جست‌وجوی استاد و پژوهشگر

Jian Tangمشاهده پروفایل

دانشیار

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

Jian Tang is an Associate Professor in the Department of Decision Sciences at HEC Montréal, holder of a Canada CIFAR Chair in Artificial Intelligence Education. He earned a Ph.D. in Computer Science from Peking University. His research focuses on machine learning, bioinformatics, and AI applications in drug discovery and protein design. He teaches courses like Machine Learning II: Deep Learning and has supervised four MSc projects in data science and AI applications. Key research interests include graph neural networks, knowledge graphs, protein structure prediction, and AI-driven scientific discovery. His recent work spans LLM reasoning, geometric diffusion models for molecular design, and multi-modal learning for biomedical applications. Notably, he received the HEC Montréal's 2022 New Researcher Award for exceptional scientific contributions. Publications highlight advancements in protein engineering, antibody design, and AI in healthcare. Collaborations include work with institutions like CDPQ on AI R&D and Bombardier on machine learning for demand forecasting. His lab explores cutting-edge AI techniques to address complex problems in medicine and computational biology.

Alan Wagnerمشاهده پروفایل

دانشیار

• Parallel Computing
• High Performance Computing
• Message-Passing Systems (MPI)
• +۵ مورد دیگر

Alan Wagner is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), where he has held tenure since 1987. He is a member of the Institute for Computational Intelligence and Cognitive Systems (ICICS). With over 35 years of experience, his research focuses on parallel and distributed computing, high-performance computing (HPC), and message-passing systems (particularly MPI). His work emphasizes latency reduction, scalability, and middleware development using protocols like SCTP and TCP. Education: Ph.D. in Computer Science, University of Toronto (1987) M.Sc. in Computer Science, University of Alberta (1983) B.Sc. (Honors) in Mathematics, Dalhousie University (1977) Research interests include parallel algorithms, distributed systems, and applications in computational finance and data analytics. He has contributed to open-source projects like FG-MPI (a fine-grain MPI implementation) and SCTP-based MPI middleware, enhancing MPI's performance on commodity networks. His work has been supported by grants such as the NSERC I2I Grant (2008) and CFI Leaders Opportunity Fund (2009). He has advised over 20 M.Sc. students and 2 Ph.D. students. His teaching includes courses on parallel algorithms (CPSC 521) and computer networking (CPSC 317). He currently chairs UBC's Computer Science Graduate Admissions Committee.

Richard Connorمشاهده پروفایل

استاد

• Similarity Search
• Metric Spaces
• High-Dimensional Geometry
• +۴ مورد دیگر

Richard Connor is a Professor of Computer Science at the University of St Andrews' School of Computer Science. He holds a BSc, PhD, and postdoctoral fellowships from the same institution, with prior roles including Director of Research and Head of Department at the University of Strathclyde. His research focuses on similarity search, high-dimensional geometry, and metric spaces, with contributions to algorithms like nSimplexZen and BitPart. He teaches distributed systems and advises PhD students, including Ben Claydon. Connor has engaged in tech startups and contributed to Scotland's computing education curriculum. His work addresses challenges in large-scale data search, leveraging geometric principles and innovative indexing techniques. Education: BSc and PhD from University of St Andrews (1981–1996) Key Roles: EPSRC Advanced Fellow, Chair at Strathclyde, Head of Division at Stirling, and return to St Andrews (2020) Research Interests: Connor investigates similarity search in high-dimensional spaces, focusing on metric geometry, Hilbert spaces, and dimensionality reduction. His work bridges theoretical foundations and practical applications like image retrieval and machine learning optimization. Recent projects include polyadic queries and Ptolemaic partitioning mechanisms to enhance search efficiency. Publications: His 2024 and 2025 works explore novel dimensionality reduction (nSimplexZen), cross-entropy loss correlations, and scalable polyadic queries, advancing the field through algorithmic innovation. Earlier contributions include BitPart for high-dimensional metric search and analysis of binary partitioning efficacy. Awards: Not explicitly listed, but his EPSRC Advanced Fellowship and academic leadership roles highlight recognition in the field. Advising & Grants: Supervised Ben Claydon’s PhD; led research funded via fellowships and institutional roles. Active in organizing academic events like Doors Open @ Computer Science. Labs/Teams: Engaged with MetricSearch research groups and collaborates internationally on similarity search projects.

