جست‌وجوی استاد و پژوهشگر

Wayne Wobckeمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Associate Professor Wayne Wobcke is a faculty member in the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he has been employed since 2002. His academic career includes previous positions at the University of Sydney until 1998, British Telecom Labs in the UK for three years, and the University of Melbourne for one year. He holds a PhD in Computer Science from the University of Essex (1989), an MSc from the University of Queensland (1985), and a BSc (Hons) in Mathematics/Computer Science from the University of Queensland (1984). Dr. Wobcke's research spans both theoretical and practical aspects of artificial intelligence and data science. His work encompasses intelligent agents, data mining, agent-based modeling, dialogue management, personal assistants, recommender systems, and computational social science. He has collaborated extensively with industry through three Cooperative Research Centres (Smart Internet Technology CRC, Smart Services CRC, and Data to Decisions CRC), where he served as a Programme Manager and Project Leader for over 10 years. Notable achievements include developing a voice-controlled mobile application for email and calendar interaction (a precursor to Apple's Siri) and deploying a people-to-people recommender system for online dating on one of Australia's largest dating sites. His recent research focuses on data science in humanitarian contexts and machine learning applications in official statistics, conducted in collaboration with BPS (Statistics Indonesia) and STIS (Politeknik Statistika, Indonesia). His publication record shows a consistent trajectory of impactful research, with recent work concentrating on poverty targeting, domain adaptation, natural language processing for recommender systems, and political opinion mining. Scientific Awards: Best Paper Nomination, 11th Workshop on Argument Mining (2024) UNSW Arc Postgraduate Research Supervisor Award (2017, 2018) AAAI Deployed AI Application Award, Twenty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (2014) Best application paper runner up, 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2013) Dr. Wobcke has successfully supervised numerous research students, with Irwan Rahadi currently working on 'Causal Modelling and Machine Learning for Official Statistics'. His grant portfolio includes significant funding from the Australian Research Council and various Cooperative Research Centres, totaling over $3.7 million since 2003. He teaches COMP9414 Artificial Intelligence and COMP9727 Recommender Systems at UNSW.

Herna Viktorمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۷ مورد دیگر

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.

Sunnie Sun Chungمشاهده پروفایل

استاد

• Big Data Analytics
• Artificial Intelligence
• Natural Language Processing
• +۳ مورد دیگر

Dr. Sunnie Sun Chung is a Professor in the Department of Electrical Engineering and Computer Science at Cleveland State University's College of Engineering. She leads the Big Data Analytics & AI Lab and has received multiple Faculty Merit Recognition Awards (2020-2024) and Golden Apple Awards (2021-2022). Research Interests : Big Data Analytics, Artificial Intelligence, Natural Language Processing, Cyber Security, Machine Learning, and Optimization in Cloud/IoT environments. Teaching : Advanced courses in Data Mining, Big Data Systems, and Deep Learning (DSA460/CIS492/593, CIS611/711, etc.) with dedicated teaching labs. Publications reflect expertise in Knowledge Graphs for Medical QA , Network Intrusion Detection with Graph Models , Privacy-Preserving Cloud Learning , and Basketball Analytics . Her work spans journals like IEEE Transactions on AI and IEEE Access. Scientific Awards : Faculty Merit Recognition Awards (4x) Golden Apple Teaching Award (2x) Students Supervised : Over 10 PhD/Master’s students including Asanka Mananayaka (NLP-based Intrusion Detection), Yixi Luo (Homomorphic Encryption), Dennis Risch (Medical QA Systems), and collaborators with Case Western Reserve University. Labs & Collaborations : Director of the Big Data Analytics & AI Lab since 2018. Hosted annual IBM Health collaborative workshops since 2018. Maintains strong industry ties with Microsoft, Amazon, Oracle, and Cleveland Clinic.

• Data Stream Management
• Real-time Data Analytics
• Data Quality
• +۷ مورد دیگر

