Find Academic Supervisors

• Business Intelligence
• Data Warehousing
• OLAP
• +7 more

Orlando Manuel Oliveira Belo is an Associate Professor with Habilitation at the School of Engineering, University of Minho, Portugal, where he has been a member of the Department of Informatics since 1986. He is also a Senior Researcher at the ALGORITMI R&D Centre, and a member of both the CST R&D Group and ISLab R&D Lab. His academic career spans over three decades, with significant contributions to the fields of Business Intelligence, Data Warehousing, and Data Mining. His educational background includes: 5-year degree in Systems and Informatics Engineering (1986) "Provas de Aptidão Pedagógica e Capacidade Científica" (MSc equivalent) in Expert Systems (1991) Ph.D. in Multi-Agent Systems (1998) Habilitation (2013) Professor Belo's research primarily focuses on Business Intelligence and related areas including Data Warehousing Systems, OLAP, Dashboarding, and Data Mining. His work has significant practical applications in fraud detection and control in telecommunication systems, data quality evaluation, and ETL systems for industrial data warehousing. In recent years, his research has expanded into Ontology Learning and Sentiment Analysis, with applications in healthcare analytics, particularly in cardiovascular health monitoring and dermatology. His interdisciplinary approach bridges computer science with practical business and healthcare applications, demonstrating the versatility of data analytics across different domains. His recent publications (2023-2025) show a clear trend toward integrating advanced machine learning techniques with domain-specific knowledge, particularly in healthcare applications. He has published extensively on sentiment analysis, ontologies, and data warehousing, with a growing emphasis on medical applications including atopic dermatitis and cardiovascular health. His work demonstrates a progression from foundational data warehousing and ETL research to more specialized applications in precision medicine and well-being analytics, reflecting the evolving landscape of data science applications. Professor Belo has an impressive publication record with 181 publications, including 26 in Q1/Q2 journals, and has accumulated 468 citations, resulting in an h-index of 11. His editorial contributions further demonstrate his standing in the academic community. As a dedicated researcher and educator, Professor Belo has been instrumental in developing computational platforms for specific applications and has contributed significantly to the advancement of Business Intelligence methodologies. His work with the ALGORITMI R&D Centre has fostered numerous collaborative projects and has positioned him as a key figure in data analytics research at the University of Minho. His laboratory affiliations include the CST R&D Group and ISLab R&D Lab, where he continues to lead research initiatives in Business Intelligence, Data Mining, and their applications across various domains including healthcare, telecommunications, and business analytics.

• Knowledge Graphs
• Large Language Models (LLMs)
• Semantic Web Technologies
• +6 more

Prof. Dr. Michael Martin is a Professor at the Professorship of Data Management , Faculty of Computer Science , Chemnitz University of Technology . His work focuses on Knowledge Graphs , Large Language Models (LLMs) , and Semantic Web Technologies , with specific emphasis on RDF/SPARQL optimization , geospatial data integration , and dataset versioning . Keywords: Knowledge Graphs, Semantic Web, LLMs, GeoSPARQL, Dataset Versioning Collaboration: Co-authors include Lars-Peter Meyer, Claus Stadler, Sara Todorovikj, and Claus Stadler Research Trends Recent publications demonstrate his leadership in LLM-KG-Bench benchmarking frameworks and CoyPu knowledge graph projects for resilience research. His work bridges Apache Spark with semantic technologies for scalable knowledge graph construction and develops domain-specific ontologies for industries like steel and copper manufacturing. Projects & Tools Martin's team created open-source platforms such as: Quit Store for distributed RDF dataset management CubeViz.js for statistical data visualization Structured Feedback protocol for decentralized data governance

• Building Information Modeling
• Automated Code Compliance
• Design Adaptation
• +3 more