David Morrisonمشاهده پروفایل

مدرس

• Medical imaging
• Computer vision
• Deep learning
• +۵ مورد دیگر

Mr. David Morrison is a Lecturer at the School of Computer Science, University of St Andrews. His research focuses on medical imaging, computer vision, deep learning, and generative models with applications in pathology and healthcare technology. He has contributed to projects such as the Automated Remote Pulse Oximetry System (ARPOS) and developed tools like wsipipe for digital pathology analysis. Research Interests: Medical imaging techniques, AI-driven diagnostics, metric search algorithms, and tangible computing applications. His work bridges theoretical computer science with practical healthcare solutions, emphasizing reproducibility and privacy in medical data. Selected Contributions: Published on cervical biopsy automation, endometrial cancer detection, and anonymizing pathology data using GANs. Collaborates with clinicians and engineers to advance digital diagnostics. Active in open-source tool development and public engagement through events like Doors Open @ Computer Science.

Jinyang Liمشاهده پروفایل

استاد

• Distributed systems
• Operating systems
• Machine learning systems
• +۳ مورد دیگر

Jinyang Li is a Professor of Computer Science at New York University's Department of Computer Science, part of the Courant Institute of Mathematical Sciences. His research focuses on distributed systems, machine learning systems, operating systems, and wireless networks. He holds a Ph.D. from MIT (2005) and a B.S. from the National University of Singapore (1998). He co-leads the NYU Systems Group and NYU WIRELESS. Key research interests include distributed training, fault tolerance, and parallel computing. Recent projects include Grendel (distributed 3D Gaussian Splatting) and Ares (memory-efficient GNN training). Awards: Distinguished Artifact Award (ASPLOS 2023), Best Paper Award (APsys'17) Teaching: Machine Learning Systems (Fall 2025), Computer Systems Organization (Spring 2026) Labs: NYU Systems Group, NYU WIRELESS Publications span topics like distributed protocols, database optimization, and deep learning systems, with a focus on scalability and efficiency. He advises students in collaboration with Prof. Panda, including Ding Ding, Haitian Jiang, and others.

Ziawasch Abedjanمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Database Systems
• +۵ مورد دیگر

Ziawasch Abedjan is a full professor of computer science and chair of the Data Integration and Data Preparation (D2IP Lab) Group at Technische Universität Berlin, part of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He holds a PhD from the Hasso Plattner Institute (2014) and was previously a junior professor at TU Berlin (2016–2020), with postdoctoral work at MIT (2014–2016). His research focuses on scalable methods for processing large heterogeneous datasets, emphasizing automated data preparation, extraction, and cleaning for data science workflows. Education: PhD in Computer Science, Hasso Plattner Institute (2014) MSc in Computer Science, Hasso Plattner Institute (2010) BSc in Computer Science, Hasso Plattner Institute (2008) Research Interests: Dr. Abedjan’s work bridges database systems and machine learning, addressing challenges in data integration, automated data cleaning, and scalable data science tools. His team develops systems like Blend for unified data discovery and MATE for multi-attribute table extraction. Recent projects include exploring the environmental impact of AutoML and advancing catalog enrichment techniques. Awards & Recognition: First Prize, GI Data Science Challenge (BTW 2023) SIGMOD Reproducibility Award (2019) Best Dissertation Award (2013/2014) Teaching & Service: Teaches foundational courses in data structures and databases. Serves on committees for major conferences (SIGMOD, VLDB) and chairs roles such as Reproducibility Chair for BTW (2023/2025). Active in editorial roles for journals including ACM JDIQ and IEEE Data Engineering Bulletin. Labs & Teams: Leads the D2IP Lab, collaborating with universities and industry. Current projects include TDC2 NFDI4DS. Offers thesis topics in data science lifecycle optimization and ML system design.

