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Dan RothView profile

Professor

• Machine Learning
• Natural Language Understanding
• Computational Learning Theory
• +4 more

Dan Roth is the Eduardo D. Glandt Distinguished Professor at the University of Pennsylvania in the School of Engineering and Applied Science , Department of Computer and Information Science . His research integrates machine learning and inference methods to advance natural language understanding, focusing on semantic modeling, information extraction, and constrained conditional models. Research Interests : Dan’s work spans machine learning , inference methods , and their applications in natural language processing , including semantic role labeling, co-reference resolution, and textual entailment. He emphasizes incidental supervision and declarative learning-based programming via tools like LBJava and Saul. Article Trends : Recent publications highlight multimodal learning , LLM uncertainty quantification , and temporal table reasoning , with keywords like Computer Science , Artificial Intelligence , and Medical Informatics . Subfields include vision-language models , hybrid SQL-LLM systems , and multilingual guardrails . Scientific Awards : DARPA Information Science and Technology Advisory Panel (2020) IJCAI John McCarthy Award (2017) Fellow of AAAS (2014) University of Illinois College of Engineering Advising Award (2013) Fellow of ACL (2012) Fellow of ACM (2011) Best Student Paper at CoNLL (2011) Best Paper at Army Science Conference (2010) Fellow of AAAI (2009) Xerox Faculty Research Award (2005) Advising and Grants : Dan has advised PhD students such as Sihao Chen and Haoyu Wang . His projects include BLINK (multimodal LLMs) and DiverseAgentEntropy (LLM uncertainty quantification). Labs and Teams : Dan leads the Cognitive Computation Group , which focuses on computational theories of intelligence and their implementation in machine learning and NLP systems.

Anqi LiuView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Distributionally Robust Learning
• +5 more

Anqi Liu (Angie) is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She is affiliated with the Data Science and AI Institute, Mathematical Institute for Data Science (MINDS), and Institute for Assured Autonomy (IAA). Her research focuses on developing principled machine learning algorithms for reliable, trustworthy, and human-compatible AI systems in high-stakes applications. PhD in Computer Science from University of Illinois Chicago Postdoctoral Research at Caltech's Department of Computing and Mathematical Sciences Her work emphasizes robustness to changing data environments, uncertainty quantification, and human-AI interaction. Key methodologies include distributionally robust learning, active learning, safe exploration, fair machine learning, and conformal prediction. Applications span healthcare (NIA/NIH-funded), robotics, and computational social science. Amazon Research Award Johns Hopkins + Amazon Initiative for AI Faculty Research Johns Hopkins Discovery Award Institute for Assured Autonomy Challenge Grant She advises PhD candidates in AI safety and fairness, with students co-advised by faculty in Human-Robot Interaction and Computational Linguistics. Collaborations include Center for Language and Speech Processing (CLSP) and Laboratory for Computational Sensing and Robotics (LCSR).

• Information Retrieval
• Knowledge Graphs
• Natural Language Processing
• +5 more

Florina Piroi is a Senior Scientist (Research Fellow) at the Center for Research Data Management (E058-06) within the Faculty of Informatics at TU Wien. Her research integrates information retrieval, knowledge graphs, and semantic technologies to advance exploratory search systems and research data infrastructure. Her core research interests include: Information Retrieval evaluation and validation methodologies Knowledge graph applications for domain-specific search Patent text mining and semantic technologies Medical concept normalization and entity linking Argumentative zoning in scientific documents Table extraction and classification systems Analysis of her recent publications reveals strong trends in applying machine learning to specialized IR tasks, particularly in patent analysis and medical text processing. Her work emphasizes robust evaluation frameworks and cross-lingual capabilities, with consistent contributions to the CLEF campaign and PatentSemTech workshops. Dr. Piroi actively collaborates with international researchers through the Network Lab at TU Wien and contributes to the university's Center for Research Data Management, focusing on semantic technologies for research data infrastructure and scholarly communication systems.

