جست‌وجوی استاد و پژوهشگر

Xiaokui Xiaoمشاهده پروفایل

استاد

• Data Management
• Data Analytics
• Data Privacy
• +۵ مورد دیگر

Prof. Xiaokui Xiao is a Professor and Deputy Head (Research & Graduate Matters) at the Department of Computer Science, National University of Singapore (NUS). He previously held an Associate Professor position at Nanyang Technological University (NTU). His academic journey includes a PhD from the Chinese University of Hong Kong (2008) and a postdoctoral stint at Cornell University. His research focuses on data management and analytics, with emphasis on large data algorithms, privacy (e.g., differential privacy), and data mining. Notable contributions include the PrivBayes system for private data release and scalable network embedding techniques. Awarded the 2024 ACM SIGMOD Test-of-Time Award, IEEE Fellowship (2022), and ACM Distinguished Membership (2021), Xiao is also a trustee of the VLDB Endowment and Editor-in-Chief of Proceedings of the VLDB Endowment . He has advised over 30 PhD students and researchers, many of whom hold academic or industry leadership roles. His work bridges theoretical foundations with practical systems, addressing challenges in privacy, scalability, and machine learning integration.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

• Data-efficient deep learning
• Adversarial attacks
• Deep learning interpretations
• +۴ مورد دیگر

Anshul Thakur is a Departmental Lecturer in Clinical Machine Learning at the University of Oxford's Institute of Biomedical Engineering. His research focuses on advancing data-efficient deep learning techniques, adversarial attacks, and interpretable AI frameworks for healthcare applications. He holds a PhD from IIT Mandi (2020), where his thesis explored audio signal analysis using dynamic kernels and deep learning. Education: PhD in Computing & Electrical Engineering, Indian Institute of Technology Mandi (2020) Research concentrated on bioacoustic signal pattern analysis and ML frameworks for acoustic classification. Research Interests: His work emphasizes clinical AI applications, including federated learning for medical data, multimodal diagnosis systems, and mitigating class imbalance in healthcare datasets. He develops interpretable models for medical practitioners and explores ethical AI deployment in clinical settings. Recent Trends in Publications: Recent work addresses federated learning optimization, multimodal clinical diagnosis, and early disease prediction using biomarker patterns. His studies highlight innovations in EHR analysis, privacy-preserving techniques, and cross-domain medical model adaptation. Labs & Teams: Active in the Institute of Biomedical Engineering, collaborating on projects like the RapiD_AI framework for pandemic preparedness and Continuous Patient State Attention Models for irregular EHR data analysis.

George Kolliosمشاهده پروفایل

استاد

• Temporal Indexing
• Spatio-Temporal Data Management
• Data Mining
• +۴ مورد دیگر

George Kollios is a Professor and Chair of the Computer Science Department at Boston University. He holds a PhD in Computer Science from Polytechnic University, New York (2000), an M.Sc. from the same institution (1998), and a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, Greece (1995). His research focuses on temporal/spatio-temporal indexing, database security, privacy-preserving technologies, and large-scale data management. His work is supported by NSF (including an NSF CAREER Award) and IARPA. Education: PhD, Polytechnic University, New York (2000) M.Sc., Polytechnic University, New York (1998) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1995) Research Interests: His expertise spans data mining , secure database systems , and privacy-preserving algorithms . He develops methods for efficient query processing on encrypted data, spatio-temporal data management, and scalable clustering techniques. Recent Research Trends: His publications emphasize differential privacy , secure encryption schemes , and large-scale graph algorithms . Notable contributions include optimizing privacy in database queries and evaluating encryption protocols for secure data access. Awards: NSF CAREER Award Grants & Collaborations: Recipient of NSF-funded projects on access pattern privacy and IARPA-supported research. He collaborates on interdisciplinary initiatives to bridge theoretical foundations with practical security frameworks.

Paula Brancoمشاهده پروفایل

استادیار

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

Paula Branco is an Assistant Professor at the School of Electrical Engineering and Computer Science, University of Ottawa (since 2020). Previously, she was a PostDoctoral Fellow at Dalhousie University's Faculty of Computer Science (2019). She holds a PhD in Computer Science from the University of Porto, alongside a MSc in Data Mining and a Mathematics degree. She is an external researcher at INESC TEC's LIAAD laboratory. Her research focuses on Machine Learning, particularly utility-based learning and imbalanced domains, with applications in cybersecurity, healthcare, and environmental monitoring. Key areas include anomaly detection, fraud detection, rare event prediction, and spatio-temporal data analysis. Recent work emphasizes resampling techniques for imbalanced regression (e.g., SMOGN, ImbalancedLearningRegression Python package), evaluation metrics for imbalanced domains, and cybersecurity applications like intrusion detection and malware analysis. She has supervised seven graduate students and contributed to open-source projects like UBL (R package) and ImbalanceMetrics. Her teaching includes courses on databases and AI for cybersecurity. She actively publishes in top venues and collaborates on tools like SMOGN-LIDTA17 and NeighborhoodBiasResamplingRegression.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +۱۱ مورد دیگر