Sandra Geisler is a Junior Professor for Data Stream Management and Analysis at the Department of Computer Science, RWTH Aachen University, a position she has held since September 2021. She is also the leader of the Digital Health Spaces group at the Fraunhofer Institute for Applied Information Technology (FIT) in St. Augustin, reflecting her dual expertise in academic research and applied digital health solutions. Bachelor/Master: Diploma in Computer Science, RWTH Aachen University (2008) PhD: Doctoral degree in Computer Science, RWTH Aachen University (2016) Her research focuses on data stream systems, real-time analytics, data quality, and their applications in digital health and industrial processes. She has made significant contributions to ontology-based data quality management, edge computing for stream processing, and FAIR data principles. Recent work explores the integration of large language models into data management workflows and the development of privacy-preserving platforms for industrial data exchange. Her recent publications demonstrate a strong trend in distributed and edge-based stream processing, interdisciplinary applications in healthcare and supply chains, and the use of AI for data discoverability and quality. Topics include in-network computing, simulation of edge queries, self-tonometry for glaucoma, and cross-company data sharing with privacy awareness. She has served as Associate Editor for the Data & Knowledge Engineering Journal (Elsevier), Public Relation Chair for QDB Workshop (VLDB 2016), and Workshop Chair for IMMoA and HIMoA workshops. She has also edited a special issue on Information Management in Mobile Applications in the Pervasive and Mobile Computing Journal. Geisler has supervised multiple theses on topics including LLM-based ontology integration, edge anomaly detection, and data ecosystem modeling. She has been actively involved in research grants and projects related to industrial data processing, digital health, and sustainable production. She teaches courses such as Data Stream Management and Analysis and Data Ecosystems Lab. She leads the Digital Health Spaces research group at Fraunhofer FIT, focusing on innovative solutions for health data management and patient-centric digital tools. Her work bridges computer science, healthcare, and industrial applications, promoting secure, efficient, and intelligent data ecosystems.

Daniel Barbaráمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Databases
• +۳ مورد دیگر

Daniel Barbará is a Professor in the Department of Information and Software Engineering at George Mason University since 1997. He holds a PhD and MS in Computer Science from Princeton University, and a BS in Electrical Engineering from Universidad Metropolitana. Research Interests: Data mining, machine learning, cybersecurity, mobile computing, and fractal-based data analysis. His work focuses on clustering algorithms, intrusion detection, data compression, and handling high-dimensional datasets. Grants: Multiple NSF and Air Force grants including "Clustering by Impact" (2002-2005) and "Data Mining Support for NIAC" (1999-2000). Scientific Awards: Outstanding Researcher, CS Department, George Mason University Best Paper Finalist at WWW 2002 Projects: Developed ADAM (Intrusion Detection), COOLCAT (Categorical Clustering), Fractal Mining, and Quasi-Cubes (Approximate Query Processing).

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

Corrado Loglisciمشاهده پروفایل

استادیار

• Temporal Data Mining
• Temporal Learning
• Textual Data Mining
• +۱۳ مورد دیگر

Corrado Loglisci is an Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. His research focuses on Temporal Data Mining , Machine Learning , and Quantum Computing , with applications in bioinformatics, medical informatics, and cybersecurity. He earned his Ph.D. in Computer Science with a thesis on temporal projection in longitudinal data. Research Highlights : Temporal Learning, Textual Data Mining, Quantum-Classical Hybrid Systems Collaborations : IRSTEA Research Institute (France), Aristotle University of Thessaloniki (Greece) His publications address dynamic network analysis , emotion detection in social media , and quantum-enhanced classification . He contributes to program committees and journal editorial work, including a special issue on Mining Complex Patterns in the Journal of Intelligent Information Systems . Notable contributions include the jKarma framework for change detection and studies on concept drift robustness in intrusion detection systems. His work spans European/National research projects, leveraging machine learning for tasks like mobile crowd sensing trustworthiness prediction (2020) and investor behavior analysis (2023-2025).

Miguel Sales Diasمشاهده پروفایل

پژوهشگر

• Machine Learning
• Smart Cities
• Immersive Technologies
• +۶ مورد دیگر

Miguel Sales Dias is affiliated with ISCTE - Instituto Universitário de Lisboa, specifically the Iscte Business School. His research spans multiple domains, including Machine Learning , Immersive Technologies , and Urban Mobility . His work focuses on: Developing AI-driven systems for energy consumption optimization in public buildings Creating co-design interfaces for housing customization Exploring mHealth applications for cardiac diagnostics Advancing virtual reality for education and visitor experiences Recent publications demonstrate expertise in smart city solutions , bioinformatics , and human-computer interaction . While specific awards or educational background weren't explicitly mentioned, his extensive publication record indicates significant contributions to interdisciplinary research.

