جست‌وجوی استاد و پژوهشگر

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

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

• Information Retrieval
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Professor Klaus Berberich is a faculty member at htw saar (Saarland University of Applied Sciences), where he serves as Professor in the Databases & Information Systems department within the Faculty of Engineering. He is the Laboratory manager of the software laboratory (SWL) and Chairman of the examination boards for Practical Computer Science, Communication Informatics and Production Informatics. His research focuses on Information Retrieval, Machine Learning, Data Mining, and Web Archives, with significant contributions to knowledge graphs, temporal information retrieval, and neural information retrieval models. Professor Berberich has developed innovative approaches for quantity extraction from web tables, structuring text into tables, and knowledge graph querying. His publication record shows a consistent trend toward increasingly sophisticated neural approaches to information retrieval, evolving from traditional temporal search techniques to modern deep learning models. Recent work demonstrates strong integration of knowledge graphs with neural information retrieval systems, particularly in handling quantities and temporal aspects of information. Professor Berberich has received numerous prestigious awards throughout his career: 2020: Test of Time Award, ECIR 2020 2018: Honorable Mention for Best Poster Award, WWW 2018 2017: Prominent Paper Award, Artificial Intelligence Journal 2014: Highly Commended Poster Presentation Award, IIiX 2014 2013: Honorable Mention for Best Paper Award, CIKM 2013 2011: Best Demo Award, WWW 2011 2009: Best Late-Breaking Result Award, WSDM 2009 As an active researcher and educator, Professor Berberich serves on numerous program committees for major conferences including WSDM, CIKM, SIGIR, and ICTIR. He has been a consistent reviewer for prestigious journals in the field and is a member of the executive committee of the Information Retrieval specialist group of the German Informatics Society. His teaching portfolio includes courses in Databases, Information Retrieval, Data Science, Machine Learning, and Deep Learning. Professor Berberich leads the software laboratory (SWL) at htw saar and has been instrumental in developing research infrastructure for knowledge-centric tasks, including the GYANI indexing infrastructure. His research group has made significant contributions to temporal information retrieval, particularly in the context of web archives and news archives.

Jeffrey D. Heflinمشاهده پروفایل

استاد

• Semantic Web
• Ontologies
• Artificial Intelligence
• +۳ مورد دیگر

Jeffrey D. Heflin is a Professor in the Department of Computer Science & Engineering at Lehigh University, leading the Semantic Web and Agent Technologies (SWAT) lab. His research focuses on semantic interoperability, ontology reasoning, and distributed knowledge systems. He is a pioneer in Semantic Web research, having authored the first Ph.D. dissertation on the topic. Heflin contributed to key Semantic Web languages like SHOE, DAML+OIL, and OWL, and developed the Lehigh University Benchmark (LUBM), a standard for evaluating large-scale Semantic Web systems. He holds a Ph.D., M.S., and B.S. in Computer Science from the University of Maryland and the College of William and Mary. His service includes editorial roles for the Journal of Web Semantics and organizing ISWC conferences, including co-chairing ISWC 2012. He received the 2004 NSF CAREER Award for his work on distributed ontology systems. Heflin's research addresses challenges in data discovery, table search, and neural-symbolic integration. His lab explores scalable solutions for semantic integration, including projects like the 'Google for research data' initiative and advancements in contextual tag clouds for linked data exploration.

• Data Analytics
• Distributed Systems
• Query Optimization
• +۵ مورد دیگر

Jorge-Arnulfo Quiané-Ruiz is a researcher at Qatar Computing Research Institute , with affiliations at institutions like Technical University of Berlin and University of Nantes (PhD, 2008). His work focuses on data analytics , distributed systems , and query optimization , particularly in cross-platform environments and trusted execution frameworks . Research Interests : Data analytics across heterogeneous platforms Secure and compliant data processing in trusted environments Efficient query allocation and optimization Scalable algorithms for big data and streaming graphs Machine learning in data management Table extraction and data cleaning Publications : Recent work includes benchmarks for TEE-based joins and cross-database query frameworks (2023). Explores space-efficient graph algorithms and ML-driven revenue optimization (2022). Developed RHEEMix , a cost-based optimizer for cross-platform systems (2018). Collaborations : Regularly partners with Volker Markl and Zoi Kaoudi on unified analytics frameworks. Contributed to projects like Apache Wayang and AgoRA .