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Qiongkai Xuمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Security
• +۳ مورد دیگر

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

Lambert Schomakerمشاهده پروفایل

استاد

• Artificial Intelligence
• Pattern Recognition
• Machine Learning
• +۶ مورد دیگر

Prof. dr. Lambert Schomaker is a Full Professor of Artificial Intelligence at the University of Groningen, leading the Artificial Intelligence and Cognitive Engineering (ALICE) institute within the Faculty of Science and Engineering. His work spans perceptual intelligence, machine learning, and neuromorphic computing, with significant contributions to handwriting recognition, robotics, and historical document analysis. Affiliations: Bernoulli Institute, CogniGron (Cognitive Systems & Materials), and Data Science & Systems Complexity (DSSC) centers. Education: M.Sc. (1983) and Ph.D. (1991) in Psychophysiology/Psychology from Nijmegen University. Research Interests: AI, pattern recognition, neural networks, autonomous systems, and applications in cultural heritage (e.g., the MONK system for handwritten archive indexing). He has pioneered handwriting recognition methods used in modern devices like tablets and led the 30MEuro Target project for large-scale data mining. Grants & Projects: Includes NWO-funded initiatives (e.g., Catch, TriGraph) and EU projects (MANTIS, MANIC). Current focus areas include neuromorphic computing with electronic materials and AI-driven analysis of the Dead Sea Scrolls. Awards: IBM Faculty Awards (2011, 2012), IAPR Best Paper Award (2012), Ching Yee Suen Special Award (2012). Advising: Supervised over 20 PhD students in AI, robotics, and document analysis. Active in industry collaborations (e.g., HP, Microsoft). Labs/Teams: Director of ALICE, contributor to CogniGron, and leader of the MONK system team. Current research includes neuromorphic hardware development and AI applications in maintenance systems.

Andrew B. Nobelمشاهده پروفایل

استاد

• Statistical Genomics
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Andrew B. Nobel is the Robert Paul Ziff Distinguished Professor of Statistics and Operations Research and Professor of Biostatistics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Lineberger Comprehensive Cancer Center and the Computational Genomics Program. His research focuses on statistical genomics, machine learning, network analysis, and inference from dynamical systems. Nobel has collaborated extensively with researchers in Biology, Computer Science, Genetics, and Mathematics. Dr. Nobel earned his PhD in Electrical Engineering from Stanford University (1992), an MS from Stanford (1988), a Certificate of Study in Mathematics from Cambridge University (1986), and a BS in Electrical Engineering from Cornell University (1985). He has taught courses ranging from undergraduate discrete mathematics to advanced graduate-level theoretical statistics and machine learning. His research interests include developing methodologies for analyzing complex biological and network data, with applications to cancer genomics, systems biology, and medical informatics. Recent work emphasizes optimal transport methods for network analysis and statistical approaches for genomic data integration. Awards: Elected Fellow of the Institute of Mathematical Statistics (2008) National Science Foundation CAREER Grant (1995) Beckman Institute Fellow (1992–1995) Churchill Scholar (1985–1986) Dr. Nobel serves on the editorial board of the Journal of the Royal Statistical Society, Series B and has previously contributed to the Annals of Statistics and IEEE Transactions on Information Theory . His work bridges theoretical statistical foundations with practical applications in public health and biomedical research.

• Network Engineering
• Internet Infrastructure
• Data Analysis
• +۳ مورد دیگر

Zachary Bischof is a Researcher at Georgia Tech's School of Computer Science within the College of Computing. His work focuses on internet infrastructure, network reliability, and broadband analysis. He investigates topics such as outage detection, submarine cable networks, and the geographic and technical aspects of internet systems. His research spans from foundational studies on IPv4 utilization to applied projects like the iGDB platform integrating physical/logical network layers. Key areas of interest include internet measurement methodologies, infrastructure resilience, and the societal impact of telecommunications policies. He has contributed to improving outage detection algorithms, analyzing AS relationships, and evaluating consumer broadband experiences across diverse regions. His work often bridges technical and policy dimensions, emphasizing real-world applications. Notably, his publications explore undersea cable criticality, Google's AMP project's impact, and the utility of service-level agreements (SLAs) for broadband providers. While no formal awards are listed, his consistent output reflects sustained engagement with high-impact networking challenges. Zachary collaborates on projects like the Dasu platform for edge-based ISP measurements and has advised on submarine cable vulnerability assessments. His research integrates machine learning (e.g., hostname geolocation extraction) with traditional networking analysis, demonstrating interdisciplinary strengths. Future directions include expanding measurement frameworks and addressing emerging infrastructure challenges.