• Graph Theory
• Data Mining
• Network Analysis
• +۲ مورد دیگر

Aristides Gionis is a Research Professor Fellow at Aalto University specializing in advanced network analysis and data mining. His work bridges theoretical graph algorithms with practical applications in dynamic and signed networks. His research focuses on graph theory and temporal network analysis , particularly in discovering dense subgraphs, community structures, and event patterns in evolving networks. Key methodologies include contrastive subgraph mining for explainable AI in brain networks and polarization detection in social systems. His publication trends reveal consistent contributions to top-tier venues like KDD, PAKDD, and ECML PKDD, with emphasis on algorithmic solutions for network segmentation, correlation mining, and interpretable classification. Primary application domains include social networks, neuroscience, and temporal data streams. As a Fellow at Aalto University, he leads research in computational network science without explicit departmental affiliation stated in available sources.

Angela Dewمشاهده پروفایل

استاد

• Disability and Inclusion
• People with cognitive disability
• Complex support needs
• +۹ مورد دیگر

Professor Angela Dew is the Discipline Lead for Disability and Inclusion at Deakin University's School of Health and Social Development within the Faculty of Health. With over 40 years of experience in the Australian disability sector and academic work since 2011, she leads the Disability and Inclusion research stream, focusing on participatory and co-designed research involving people with disability and complex support needs, including those from refugee backgrounds. Professor Dew earned her PhD from The University of Sydney in 2011 and completed her Masters (Hons) from the University of Western Sydney in 2005. Her academic journey reflects a deep commitment to disability inclusion and rights-based approaches to research and practice. Her research centers on understanding the lived experiences of people with disability and their families, with particular attention to those from refugee backgrounds, Aboriginal and Torres Strait Islander communities, and rural and regional settings. Professor Dew employs innovative arts-based research methods and maintains a strong human rights framework in all her work. Her research interests span complex support needs, LGBTQI issues in disability, dementia, abuse prevention, and sexuality and relationships. She fundamentally believes that 'all people are entitled to fairness, equity and a good life,' which drives her research and teaching philosophy. Professor Dew's publication record reveals a consistent focus on disability inclusion, with increasing emphasis on intersectional approaches that examine how disability intersects with refugee status, cultural background, and geographic location. Her recent work shows sophisticated methodological approaches, particularly in arts-based methods like body mapping and photovoice, which empower participants to express their experiences in non-traditional ways. As an educator, Professor Dew teaches in the postgraduate Masters of Disability and Inclusion program and contributes to undergraduate units. She actively supervises PhD students working on diverse aspects of disability research, including Phuong Do, Tom Voigt, Jane Adams, Peter Conway, and Simone Rowe, among others. Professor Dew has secured significant research funding from the National Disability Research Partnership, Australian Research Council Discovery, and UNSW Disability Innovation Institute. Her major projects include 'Co-designing Resources to Increase Access to Information and Services' for Syrian and Iraqi people with disability from refugee backgrounds, 'Women marginalised by mental health, disability or refugee status,' and 'Planning for a better life under the National Disability Insurance Scheme' for Aboriginal and Torres Strait Islander people with disability.

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

Zoi Kaoudi is a researcher at the IT University of Copenhagen , specializing in Data Management , Knowledge Graphs , and Machine Learning . Her work focuses on cross-platform data processing, query optimization, and scalable systems for graph analytics. She has published extensively in venues like SIGMOD , VLDB , and ISWC , with recent contributions to Apache Wayang , DORIAN , and Space-Efficient Graph Algorithms . Her research bridges theoretical advancements with practical frameworks for data science pipelines. Collaborations include Volker Markl, Jorge-Arnulfo Quiané-Ruiz, and Ioana Manolescu. She has explored topics such as Parameter Servers , Knowledge Graph Embeddings , and RDF Data Management in the cloud. Her work emphasizes open science and system integration.

• Internet of Things
• Business Process Management
• Model-Driven Development
• +۷ مورد دیگر

Prof. Estefanía Serral Asensio is an Associate Professor at the Faculty of Economics and Business (FEB) at KU Leuven , with a primary affiliation to the Information Systems Engineering Research Group (LIRIS) in Brussels. She holds a highly international and interdisciplinary academic profile, having previously served as an Assistant Professor at Eindhoven University of Technology (2018), led the Semantic Knowledge Representation and Integration research group at the Technical University of Vienna (2012–2014), and contributed to the ProS Research Center at the Technical University of Valencia (until 2012). PhD in Computer Science (2011) Master in Software Engineering, Formal Methods, and Information Systems (2008) 5-year Bachelor in Computer Science (2006) Her research focuses on Internet of Things (IoT) , Business Process Management , and context-adaptive systems , with methodological expertise in Model-Driven Development , Conceptual Modeling , and ubiquitous systems . Key projects include Novel Process Mining Techniques for Discovering IoT-enhanced Business Processes (2022–2025), Novel Sustainability-Driven IoT Prescriptive Analytics for Improving Irrigation Practices in Fruit Trees (2021–2024), and foundational work on Runtime Evolution of IoT Processes (2018–2020). Her publications span top-tier venues like CAiSE , ER , SOSYM , and Internet of Things Journal . She teaches courses in ICT Strategy and Architecture , ICT Management , and Research Methodologies in Business Information Systems Engineering , contributing to academic programs at KU Leuven.