Jiabin Wu is a researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM), specializing in Building Information Modeling (BIM) and AI-driven solutions for building design compliance. His work focuses on automating regulatory adherence in early design stages through computational innovation, with contact via j.wu@tum.de and +49 (89) 289-23147. His research centers on automated code compliance checking, design adaptation, and BIM conflict resolution using artificial intelligence techniques. Key methodologies include reinforcement learning for conflict resolution, graph-based semantic enrichment, and parametric modeling to enhance IFC (Industry Foundation Classes) models. This work addresses critical gaps in construction informatics by enabling self-healing building designs that dynamically adapt to regulatory requirements while preserving design intent. Analysis of Wu's 2022-2025 publications reveals a cohesive trajectory toward intelligent BIM ecosystems. Core themes include the 'Design Healing' framework for automated compliance, spatial logic integration in IFC models, and machine learning applications for conflict resolution. His research bridges civil engineering with computer science, emphasizing practical implementations that reduce manual review cycles and improve design efficiency in architectural workflows. Wu actively mentors graduate students, supervising theses on BIM-based issue resolution, circulation design optimization, and egress compliance. He contributes to TUM's educational mission through teaching 'BIM.fundamentals' in summer semesters 2022 and 2023, focusing on scalable assessment methods for large student cohorts. His academic service extends to developing pedagogical frameworks that integrate industry standards with computational thinking. As part of TUM's research infrastructure, Wu collaborates within the BIM-Lab and specialized groups including Information Management and Digital Twinning. His work leverages the university's Robotic Fabrication Lab resources and aligns with cross-departmental initiatives in spatial computing, advancing the integration of physical construction processes with digital model evolution.

Andreas VlachidisView profile

Associate Professor

• Natural Language Processing
• Semantic Technologies
• Information Extraction
• +7 more

Dr. Andreas Vlachidis is an Associate Professor in the Department of Information Studies at University College London (UCL), Faculty of Arts and Humanities. He serves as Co-Investigator and Technical Lead of the Sloane Lab, Principal Investigator of the 'Text Analytics for inclusive AI education platforms' project, and convenor of the Alan Turing Research Interest Group – Humanities and Data Science. Previously, he held academic positions at the University of the West of England and University of South Wales. His educational background includes a PhD in Semantic Indexing of Archaeological Grey Literature, MSc, and dual BSc degrees from the University of Glamorgan. He is a Fellow of the Higher Education Academy (FHEA) and a member of the British Computing Society (BCS). Dr. Vlachidis's research focuses on interdisciplinary work at the intersection of Information Science, Humanities, and Computer Science. His primary interests include Information Extraction, Semantic Data Modeling, Metadata, Natural Language Processing, and Text Mining, with applications in cultural heritage, digital humanities, and social sciences. His work emphasizes data integration, FAIR data principles, and semantic interoperability for cultural heritage collections. His recent publications demonstrate expertise in cultural heritage data modeling, semantic technologies for historical collections, NLP applications, and inclusive AI education. His work with the Sloane Lab project has been particularly influential in advancing knowledge base development for historical catalogues and museum collections. Outstanding Paper award from the Emerald Literati Network Fellow of the Higher Education Academy (FHEA) Member of the British Computing Society (BCS) Dr. Vlachidis supervises multiple doctoral students including Liu Lu (principal supervisor) and secondary supervises Binxia Xu, Charlotte Heng Men, and George Cooper. He has previously supervised to completion Helena Holis, Diego Ramírez Echavarría, and Olga Loboda. He serves as Departmental Tutor for pastoral care of students and chairs important committees including the Departmental Staff Student Consultative Committee. He leads significant research projects including the Sloane Lab (data unification, aggregation, and knowledge base development) and 'Text Analytics for inclusive AI education platforms' (promoting literacy development and writing skills). He also collaborates with the EU Horizon 2020 Ariadne Plus project on multilingual processing of archaeological literature.