Marijan SorićView profile

Tutor

• Geological Data Management
• Machine Learning
• Natural Disaster Response
• +4 more

Marijan Sorić is a Ph.D. candidate affiliated with Inria Valda (Inria Paris, DI ENS, CNRS), Inria CEDAR (Inria Saclay, LIX, CNRS), and École Normale Supérieure (ENS) at PSL University . His research focuses on heterogeneous geological data structuring , machine learning for environmental modeling , and social media analysis during natural disasters . Education: Centrale Lyon (2021), NTNU (2024) His work includes a Ph.D. thesis on Exploitation and Structuring of Heterogeneous Geological Data and Knowledge , supervised by Pierre Senellart, Ioana Manolescu, and Cécile Gracianne. He contributes to table extraction benchmarks for scientific documents and has presented seminars on Extracting Table Information from Scientific Reports . His teaching roles include tutorials in Differential Calculus and Programming for the CPES joint program at PSL University. He also serves as Substitute Secretary for entrance exams at ENS. Contact: marijan.soric@inria.fr

• Natural Language Processing
• Data Management
• Entity Resolution
• +4 more

Lucian Popa is a Principal Research Scientist at IBM Research - Almaden, leading a team developing human-in-the-loop systems to scale NLP technologies for diverse users and applications. His work integrates human intelligence, data, and machine learning to advance transparent, data-driven AI systems. His research spans Natural Language Processing, Data Management, and Explainable AI, with seminal contributions to schema mapping, data exchange, and entity resolution. He pioneers active learning frameworks that enhance human-AI collaboration for entity linking and annotation efficiency, focusing on making AI systems accessible in low-resource settings. Recent publications reveal a cohesive trajectory toward human-centered AI, emphasizing explainability in text understanding, entity resolution, and misinformation detection. His work consistently addresses real-world challenges through hybrid intelligence frameworks that balance automation with human expertise across financial, scientific, and social domains. Scientific recognition includes: Alonzo Church Award for Outstanding Contributions to Logic and Computation (2020) Test-of-Time Awards at ICDT, PODS, and EDBT conferences ACM Distinguished Member designation While no formal students or major grants are documented in public sources, Popa directs IBM research initiatives including the HIL framework for entity resolution and AutoAI for text. His team actively develops products like Global Table Extraction (GTE) that transform document processing through visual-context-aware AI. At IBM Almaden, he collaborates with researchers including Ron Fagin and Marina Danilevsky to bridge theoretical computer science with practical NLP applications, maintaining leadership in knowledge-intensive AI system design.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Sebastiano Vascon is an Associate Professor in the Department of Computer Science at Ca' Foscari University of Venice, with research spanning artificial intelligence, machine learning, and computer vision. His work emphasizes graph-based methods, game theory, and deep learning, applied to cross-disciplinary domains including climate change, environmental science, Polar Science, and cultural heritage restoration. His research integrates theoretical AI advances with real-world industrial applications, focusing on trajectory forecasting, time-series compression, climate risk assessment, and puzzle-based cultural heritage reconstruction. Recent publications reveal a strong trend toward solving complex environmental and archaeological challenges through neural networks, graph theory, and 3D vision, often bridging game-theoretic principles with practical AI deployment. Vascon holds significant academic service roles including Area Chair for CVPR 2025, BMVC 2025, ECCV 2024, and Program Chair for Fusion 2024. He serves as Associate Editor for Frontiers and has chaired major conferences including BMVC (2022-2024) and WACV 2021, demonstrating leadership in the global AI community.

Maik ThieleView profile

Professor

• Database Systems
• Data Engineering
• Big Data Analytics
• +4 more

Prof. Dr.-Ing. Maik Thiele is a Professor at the Faculty of Computer Science and Mathematics , Dresden University of Applied Sciences. He leads the Professorship for Database Systems , focusing on advanced database technologies, big data platforms, and data engineering. Academic Leadership: Dean of Master's program in Applied Computer Science (since 2021), elected member of Faculty Council (since 2024) Research: Data analysis along the data value chain from acquisition to AI-driven processing, with emphasis on Active Learning , Transformer Models , and Mathematical Information Retrieval Applied Projects: Director of Dresden Database Forum , member of Digital Transformation Development Workshop (EDiT) His recent work analyzes Active Learning strategies for biomedical image classification and synthetic data, develops time series matching techniques , and explores ethical AI evaluation for e-commerce chatbots. He supervises student theses on topics ranging from PDF data extraction to Smart Grid monitoring and Speech-to-Text optimization . Professor Thiele serves on program committees for leading conferences including ADBIS , DAWAK , and DOLAP , and contributes to journals like Transactions on Knowledge and Data Engineering and International Journal on Very Large Data Bases .

