustworthy AI for Healthcare Lab (since 07.2024) Research Interests : Medical data stream analysis and linguistic summarization Time series forecasting with fuzzy Bayesian methods Smartphone-based mental health monitoring Scientific Contributions : Best Paper Award at FUZZ-IEEE 2022 (Padova) Over 50 publications in computational statistics and fuzzy systems Active reviewer for 15+ journals and committee member at 20+ conferences

Floriana Espositoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Programming Languages
• +۳ مورد دیگر

Floriana Esposito is a Full Professor and current Director of the Department of Computer Science at the University of Bari, Italy. With a career spanning over 50 years since her 1974 appointment, she has held pivotal leadership roles including Dean of the Faculty of Computer Science (1997-2002) and Director of the Interdepartmental Center for Logic and Applications (1997-2001). She teaches core courses including Algorithms and Data Structures, Knowledge Engineering, and Machine Learning. Education: Degree in Electronic Physics from University of Bari (1970) First Academic Appointment at University of Bari (1974) Research Focus: Her work centers on Artificial Intelligence and Machine Learning with emphasis on logical/algebraic foundations. Initial research in statistical pattern recognition evolved into Intelligent Tutoring Systems and Machine Learning, now focusing on similarity-based learning, multistrategy approaches, and integration of symbolic/numeric methods. Major applications target Document Analysis and Understanding for paper-based systems, with significant contributions to symbolic data analysis and knowledge discovery. Publication Trends: Recent work shows strong continuity in multistrategy and incremental learning techniques applied to document processing and symbolic data analysis. Her research consistently bridges theoretical foundations (logical theories, concept drift handling) with practical implementations in spatial data mining, e-learning systems, and urban planning, demonstrating a 30-year trajectory from knowledge acquisition fundamentals to real-world AI deployment. Scientific Awards: No specific awards were mentioned in the source text. Advising & Grants: As founder of LACAM Laboratory (1986), she has mentored numerous PhD students and researchers. Her group secured extensive EU funding through ESPRIT, IST, and Framework programs including MLNet, KDNET, and DELOS Networks of Excellence, focusing on machine learning applications in document analysis, digital libraries, and spatial data mining across 15+ European collaborative projects. Labs & Teams: She chairs LACAM Laboratory (2 full professors, 3 associate professors, 5 assistant professors, multiple PhD students), which serves as a European hub for AI research. The lab has been integral to ESPRIT Intrepid (document processing), CONCERTO (digital document retrieval), COLLATE (historical archive systems), and SODAS (symbolic data analysis), maintaining continuous involvement in EU Networks of Excellence since the 1990s.

• Machine Learning
• Data Mining
• Big Data
• +۸ مورد دیگر

Donato Malerba is a Full Professor at the Department of Computer Science, University of Bari, Italy, and Director of the CINI Lab on Big Data. His research focuses on Machine Learning, Data Mining, and Big Data applications, with notable contributions to spatial data analysis, process mining, and cybersecurity. He has led multiple European and national research projects and received the IBM Faculty Award in 2004. Malerba serves on the Board of Directors of the Big Data Value Association and chairs international conferences like ECML PKDD and ALT/DS. His work bridges theory and practice, addressing challenges in environmental monitoring, network security, and explainable AI. Research interests include inductive logic programming, multi-relational mining, bioinformatics, and cyber threat detection. He has published over 200 papers in top venues and actively collaborates with industry stakeholders through projects like the PPP Big Data Value initiative. Awards: IBM Faculty Award 2004 Roles: Director of Computer Science Department, Director of CINI Lab on Big Data Key Projects: Lead investigator in EU-funded projects on Big Data analytics, cybersecurity, and environmental monitoring. Recent work emphasizes LLM applications for environmental monitoring (e.g., bark beetle outbreak detection via Sentinel-2 imagery) and quantum machine learning for incremental learning. His lab develops tools like PROPHET (explainable predictive process monitoring) and PANACEA (neural ensemble-based cyber threat detection).

• Data Mining
• Social Computing
• Graph Mining
• +۷ مورد دیگر

Erisa Terolli is a Teaching Assistant Professor at the Charles V. Schaefer, Jr. School of Engineering and Science at Stevens Institute of Technology , where she has been employed since September 2021. Her academic work spans Data Mining , Social Computing , and Graph Analytics . Education : PhD in Computer Science (2018) from Sapienza University of Rome Research Interests : Erisa focuses on Efficient graph mining techniques for massive datasets Understanding user behavior and entity dynamics in health forums Modeling political discourse anomalies on platforms like Reddit Computational psychology of digital shop assistants Physics-inspired optimization algorithms for terrain and supply chain problems Design of career office information systems Scientific Awards : Google Anita Borg Fellowship (2015) ICT Awards - Female in ICT (2016) Institutional Service : Erisa has contributed to various committees and initiatives at Stevens, including: Chair of the DEI Committee Advisor for the SWICS Student Club Member of the ADAPT S-STEM Program Chair of the Developing Curriculum Committee NSF BPC Plan Workshop participant Academic Ambassador and member of CS NTT/Chair Search Committees