• Knowledge Graphs
• Natural Language Processing
• Machine Learning
• +3 more

Sareh Aghaei serves as a Research Fellow at the Institute of Management Sciences within the Faculty of Mechanical Engineering and Industrial Management at Vienna University of Technology (TU Wien), focusing on knowledge-driven solutions for industrial maintenance and healthcare systems. Her academic credentials include: Ph.D. in Computer Science from the University of Innsbruck (2023) M.Sc. in Computer Science from the University of Isfahan Dr. Aghaei's research integrates knowledge graphs with natural language processing and machine learning to develop explainable AI systems. Her work spans industrial maintenance optimization, clinical decision support, and tourism information systems, emphasizing ontology engineering and question-answering frameworks that transform unstructured data into actionable knowledge. Analysis of her 2021-2025 publications reveals a strategic shift toward domain-specific knowledge graph applications, particularly in maintenance management (2022-2025) and health informatics (2023-2024). This evolution demonstrates increasing specialization in medical knowledge representation while maintaining foundational contributions to semantic web technologies established in earlier works like her 2011 Web services architecture research. Her scholarly recognition includes: netidee Grant Call 17: Austria's award for most innovative doctoral theses Dr. Aghaei's doctoral research was funded through the netidee scholarship. Current documentation indicates no active student supervision or major grant leadership beyond her postdoctoral position at TU Wien. Within TU Wien's Institute of Management Sciences, she contributes to research bridging production engineering and artificial intelligence, developing knowledge-based systems for predictive maintenance and industrial process optimization through interdisciplinary collaboration.

Guoqiang LiView profile

Associate Professor

• formal verification
• programming language theory
• zero-knowledge proofs
• +3 more

Dr. Guoqiang Li is an Associate Professor in the School of Computer Science at Shanghai Jiao Tong University, where he conducts research at the intersection of formal methods, programming languages, and security. His academic journey includes prior positions as Assistant Professor (2009-2013) and subsequent promotion to Associate Professor (2014-present) at the same institution. He has held international appointments including a postdoctoral fellowship at Nagoya University (2008-2009), an academic visit at the University of Oxford (2015-2016), and a Guest Associate Professorship at Kyushu University's Research and Development Center for Smart Mobility (2016-2020). Dr. Li earned his B.S. from Taiyuan University of Technology (2001), M.S. from Shanghai Jiao Tong University (2005), and Ph.D. from Japan Advanced Institute of Science and Technology (2008). His educational background reflects a strong foundation in both Chinese and Japanese academic traditions. His research focuses on formal verification, programming language theory, zero-knowledge proofs, knowledge reasoning, and intelligent system verification and security. Over the past decade, his work has evolved from traditional formal methods applied to timed automata and process calculi to contemporary topics including neural network verification, zero-knowledge proof systems, and the application of large language models to program analysis. His publication record demonstrates a clear progression toward addressing verification challenges in increasingly complex modern systems. Dr. Li's recent publications reveal a strategic research trajectory: early work centered on timed automata and formal verification of concurrent systems, while current research addresses the verification of AI systems, zero-knowledge cryptographic protocols, and LLM-enhanced program analysis. This evolution reflects his commitment to applying rigorous formal methods to emerging computational challenges. Distinguished Paper Award at ICSE 2020 for 'Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning' Consistent publication in top-tier venues including ASE, ICSE, FSE, and OOPSLA Recognition through multiple National Natural Science Foundation of China (NSFC) grants as Principal Investigator Dr. Li actively contributes to the academic community as a Senior Member of the China Computer Federation, Deputy Director of Theoretical Computer Science for the Shanghai Computer Society, and committee member for multiple technical organizations. He serves as Organization Chair for SEKM'20-22 and FMAC'17, Publicity Chair for TASE'21-23, and has participated in program committees for numerous international conferences. His teaching portfolio includes undergraduate and graduate courses in algorithm design, mathematical foundations, and scientific writing, demonstrating his commitment to both theoretical computer science education and practical application.