Motagh M.مشاهده پروفایل

پژوهشگر

• Remote Sensing
• Geodesy
• Seismology
• +۳ مورد دیگر

Motagh M. is a Researcher at the Deutsches GeoForschungsZentrum (GFZ) in Potsdam, Germany. With expertise in Remote Sensing and Geodesy , their work focuses on geohazard assessment through satellite-based interferometric techniques. Current research explores land subsidence in Afghanistan and Iran using Sentinel-1 data Developing hybrid machine learning models for landslide susceptibility assessment Pioneering quantum machine learning applications in deformation detection Investigating mining-induced subsidence in Germany's Hambach region Contributing to multi-sensor InSAR integration for complex geological analysis Publications since 2011 demonstrate sustained contributions to: Earthquake source modeling (e.g., Christchurch 2011, Maule Chile 2010) Volcanic deformation studies in Iceland Environmental geoscience applications across three continents Collaborations span institutions in: New Zealand (University of Canterbury, GNS Science) Iran (University of Tehran, K. N. Toosi University) Germany (GFZ Potsdam, Technical University Munich) USA (University of Miami)

Ruth Godfreyمشاهده پروفایل

دانشیار

• Analytical Technology Development
• Applied Chromatography
• Mass Spectrometry
• +۳ مورد دیگر

Dr. Ruth Godfrey is an Associate Professor in Biomedical Sciences at Swansea University Medical School, Faculty of Medicine, Health and Life Science. With over a decade of academic experience, she has progressed from Lecturer (2011-2017) to Senior Lecturer (2017-2020) and currently serves as Associate Professor (2020-present). Her work bridges academic research and practical applications, supporting R&D for multinational companies, government agencies, and SMEs through her expertise in analytical technologies. Dr. Godfrey's research focuses on analytical technologies and method development for medical and chemical analysis, with particular emphasis on environmental medicine, mass spectrometry, and sample preparation technology. Her work spans pharmaceutical analysis, environmental monitoring, and clinical applications, with a strong focus on developing practical analytical solutions for complex real-world problems. She has made significant contributions to the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) methodology, adapting it for environmental and clinical matrices, and has developed educational tools like the 'Periodic Table' for liquid chromatography separation modes and mass spectrometry instrumentation. Her publication record demonstrates consistent productivity across environmental analysis, mass spectrometry, and chromatography. The trends in her work show increasing focus on environmental applications of analytical techniques, particularly regarding pollutant monitoring and sustainable analytical methods. While maintaining strong foundations in mass spectrometry fundamentals, her recent work has expanded into green chemistry applications and the intersection of analytical science with environmental sustainability. Dr. Godfrey has received significant recognition through her professional designations: Chartered Chemist Chartered Scientist Fellow of The Higher Education Academy She has successfully secured diverse research funding from UKRI, EU programs, industry partners, and charities, totaling over £2 million across multiple projects. Her supervision record includes PhD, MRes, and MPhil students, with current projects focusing on environmental and health impacts of chemical pollutants. Beyond her university role, she actively contributes to the scientific community as Secretary of the RSC Analytical Methods Committee's Instrumental Analysis Expert Working Group and as an RSC Chemnet Ambassador promoting science education.

DENG Yangمشاهده پروفایل

استادیار

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +۷ مورد دیگر

Dr. Yang Deng is a tenure-track Assistant Professor at the School of Computing and Information Systems, Singapore Management University, and a Lee Kong Chian Fellow. Previously, he was a Postdoctoral Research Fellow at NExT++ (National University of Singapore). His research focuses on Natural Language Processing, Information Retrieval, and Large Language Models, with special interests in Proactive Conversational AI, Trustworthiness of LLMs, and Human-Centered Information Seeking. He has published over 40 papers in top-tier venues including ACL, EMNLP, WWW, and SIGIR. PhD from The Chinese University of Hong Kong (2023) Research Advisor to CHEANG Chi Seng Research Domains: Natural Language Processing Information Retrieval Large Language Models Human-Agent Interaction Digital Transformation Trustworthy AI Scientific Recognition: Lee Kong Chian Fellowship Google South Asia & Southeast Asia Research Awards 2024 EMNLP 2024 Outstanding Area Chair NeurIPS 2024 Best Reviewer

Ralph Peetersمشاهده پروفایل

پژوهشگر

• Entity Matching
• Data Integration
• Large Language Models
• +۳ مورد دیگر

Ralph Peeters is a Postdoctoral Researcher at the Data and Web Science Group within the University of Mannheim . He specializes in entity matching, data integration, and web-based systems. His research explores the application of large language models and deep learning techniques to improve product matching and knowledge graph construction. Focus areas: Entity Matching, Data Integration, Large Language Models Teaching roles: Course Creator/Lecturer for LLM-based Agents, TA for Data Mining, Web Mining, and Web Data Integration (2019–2025) Collaboration: Works under the supervision of Prof. Dr. Christian Bizer Recent publications highlight his work on benchmarks like WDC products and SOTAB, which leverage schema.org annotations and semantic web technologies for improving data quality and integration at scale. His research combines traditional NLP techniques with modern deep learning approaches.