Hassan Karimiمشاهده پروفایل

استاد

• Geoinformatics
• Machine Learning
• Location-Based Services
• +۵ مورد دیگر

Hassan Karimi is a Professor at the University of Pittsburgh's School of Computing and Information, Department of Informatics and Networked Systems. His research focuses on Geoinformatics, Machine Learning, Location-Based Services, and Navigation Applications, with expertise in Mobile Computing and Distributed/Parallel Computing. He holds a Ph.D. in Geomatics Engineering from the University of Calgary, along with an MS in Computer Science from the University of Calgary and a BS in Computer Science from the University of New Brunswick. His research interests encompass computational geometry, geospatial data science, and smart city technologies. Notable contributions include methodologies for obstacle detection for visually impaired navigation, collaborative wayfinding systems, and spatiotemporal activity prediction. He leads the Geoinformatics Laboratory, which explores geospatial data science, mobile computing, and navigation systems. Key publications include works on geospatial data science techniques, navigation systems, and machine learning applications. His lab develops technologies for autonomous vehicles, environmental monitoring, and precision agriculture. He is the author of several books, including *Geospatial Data Science Techniques and Applications* (Taylor & Francis, 2018) and *Big Data: Techniques and Technologies in Geoinformatics* (Taylor & Francis, 2014). His work bridges theoretical research and practical applications, addressing challenges in smart cities, environmental sustainability, and health informatics. The Geoinformatics Lab collaborates on projects such as sensor networks, spatial data mining, and geovisualization.

Giuseppe Vinciمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +۸ مورد دیگر

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

Mathias Wellerمشاهده پروفایل

استاد

• Parameterized Algorithmics
• Genome Scaffolding
• Phylogenetic Networks
• +۳ مورد دیگر

Mathias Weller is a Professor and Chair of the AKT group at Université Gustave Eiffel (Paris), France. Previously, he held a Full-Time Researcher position at CNRS/LIGM (2018–2022) and postdoctoral roles at LIRMM (2013–2017). He earned his PhD in Theoretical Computer Science from TU Berlin (2012) and a Diploma in Computer Science from Friedrich Schiller University Jena (2009). His research focuses on Parameterized Algorithmics, Genome Scaffolding, Phylogenetic Networks, and Structural Parameterization. He has contributed extensively to algorithm design for computational biology, including preprocessing techniques and kernelization concepts. Weller’s work bridges theoretical computer science and applied bioinformatics, addressing challenges in phylogenetic analysis, genome assembly, and combinatorial optimization. His publications span algorithmic efficiency, network analysis, and complexity theory, with a focus on practical applications in genomics and phylogenetics.

Justus Selwynمشاهده پروفایل

استاد

• Software Engineering
• Knowledge Management Systems
• Object-Relational Data Modeling
• +۵ مورد دیگر

Dr. Justus Selwyn is a Professor and Department Chair of Computer Science at John Brown University (JBU), where he teaches and conducts research in Software & Knowledge Engineering. He previously served academia and industry in Indian universities from 2001-2021. His expertise includes knowledge management systems, object-relational data modeling, and software engineering. He holds a Ph.D. from Madurai Kamaraj University, an M.C.A. from The American College, and a B.Sc. from MS University. Research Interests: Knowledge Management Systems, Software Engineering, Machine Learning, Web Services, and Data Mining. Publications: Over 54 journal articles, 20+ conference papers, and 2 co-authored books. Recent work focuses on knowledge representation models and service-oriented architectures. Consulting: Part-time software consultant for companies like Ericson India and PhantomFX, specializing in software solutions for SMEs and Christian organizations. Dr. Selwyn actively contributes to music ministry through his vocal quartet Chordiels and produces gospel music. He documents Christian hymns in Tamil and publishes weekly. Married to Hepsiba (also a JBU professor), they have a son named Handel and serve their church community through music.

Evaggelia Pitouraمشاهده پروفایل

استاد

• Data Management
• Distributed Databases
• Database Systems
• +۷ مورد دیگر

Evaggelia Pitoura is a Professor in the Department of Computer Engineering and Informatics at the University of Ioannina, Greece, and a Lead Researcher at the Archimedes Research Unit, ATHENA Research Center. Her work spans data management, distributed systems, and mobile computing, with a strong focus on database personalization and diversity. Her research interests include data diversity, contextual preferences, XML data management, peer-to-peer systems, and mobile computing . She has pioneered work in result diversification, preference-aware querying, and distributed data indexing, publishing extensively in top venues such as PVLDB, ICDE, SIGMOD, and IEEE TPDS. The recent publications highlight a consistent trend in personalized, context-aware database systems , with emphasis on improving user experience through intelligent query processing, recommendation, and efficient data organization in distributed environments. Best Paper Award, SIGMOD 1999 Best Paper Award, PVLDB 2013 She has advised several PhD students, including Georgia Koloniari, Kostas Stefanidis, Marina Drosou, and Konstantinos Semertzidis, who now hold positions in academia and industry. She teaches courses in Databases and Information Retrieval and actively contributes to the academic community through program committee roles in major conferences like ICDE, VLDB, SIGMOD, and WWW. She leads the Distributed Management of Data Group , fostering innovation in scalable and intelligent data systems.