Ali BasiratView profile

Associate Professor

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +5 more

Ali Basirat is an Associate Professor in the Department of Nordic Studies and Linguistics at the Faculty of Humanities, University of Copenhagen, affiliated with the Center for Language Technology (CST). His research focuses on data-driven approaches to language technology, particularly explainable natural language processing and morphosyntactic analysis, with emphasis on universal patterns across languages and efficient language processing tools. His academic qualifications include: PhD in Computational Linguistics from Uppsala University (awarded 2018) Certificate in Higher Education Pedagogy from Linköping University (awarded 2022) Dr. Basirat's research centers on mathematical modeling of natural languages, with core interests in universal language models, representation learning, dependency syntax, and explainable NLP. He investigates data-driven syntax, word embeddings, grammatical gender patterns across languages, and adaptation of large language models for environmental studies and sustainable development goals. His work bridges theoretical linguistics with practical NLP applications. Analysis of his 2020-2025 publications reveals three dominant trends: (1) Advancements in multilingual dependency parsing through linguistic typology and syntactic nuclei exploration; (2) Investigation of grammatical gender representation in word embeddings and language models; (3) Application of NLP techniques to climate conceptual history and environmental studies. His research increasingly addresses efficiency challenges in large language model adaptation while maintaining cross-lingual performance. No major scientific awards are currently documented in his public profile. Dr. Basirat supervises master's theses in natural language processing and computational linguistics, focusing on machine learning, language modeling, syntactic parsing, and knowledge graphs. He teaches graduate courses including Scientific Programming and Representation Learning for Natural Language Processing (RL4NLP) at the University of Copenhagen's IT & Cognition and Computer Science programs. He is a core member of the Center for Language Technology (CST) and actively collaborates with the Nordic Language Processing Laboratory (NLPL). His research network extends through membership in the Association for Computational Linguistics (ACL), facilitating international collaborations across European institutions.

Mohammad Taher PilehvarView profile

Senior Lecturer

• Natural Language Processing
• Lexical Semantics
• Semantic Representation
• +3 more

Mohammad Taher Pilehvar is a Senior Lecturer at Cardiff University's School of Computer Science and an Affiliated Lecturer at the University of Cambridge's Department of Theoretical and Applied Linguistics. His research focuses on lexical semantics and semantic representation in NLP, particularly in word sense disambiguation and embedding space isotropy. He has held academic positions at Tehran Institute for Advanced Studies (TeIAS) and Sapienza University of Rome, where he completed his PhD under Dr. Roberto Navigli. Key Research Interests Natural Language Processing (NLP) Lexical Semantics & Semantic Representation Contextualized Embeddings & Word Sense Disambiguation Knowledge-Infused Language Models His recent publications (2021-2013) demonstrate expertise in semantic similarity frameworks, metonymy resolution, and biomedical NLP applications like PheneBank. He supervised PhD students including Jose Camacho-Collados and Costanza Conforti, now at top institutions. Awards 2023 AIJ Prominent Paper Award 2014 Sapienza Starting Grant 2015 Best PhD Paper (Sapienza University) He has collaborated with the University of Cambridge's Language Technology Lab on MRC-funded projects and co-organized SemEval tasks. Current roles include editorial board membership at JNLE and co-chairing ACL 2025.

• Information Management
• Knowledge Management
• Data Integration
• +5 more

Khalid Belhajjame is a Lecturer at Paris Dauphine University, where he is a member of the LAMSADE research laboratory. His academic career spans multiple prestigious institutions, having previously worked as a researcher at the University of Manchester and completed his PhD at the University of Grenoble. His research focuses on information and knowledge management systems with applications across various scientific domains. Belhajjame's research interests center on information and knowledge management, with specific contributions to pay-as-you data integration, e-Science, scientific workflow management, provenance tracking and exploitation, and semantic web services. His work bridges theoretical computer science with practical applications in astronomy, biodiversity, and life sciences. He has developed innovative approaches to workflow management, data integration, and provenance tracking that enhance scientific reproducibility and data transparency. His publication record shows a clear trajectory of advancing scientific workflow systems and data management techniques. Recent work focuses on workflow validation, knowledge graph maintenance, bioinformatics data analysis, and privacy-aware workflows. His research demonstrates increasing sophistication in handling complex data provenance while maintaining usability and transparency. The interdisciplinary nature of his work is evident in applications spanning genomics, business process analytics, and cloud computing. Belhajjame actively contributes to the scientific community through multiple roles. He serves on the editorial board of the MethodX Elsevier journal and has participated in numerous European, French, and UK-funded projects. His leadership extends to co-leading ProvBench, a provenance benchmarking initiative, and participating in influential working groups including the W3C Provenance working group and the NSF-funded DataONE working group on scientific workflows and provenance. He has established himself as a key contributor to research object frameworks and scientific workflow standards. His collaborations span multiple continents and disciplines, reflecting the broad applicability of his work in data management and workflow systems. His current research continues to push boundaries in making scientific workflows more transparent, reusable, and privacy-preserving.