H. (Hans) Philippiمشاهده پروفایل

استادیار

• Database Systems
• Bioinformatics
• Data Science
• +۳ مورد دیگر

H. (Hans) Philippi is an Assistant Professor at Utrecht University, affiliated with the Department of Algorithmic Data Analysis under the Faculty of Science. His research focuses on database systems, bioinformatics, distributed systems, and algorithms. He can be reached at h.philippi@uu.nl and is located in Room BBL464 of the Buys Ballot building in Utrecht. Philippi's work spans multiple areas, including leveraging database technology for bioinformatics challenges like sequence alignment (e.g., BLAST emulation) and exploring distributed database architectures. His earlier research also involved object-oriented systems evaluation, reflecting a longstanding interest in software design and systems optimization. His publications highlight contributions to both theoretical and applied database systems, with a strong emphasis on algorithmic approaches to data analysis. No notable scientific awards have been mentioned in the records. Philippi has not listed any current or past advisees or grants in the provided information. His work is primarily conducted within the Algorithmic Data Analysis group, part of Utrecht University's broader AI & Data Science initiative.

Guanfeng Liuمشاهده پروفایل

استاد

• Recommender Systems
• Privacy-Preserving Technologies
• Graph Neural Networks
• +۵ مورد دیگر

Guanfeng Liu is the Vice Dean at the NNU-MQ Joint Institute, Macquarie University, within the Faculty of Science and Engineering. His research focuses on Recommender Systems, Privacy-Preserving Technologies, Graph Neural Networks, and Data Analytics. He leads projects like the 'Intelligent Health Data Analytics Platform' and 'Data-intensive Scheduling Optimisation in Database Systems.' His work emphasizes ethical AI, with contributions to trust prediction, spatio-temporal data analysis, and privacy-aware algorithms. In 2023, he won the Faculty of Science and Engineering Award for Inter-School Collaboration. Liu has published extensively in top journals like IEEE Transactions and ACM Conferences, with over 200 publications and a h-index of 34. Key projects include scalable document intelligence platforms and confidentiality preservation in graph learning. His research bridges theoretical advancements with practical applications in smart cities and healthcare, leveraging collaborative filtering and contrastive learning techniques.

Rakan Alseghayerمشاهده پروفایل

پژوهشگر

• Data Management
• Database Systems
• Distributed Systems
• +۴ مورد دیگر

Rakan Alseghayer is a PhD candidate in Computer Science at the University of Pittsburgh, affiliated with the Advanced Data Management Technologies (ADMT) Lab under Dr. Panos K. Chrysanthis and Dr. Constantinos Costa. He holds an MS in Information Science (Networking & Security) and a BS in Computer Science from King Saud University. His academic roles include Researcher at ADMT Lab (2022–2023) and multiple teaching roles as a Teaching Assistant and Lecturer at both Pitt and King Saud University. Education: PhD in Computer Science, University of Pittsburgh (2014–2023) M.Sc. in Information Sciences, University of Pittsburgh (2011–2013) B.Sc. in Computer Science, King Saud University (2004–2008) Research Interests: Rakan specializes in data management systems, spatiotemporal databases, distributed systems, and high-performance computing. His work emphasizes privacy-preserving technologies, contact tracing algorithms, and energy-efficient urban analytics. He has pioneered frameworks like e-Racoon for indoor contact tracing and DCS for real-time data stream correlation. Awards & Recognition: People’s Choice Best Pitch Video Award (IEEE MDM 2021) Black Hat Cybersecurity Student Scholarship (2022) King Saud University PhD Scholarship (2014–2020) Advising & Grants: Rakan contributed to projects like NIH-funded Causal MGM and TRIAD inequality forecasting. His work integrates software engineering (C++/Java/Python) with system design and security protocols. Labs & Teams: Member of ADMT Lab, collaborating on projects involving distributed systems, edge computing, and smart city technologies.