Anh Nguyenمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Generative Modeling
• +۵ مورد دیگر

Anh Nguyen is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, affiliated with the Samuel Ginn College of Engineering. His work focuses on deep learning, computer vision, and explainable AI. He holds a Ph.D. from the University of Wyoming and a B.S. from Assumption University. Research highlights include developing methods to improve AI robustness, analyzing biases in large language models, and creating tools for visual correspondence in image processing. He leads the Center for Artificial Intelligence and Cybersecurity Engineering and directs Auburn's first K-6 AI education program through his AI Club after-school initiative. Recipient of a $460,736 NSF CAREER Award for AI innovation Developed the AI@AU lecture series and multiple benchmark datasets (e.g., ImageNet-Hard) Collaborates with industry partners on real-world AI applications Recent projects explore multimodal model limitations (Zerobench), medical imaging (LiteGPT for chest X-rays), and interactive AI systems that incorporate human feedback. His work bridges theoretical advancements with practical implementations in healthcare, wildlife monitoring, and education.

Yaser Oulabiمشاهده پروفایل

پژوهشگر

• Knowledge Base Completion
• Web Data Integration
• Entity Extraction
• +۳ مورد دیگر

Yaser Oulabi is a researcher at the University of Mannheim's Chair of Information Systems V: Web-based Systems. His work focuses on knowledge base augmentation through web data extraction, particularly developing methods to integrate long-tail entities from web tables into cross-domain knowledge graphs like DBpedia and Wikidata. Key contributions include creating the Time-Dependent Ground-Truth Dataset for evaluating temporal data fusion and curating the Web Tables for Long-Tail Entity Extraction benchmark. His research addresses challenges in entity resolution for sparse datasets, temporal metadata estimation using knowledge-based trust metrics, and weak supervision approaches for identifying rare entities. Dr. Oulabi's frameworks enable structured web data consolidation across domains through innovative conflict resolution and version management techniques. Recent publications examine how web table characteristics influence knowledge base expansion potential and develop data programming methods for entity extraction in low-resource domains.

Julie Wallمشاهده پروفایل

استاد

• Artificial Intelligence
• Advanced Computing
• Deep Learning
• +۱۲ مورد دیگر

Professor Julie Wall is a faculty member at the University of West London, serving as Professor of AI and Advanced Computing in the School of Computing and Engineering. She is actively engaged in research, teaching, and professional service, including her role as an expert at the British Standards Institution (BSI) in the domain of artificial intelligence. Research Interests: Julie Wall's research is centered on the design and application of intelligent systems for processing and modeling temporal data, particularly in speech and language. She leverages neural network architectures—ranging from biologically inspired models to computationally efficient deep learning systems—to analyze diverse data types such as audio, video, images, tabular data, and 3D features. Her work extends to developing production-grade deep learning and natural language understanding systems for immersive environments like virtual and augmented reality. Publications and Research Trends: Her body of work, comprising over 50 high-quality papers and multiple patents, reflects a strong trajectory in AI systems that integrate multimodal data with temporal dynamics. The research spans core areas of machine learning, natural language processing, and applied AI, with increasing focus on real-world deployment, efficiency, and intelligent interaction in extended reality platforms. Scientific Awards: US Patent UK Patent Advising and Grants: Julie Wall supervises research students across disciplines including forensic science and artificial intelligence. She contributes extensively to academic programs, teaching courses such as BSc and MSc in Computer Science, Artificial Intelligence, Data Science, and specialized AI programs in cybercrime and criminal justice. While specific grant details are not listed, her patent holdings and publication volume suggest sustained research funding and project leadership. Labs and Teams: As a leading researcher in AI and advanced computing, she is likely involved in or leads research groups focused on intelligent systems, deep learning, and multimodal AI at the University of West London, though specific lab names are not mentioned in the text.