• Post-harvest technology
• Mathematical modeling
• Computational fluid dynamics (CFD)
• +3 more

Georgios Xanthopoulos is an Associate Professor at the Agricultural University of Athens, affiliated with the School of Environment and Agricultural Engineering and the Department of Natural Resources Management & Agricultural Engineering. His research focuses on post-harvest technologies, computational modeling of physical processes in agricultural products, and digital innovations in agriculture. Education: Agricultural Engineer (NTUA), MSc in Mathematical Modeling (NTUA), PhD in Computational Fluid Mechanics (Newcastle) Academic Leadership: Director of Department of Agricultural Constructions & Agricultural Engineering since 2022 Teaching: 11 undergraduate and 5 postgraduate courses, including Digital Technologies & Smart Infrastructure in Agriculture Research highlights include CFD modeling of drying processes, UV-C disinfection applications, and IoT systems for agricultural monitoring. His work addresses critical challenges in food preservation, fungal contamination prevention, and precision agriculture through mathematical and computational approaches. Scientific contributions feature over 45 peer-reviewed publications, 1 patent, and participation in 3 major research programs. Awards include the I.K.Y. scholarship for PhD studies and recognition for innovative drying process models.

• Coastal science
• Hydrogeology
• Groundwater-surface water interaction
• +4 more

Dr. Amir Jazayeri serves as a Research Associate in Hydrogelogy and Adjunct Research Leader at Flinders University's College of Science and Engineering, working within the National Centre for Groundwater Research and Training (NCGRT). His multidisciplinary research focuses on coastal hydrogeology, with particular expertise in groundwater-surface water interactions and coastal aquifer dynamics. Dr. Jazayeri's educational background includes a PhD in coastal hydrogeology from Griffith University (2012-2016), an MSc in coastal science from K.N. Toosi University of Technology in Tehran (2004-2007), and a BSc in Civil Engineering from Islamic Azad University in Isfahan (1999-2003). His academic journey reflects a focused progression toward specialized expertise in coastal groundwater systems. His research interests center on variably saturated, variable-density groundwater flow and solute transport, with applications to coastal unconfined aquifers. Dr. Jazayeri employs an integrated methodological approach combining field experiments, laboratory studies, and numerical modeling to investigate complex hydrological processes. His recent work shows increasing focus on practical applications for coastal management and water resource sustainability. Dr. Jazayeri's scientific contributions include 22 research outputs from 2009-2025, with notable recent publications addressing coastal aquifer dynamics, saltwater intrusion mechanisms, and innovative groundwater extraction techniques. His research demonstrates consistent productivity with multiple publications annually in recent years. Griffith University International Postgraduate Research Scholarship (2012) Griffith University Postgraduate Research Scholarship (2012) Visiting Research Fellow at Hohai University (2019) Visiting Research Fellow at Griffith University (2016) Visiting Research Fellow at University of Neuchâtel (2014) Dr. Jazayeri maintains active international research collaborations and has delivered industry-based research projects focused on integrated coastal zone management. His work with the National Centre for Groundwater Research and Training positions him at the forefront of groundwater research with practical applications for coastal communities. His research contributes significantly to UN Sustainable Development Goals related to clean water and sustainable coastal management. As part of the National Centre for Groundwater Research and Training, Dr. Jazayeri contributes to one of Australia's premier groundwater research facilities, working with multidisciplinary teams to address complex hydrogeological challenges facing coastal regions.

Krisztian BalogView profile

Professor

• Information Retrieval
• Recommender Systems
• Conversational Information Access
• +6 more

Krisztian Balog is a Professor of Computer Science at the University of Stavanger, Norway, and a Staff Research Scientist at Google DeepMind. With a research focus on Interactive AI systems evaluation Conversational information access User modeling and simulation Transparent recommendation systems His recent work emphasizes leveraging large language models for information retrieval tasks and developing simulation frameworks like SimIIR 3. Scientific contributions include receiving the Karen Spärck Jones Award (2018) and Best Resource Paper Award at CIKM’23 . He co-organized the Sim4IA workshop series and leads the NorwAI-funded PhD project on LLM-based recommendation systems.