• Artificial Intelligence
• Machine Learning
• Network and Distributed Systems Management
• +۳ مورد دیگر

Jorge Lobo is an ICREA Research Professor at the Department of Information and Communication Technologies at Universitat Pompeu Fabra (UPF) and holds a Visiting Professor appointment at Imperial College London's Department of Computing. He holds a Ph.D. in Computer Science from the University of Maryland (1990), an M.S., and B.E. from Simon Bolivar University. His career includes roles at IBM Research, Bell Labs, and academic positions at the University of Illinois at Chicago. His research focuses on AI, Network & Distributed Systems Management, Security/Privacy, and policy-driven systems. He pioneered policy-based network management, developing the PDL language and contributing to policy enforcement frameworks in telecommunication networks. His work spans policy languages (XACML, PMAC), role mining algorithms, and security policy analysis. He holds 7 patents in policy technologies and has authored two books with over 100 refereed publications. Key contributions: Policy-based systems, neuro-symbolic learning, privacy-preserving frameworks, and declarative distributed computing Notable achievements: ACM Distinguished Scientist, IBM Identity Management Product implementations Recent publications (2020-2024) emphasize neuro-symbolic learning, federated learning frameworks, differential privacy, and AI-driven security solutions. His work bridges formal logic with machine learning to create interpretable systems for complex challenges in network management and policy enforcement. Labs/Teams: Active in UPF's Artificial Intelligence and Machine Learning group. Collaborates internationally on policy-aware systems and declarative networking architectures.

Paul Gratzمشاهده پروفایل

استاد

• Computer Architecture
• Multicore Processors
• Network-on-Chip (NoC)
• +۳ مورد دیگر

Paul Gratz is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, leading the Computer Engineering and Systems Group. He is also affiliated with the Computer Science & Engineering department. His research focuses on secure, efficient, and reliable design in high-performance computer architectures, including processor memory systems and on-chip interconnection networks. He holds a Ph.D. from the University of Texas at Austin (2008), and previously worked as a design engineer at Intel Corporation (1997–2002). Gratz has received prestigious awards, including the Best Paper Award at ASPLOS 2009 and teaching accolades from Texas A&M. Education Background: B.S. and M.S. in Electrical Engineering, University of Florida (1994, 1997) Ph.D. in Electrical & Computer Engineering, University of Texas at Austin (2008) Research Interests: Security in multicore/distributed architectures Processor memory systems and on-chip networks Hardware prefetching and cache coherence Energy-efficient design and reliability optimization Publications highlight innovations in network-on-chip (NoC) design, hardware security, and performance optimization. His work includes seminal contributions like the TRIPS processor evaluation (ASPLOS 2009) and pioneering research on branch prediction-driven prefetching (MICRO 2014). Awards also include the 2016 Distinguished Teaching Award and 2017 College of Engineering Excellence Award. Gratz's lab, the Computer Engineering and Systems Group (CESG), explores cutting-edge topics in computer architecture and system design, emphasizing practical applications and interdisciplinary collaboration.

Théo Le Calvarمشاهده پروفایل

استادیار

• Software Engineering
• Model-Driven Engineering
• Model Transformation
• +۴ مورد دیگر