• Information Systems
• Data Analytics
• Business Intelligence
• +7 more

Nicolas PRAT is an Associate Professor at ESSEC Business School specializing in Information Systems, Data Analytics and Operations. He serves as Head of the Information Systems Track at the Cergy campus and has been with ESSEC since 1995, progressing from Lecturer to his current position as Associate Professor since 2006. His academic credentials include: Accreditation to supervise research (Université Paris Dauphine-PSL, 2012) PhD in Information Systems (Université Paris Dauphine-PSL, 1999) Specialized Master in Information Systems (ESSEC Business School, 1991) MSc in Management (ESSEC Business School, 1990) International Teachers Programme (Stockholm School of Economics, 2004) PRAT's research focuses on conceptual modeling, design science research, business intelligence, and knowledge management with increasing attention to emerging technologies like AI and blockchain. His work bridges theoretical foundations with practical applications, examining how data and knowledge engineering can support organizational decision-making processes. He has made significant contributions to understanding the evolution of conceptual modeling and taxonomy development for complex technologies. His recent publications demonstrate a clear research trajectory toward exploring generative AI's impact on business intelligence, sustainability frameworks for design science research, and advanced methods for taxonomy development in complex emerging technologies. These works reflect his ongoing engagement with cutting-edge developments while maintaining strong theoretical foundations in information systems research. Scientific recognition includes: Qualification to the function of full Professor in the French University system (Computer Science, 2013) PRAT has supervised doctoral research, including Demigha S. at Université Paris 1 Panthéon-Sorbonne in 2005. His academic leadership includes serving as Academic Director for specialized programs and Head of the Information Systems Track. He actively contributes to the scholarly community through editorial board memberships for "Systèmes d'Information et management" (since 2022) and "Journal of Database Management" (since 2013).

• Medieval Religious History
• Digital Humanities
• Inquisition Studies
• +3 more

David Zbíral is Professor and Deputy Head of the Department of Religious Studies at Masaryk University's Faculty of Arts, where he leads research initiatives through the Center for Digital Research on Religion. His academic position involves both administrative leadership and advanced research in medieval religious history with computational methodologies. His research focuses on applying digital humanities approaches to medieval inquisition records, particularly through: Computational analysis of religious dissent and heresy prosecutions Network modeling of social and religious relationships Gender studies in medieval religious contexts Digital reconstruction of historical judicial processes Semantic text modeling of historical documents Zbíral's recent publications demonstrate a strong emphasis on computational methods in historical research, particularly using network analysis, natural language processing, and quantitative approaches to study medieval inquisition records across Europe. His work frequently examines social dynamics, gender patterns, and spatial relationships in religious dissent cases from the 11th to 15th centuries. He leads the DISSINET project (Networks of Dissent), which develops computational models of dissident and inquisitorial cultures in medieval Europe. This interdisciplinary initiative combines historical research with cutting-edge digital methodologies to analyze patterns of religious nonconformism.