Abdul-Rahman Mawlood-Yunisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Android Mobile Application Development
• Software Engineering
• +۸ مورد دیگر

Abdul-Rahman Mawlood-Yunis is an Associate Professor in the Department of Physics and Computer Science at Wilfrid Laurier University. His research focuses on Artificial Intelligence, Android Mobile Application Development, Software Engineering, Distributed Systems, and P2P Networking with an emphasis on fault-tolerant systems and semantic web technologies. He has contributed to frameworks for live streaming apps and machine learning algorithms for feature selection. Research interests include: Chatbots and Natural Language Processing (NLP) Ontology engineering and knowledge representation Algorithm design for distributed systems Mobile agent performance analysis Fault-tolerant semantic P2P networks His recent work (2022-2024) emphasizes machine learning applications in feature selection and real estate price estimation, reflecting a shift towards data-driven solutions. Earlier contributions (2003-2013) explored foundational aspects of mobile agents and semantic interoperability in P2P networks. Teaching responsibilities include courses on Android development and Java programming, with associated open-source materials and courseware. His book Android for Java Programmers provides foundational resources for students and instructors. Languages spoken: English, Kurdish, Arabic, Farsi.

• Computational Logic
• Artificial Intelligence
• Knowledge Representation
• +۵ مورد دیگر

Roles and Affiliations: Professor of Computational Logic at the Department of Computing, Imperial College London. Part of the Faculty of Engineering. Active in research, teaching, and publication in computational logic, artificial intelligence, and formal systems. Research Interests: Focuses on computational logic with applications to knowledge representation, action and agency theories, temporal reasoning, normative systems, legal reasoning, and formal organization models. Recent work also intersects with bioinformatics and proteomics analysis. Publications: Over 50 publications since 1986, including foundational work on the Event Calculus, normative systems, and formal methods in multi-agent systems. Notable contributions span deontic logic, agent coordination, and applications in legal reasoning and bioinformatics. Recent work emphasizes logic-based approaches to activity recognition and proteomic profiling. Teaching: Taught advanced courses on modal and temporal logic (2008–2009) and knowledge representation (2017–2018). Course materials emphasize formal semantics, nonmonotonic reasoning, and logic programming. Labs/Teams: Involved in interdisciplinary projects on computational systems, including collaborations with bioinformatics researchers and multi-agent system developers.

David Gerdesمشاهده پروفایل

استاد

• Astrophysics
• Cosmology
• Planetary Science
• +۳ مورد دیگر

David Gerdes is a Professor of Physics and former Department Chair at the University of Michigan, affiliated with the College of Literature, Science, and the Arts. His research primarily focuses on astrophysics and cosmology, with a strong emphasis on observational studies of the outer Solar System, including Kuiper Belt Objects (KBOs), Trans-Neptunian Objects (TNOs), and exoplanetary systems. He leads major projects like the DECam Ecliptic Exploration Project (DEEP) and contributed to the Dark Energy Survey (DES). Gerdes has discovered several dwarf planets and pioneered citizen science initiatives for solar eclipse observations. His work integrates advanced observational techniques, numerical simulations, and data analysis to explore planetary dynamics and galactic structures. Key research interests include the formation and evolution of the Solar System, dark matter distribution, and gravitational interactions. Gerdes is involved in projects like NASA’s New Horizons mission, using DECam and HST data to study KBOs. He has developed novel algorithms for detecting faint celestial objects and contributed to understanding Neptune’s resonances and potential undiscovered planets. His leadership roles include steering the Dynamic Eclipse Broadcast Initiative (DEB) and advancing collaborative science through citizen engagement. Gerdes’ work has advanced our knowledge of minor planets, cosmic surveys, and public astronomy outreach. He collaborates on interdisciplinary projects, such as the Rubin Observatory’s Rubin Survey and JWST spectroscopy of Neptune Trojans. His research bridges observational astronomy, computational modeling, and educational outreach, making significant contributions to both academic and public understanding of the cosmos.