Théo Le Calvar is an Assistant Professor in Software Engineering at IMT Atlantique in Nantes, France, specializing in Model-Driven Engineering and Constraint Solving. He holds a PhD in Computer Science from the University of Angers (2016–2019), with a focus on exploring model sets under the supervision of Frédéric Saubion, Fabien Chhel, and Frédéric Jouault. His academic career includes a Post-Doctorate at the University of Montreal and ERIS team (2020–2021), and roles as a Temporary Teaching and Research Associate at the University of Angers (2020). He has conducted research internships at the National Institute of Informatics in Tokyo (2016) and the LERIA lab (2015). His research interests include model transformations, constraint programming, incremental systems, and combinatorial optimization. He has published extensively in venues like the ACM/IEEE MODELS Conference and the International Conference on Model Transformations. His work integrates formal methods, such as process algebra and OCL-based analysis, with practical applications like anomaly detection and fruit basket management systems. Le Calvar advises engineering students on projects involving logic solvers, anomaly detection, and food waste reduction. He contributes to open-source tools like the Eclipse Modeling Framework and actively participates in workshops and seminars, including the Dagstuhl Seminar (2018) and the Transformation Tool Contest. His technical skills span Java, C++, Python, and tools like Docker and Ansible.

Gerhard Weikumمشاهده پروفایل

استاد

• Databases
• Information Systems
• Knowledge Graphs
• +۷ مورد دیگر

Gerhard Weikum is a leading professor and researcher in the field of databases and information systems at the Max Planck Institute for Informatics, part of the Max Planck Society in Saarbrücken, Germany. He heads the Databases and Information Systems department, where his work bridges foundational database research with cutting-edge applications in knowledge graphs, information retrieval, and natural language processing. His research interests include knowledge graph construction and curation, data integration, question answering, conversational search, and the integration of large language models with structured knowledge. He has made significant contributions to the development of methods for creating comprehensive and reliable knowledge bases from web and text sources, with a focus on scalability, quality, and usability. The recent publications highlight a strong trend towards hybrid AI systems that combine the strengths of knowledge graphs and large language models, particularly in the domains of conversational search, recommendation systems, and complex question answering over heterogeneous data. His work emphasizes efficiency, robustness, and faithfulness in AI-generated responses. ACM Web Conference WSDM CIKM Advances in Information Retrieval Journal of Web Semantics IEEE TKDE EMNLP He has advised numerous PhD students and researchers who have gone on to make their own contributions in areas such as knowledge base completion, data curation, and question answering. His scientific work is highly collaborative, involving researchers from various institutions and industrial partners. Gerhard Weikum is a central figure in the database and semantic web research communities, with a sustained record of high-impact publications and leadership in advancing the field of information systems.

Morgan Harveyمشاهده پروفایل

مدرس ارشد

• Information Retrieval
• Recommender Systems
• Information Behavior
• +۴ مورد دیگر

Dr. Morgan Harvey is a Senior Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. He holds a PhD from the University of Strathclyde (2011) and has held academic roles in the UK, Germany, and Switzerland. His research bridges systems-centric and user-centered Information Retrieval (IR), focusing on recommender systems, mobile search behavior, and health informatics. He has secured over £2.5 million in research funding, including an AHRC grant for historical record transcription using machine learning. Research interests include: information retrieval, recommender systems, health behavior nudging, mobile search interruptions, and digital government services. Recent projects include using AI to promote sustainable eating and analyzing the impact of distractions on mobile search tasks. He supervises PhD students in areas like healthy food recommendation systems and health information seeking on social media. His work is published in top journals like JASIST and Information Processing & Management. He is a member of the Information Retrieval Research Group and teaches modules like Database Design and Information Retrieval.

• Information Extraction
• Natural Language Processing
• Data Privacy
• +۳ مورد دیگر

Besat Kassaie is a Postdoctoral Researcher collaborating with Renée Miller. His research focuses on information extraction, natural language processing, and data privacy. Key areas include improving unstructured data quality through updatable extracted views, mathematical information retrieval (MathIR), and ontology matching techniques. His work spans theoretical advancements in information systems and practical applications in healthcare analytics. Notable contributions include frameworks for automated view maintenance, differential privacy methods for text data, and systems for detecting math answers using neural networks like Tangent-L. Kassaie's articles demonstrate a strong emphasis on database systems optimization, dynamic data processing, and the intersection of AI with mathematical problem-solving. His research bridges foundational computer science principles with real-world challenges in data privacy and interpretability.