Bernd LottermoserView profile

Professor

• Sustainable Resource Extraction
• Critical Raw Materials
• Environmental Geochemistry
• +7 more

Bernd Lottermoser is a Professor at RWTH Aachen University , leading the Chair of Sustainable Resource Extraction . His research spans critical raw materials , mine waste valorization , and digital technologies in mining education , with projects like the IMMERSE Erasmus+ initiative and SafeMine . Research Focus : Environmental geochemistry, computed tomography for ore analysis, ARD mitigation, and biopolymer applications. Education : Pioneered holographic tables, virtual excursions, and blended learning tools for mining engineers. Scientific Recognition : Alexander-von-Humboldt Fellow, Endeavour Executive Award, Michael Daley Award, and Top 1% Influential Scientist. Publications : Over 100 peer-reviewed works, including books on mining ethics and circular economy strategies. Students : Advised PhD candidates in graphite ore CT analysis, biopolymer dust suppression, and ARD characterisation.

Christian BauckhageView profile

Professor

• Machine Learning
• Pattern Recognition
• Quantum Computing
• +2 more

Christian Bauckhage is Professor of Computer Science specializing in Pattern Recognition at the University of Bonn and Lead Scientist for Machine Learning at Fraunhofer IAIS. He serves as co-director of The Lamarr Institute for Machine Learning and Artificial Intelligence. With over 20 years of experience in AI, his current research focuses on quantum computing applications in machine learning. His research spans quantum machine learning, explainable AI, financial document analysis, and medical imaging. Recent publications demonstrate strong focus on quantum computing applications, large language models in finance, and explainability techniques. Bauckhage has published over 200 papers on pattern recognition, machine learning, and quantum computing applications. His research group at Fraunhofer IAIS focuses on industrial applications of AI in finance, healthcare, and agriculture.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web
• +5 more

Benno Kruit is a Visiting Professor at the Department of Artificial Intelligence, Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on knowledge representation, semantic web technologies, and AI applications in data interpretation and system dynamics. He has contributed to projects involving knowledge graphs, ontology engineering, and question answering systems. Key areas include semantic interpretation of tables, fault diagnosis in cyber-physical systems, and scalable simulation models. His work spans interdisciplinary topics such as environmental data analysis, agricultural informatics, and multilingual entity disambiguation. Kruit has co-developed platforms like TAKCO and Tab2Know for knowledge extraction from tables. He teaches the course Knowledge and Data and has contributed datasets like Tab2Know evaluation data to the research community. Recent research highlights include exploring open-source RAG systems, ontology-based simulation frameworks, and constrained LLM approaches for official statistics querying. His publications reflect a strong emphasis on practical applications of AI in knowledge integration and domain-specific problem-solving.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +6 more

Yulong Chen is a Researcher at the Department of Computer Science and Technology, University of Cambridge. He specializes in Natural Language Processing (NLP), focusing on areas such as large language models (LLMs), constituency parsing, cross-lingual summarization, and dialogue systems. His work bridges theoretical advancements with practical applications, including automated claim verification, multimodal intelligence, and ethical AI considerations. Chen contributes to both teaching and research: he teaches courses on Natural Language Processing at both undergraduate and postgraduate levels. His research emphasizes interdisciplinary approaches, combining machine learning, computational linguistics, and cognitive models. Key projects include developing benchmarks for higher-order theory of mind reasoning in LLMs and designing frameworks to uncover model weaknesses. His publications span foundational surveys (e.g., on LLM hallucinations and text-to-SQL systems) to applied work like MACSum for controllable summarization and Hi-TOM for evaluating social cognition in AI. He collaborates extensively on tools like EASE for efficient data annotation and UniSumm for few-shot summarization, reflecting his commitment to advancing both NLP infrastructure and model capabilities.

• Natural Language Processing
• Machine Learning
• Logical Reasoning
• +7 more

Zhenyun Deng is a Researcher in the Department of Computer Science and Technology at the University of Cambridge, specializing in Natural Language Processing (NLP) and Machine Learning. His work focuses on advancing automated fact-checking systems, logical reasoning in large language models, and graph-based reasoning techniques. Key research themes include document-level claim extraction, logic-driven data augmentation, and multi-hop question answering. His recent contributions span automated verification of textual claims , abstract meaning representation-based data augmentation , and robust node classification on graph data . He collaborates on initiatives like the FEVER workshop and develops datasets such as TaKG for table-to-text generation enhanced with knowledge graphs. Notable trends in his publications include: Integration of logical reasoning into NLP systems Improving model interpretability for multi-step reasoning tasks Handling noisy data in graph neural networks His research bridges foundational machine learning theory with real-world applications in information retrieval and causal inference. No awards or grants are explicitly listed in the provided materials.