• Electrical Engineering
• Circuit Theory
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Teresa Maria Canavarro Menéres Mendes de Almeida is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon), and a researcher at INESC-ID. She specializes in circuit theory, signal processing, and biosensor technologies. Her teaching focuses on core electrical engineering courses such as Circuit Analysis, Analog/Digital Filters, and Electronics fundamentals, for which she has received multiple teaching excellence awards from the IST Pedagogical Council (2013-2019). Her research interests span resistive circuits, magnetoresistive biosensors, and biochip-based microsystems. She has developed educational materials like problem sets and lecture slides on filters and circuits, alongside applied work in biomedical embedded systems and sensor modeling. Collaborations include projects on portable biosensing platforms and noise analysis in biochip elements. Notable achievements include a series of teaching excellence awards highlighted for courses like 'Circuit Theory and Fundamentals of Electronics' and 'Analog and Digital Filters.' Her work bridges theoretical circuit analysis with practical applications in biomedical engineering and digital signal processing.

Niloofer Shanavasمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۵ مورد دیگر

Niloofer Shanavas is an Assistant Professor in the School of Computer Science at the University of Birmingham, Dubai campus. She holds a PhD in Computer Science from Ulster University, UK (2020), and an M.Tech in Computer Science and Engineering with specialization in Information Systems from Rajagiri School of Engineering and Technology, India (2014). Her research focuses on artificial intelligence, machine learning, natural language processing, text mining, and semantic computing . She develops innovative approaches combining graph-based methods, ontologies, and deep learning models to enhance text classification, information extraction, and knowledge representation in both medical and technical domains. Recent publications demonstrate a strong trend in applying advanced NLP techniques—particularly graph-based learning, contextual embeddings, and large language models—to challenging real-world problems such as clinical concept annotation, tender document analysis, and structured entity extraction from unstructured and tabular data. Scientific Awards: No scientific awards mentioned in the provided text. She is actively engaged in research and publication, with recent contributions in 2024 and upcoming works in 2025. Although her advisees and grant funding are not listed, her collaborative work with researchers such as H. Wang, Z. Lin, G. Hawe, and A. Abbas indicates strong research team involvement. Her work suggests leadership in developing knowledge-driven AI systems for complex document understanding. Labs and Research Teams: While specific lab affiliations are not mentioned, her research outputs imply active participation in NLP and AI research groups, particularly focused on semantic computing and document intelligence.

Michael Sejr Schlichtkrullمشاهده پروفایل

مدرس

• Natural Language Processing (NLP)
• Automated Reasoning
• Fact-Checking
• +۳ مورد دیگر

Michael Sejr Schlichtkrull is a Lecturer at the School of Electronic Engineering and Computer Science , Queen Mary University of London. His research focuses on automated reasoning using large language models (LLMs) and NLP systems for fact-checking and handling complex epistemological challenges, with a focus on integrating structured data sources like knowledge graphs, tables, and parse trees into verification workflows. His academic journey includes a PhD in Natural Language Processing from the University of Amsterdam (2021) and postdoctoral research at the University of Cambridge and Fitzwilliam College , where he developed automated fact verification systems under Andreas Vlachos. Research trends include cross-format verification (tables/web evidence), GNN interpretation , and improving question answering systems through unified knowledge representations. Recent work explores claim decontextualisation and diversity evaluation in NLP generation. Scientific Awards : Best student research paper at ESWC, 2018 Best paper at CogInfoCom, 2015

• Geographic Information Systems
• Knowledge Graphs
• Historical GIS
• +۳ مورد دیگر

Nathalie Abadie is a Researcher at the Geographic Information Science and Technology Laboratory (LaSTIG) within the National School of Geographic Sciences at the University of Paris-East. She leads research in geographic information science with a focus on knowledge capture, geohistorical data, and semantic web technologies. Her work bridges historical geography, digital humanities, and artificial intelligence. Her research interests include structured data matching with geographic reference datasets, creation of geohistorical knowledge graphs, and knowledge acquisition for geographic data. She develops methodologies for spatial named entity linking, multimodal image matching, and historical data integration, particularly applied to urban evolution studies of Paris from 1789-1950. Dr. Abadie's recent publications demonstrate trends in geohistorical knowledge graph construction, historical document analysis, and real-time geolocation applications. Her work increasingly integrates machine learning with traditional GIS techniques to address challenges in historical data processing and disaster response. She actively advises PhD students including Solenn Tual, Charly Bernard, and Helen Mair Rawsthorne, and has led major research projects such as SoDuCo (Study of Urban Spatial Structures Evolution) and Mezanno (Collaborative Annotation Tools). Her grants include CNRS-funded initiatives in digital humanities and geospatial AI. Dr. Abadie co-leads the STRUDEL research team and directs multiple national working groups including the CNRS GDR MAGIS commission on Geohistorical Knowledge Graphs. She organizes major conferences including the French Knowledge Engineering Conference and workshops on Digital Humanities and Artificial Intelligence.