Dario ColazzoView profile

Professor

• Databases
• Programming Languages
• Cloud Computing
• +7 more

Dario Colazzo is a Professor at Université Paris-Dauphine, affiliated with the LAMSADE laboratory. His research focuses on database systems and programming languages, particularly in cloud databases, type systems for semi-structured data, and query processing optimizations. He has contributed extensively to XML and XQuery research, including parallel query execution and static analysis techniques. His teaching includes courses on programming (Java), UML, database systems, and semantic web technologies at both undergraduate and graduate levels (in French). Key research interests span XML/XQuery optimization, JSON processing, distributed systems, and type systems for safe and efficient data handling. His work emphasizes scalable cloud-based solutions and efficient query execution models. Publications highlight advancements in parallel query processing (e.g., PAXQuery), type-based optimizations for XML updates, and semantic graph analytics. He has collaborated with institutions like EDBT, WWW, and VLDB conferences.

Zhiguang CaoView profile

Assistant Professor

• Artificial Intelligence
• Data Science
• Decision Making & Optimization
• +3 more

Dr. Zhiguang Cao is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). With a PhD from Nanyang Technological University (2017), his work bridges Artificial Intelligence , Deep Learning , and Combinatorial Optimization to tackle complex logistics and urban mobility challenges. Research Advisor to PhD/Master's students: XIAO Xiangjie, YI Hang, ZHANG Ni Recipient of the World’s Top 2% Scientist recognition (2024), IEEE Transactions on Industrial Informatics Outstanding Paper Award (2022), and Nobert Wiener Review Award (2022) Completed degrees: PhD (NTU), MSc (NTU), BEng (GDUT, China) Research Interests focus on Learning to Optimize (L2Opt) and Neural Combinatorial Optimization , with applications in: Vehicle Routing Problems (TSP, CVRP, PDP) Job Shop Scheduling and Multimodal Optimization UAV Routing , Drone Delivery , and Stochastic Programming His 15 most recent publications (2024-2025) span top venues like NeurIPS , ICML , IJCAI , and IEEE Transactions journals, covering topics from multi-task vehicle routing to graph domain adaptation and learning with large language models . Scientific Awards : World’s Top 2% Scientist (Stanford, 2024) IEEE Transactions on Industrial Informatics Outstanding Paper (2022) Nobert Wiener Review Award (2022) BMW Summer School Excellent Research Work (2014) IEEE Singapore Chapter Tennis Prize (2012) Dr. Cao advises the L2Opt Research Group , which recruits PhD students and remote interns. He has served as Area Chair for ICLR, SIGKDD, ICML, and Senior PC for AAAI and IJCAI. His ongoing projects include Trustworthy Human-AI Optimization (AISG, 2025-2027) and Mobile Crowdsourcing Emergency Supplies (SMU-UofT, 2025-2026).

Marios SavvidesView profile

Professor

• Biometric Identification
• Artificial Intelligence
• Machine Learning
• +5 more

Marios Savvides is the Bossa Nova Robotics Professor of Artificial Intelligence and a Full Tenured Professor in the Electrical and Computer Engineering Department at Carnegie Mellon University (CMU). He is also the Founder and Director of the CyLab Biometrics Center. His research focuses on AI algorithms for biometrics, facial recognition, iris scanning, and object detection under challenging conditions. He holds a BEng from the University of Manchester, an MS in Robotics from CMU, and a PhD in Electrical and Computer Engineering from CMU. Education: BEng, Microelectronics Systems Engineering, University of Manchester Institute of Science and Technology (1997) MS, Robotics, Carnegie Mellon University (2000) PhD, Electrical and Computer Engineering, Carnegie Mellon University (2004) Research Interests: Core AI and machine learning for robust biometric systems Long-range iris capture and matching Low-shot object detection and scene understanding Applications in retail automation, airport security, and medical imaging Key Achievements: Recipient of seven Best Paper Awards and the 2022 PIPLA Inventor of the Year Recipient of the 2015 Edison Gold Award, 2018 Immigrant Entrepreneur Award, and 2020 AI Excellence Award Developed AI algorithms deployed in over 3 million ADT security cameras and Bossa Nova robots Spun off startups including HawXeye and Oosto, serving as CTO/Chief AI Scientist Grants and Partnerships: Collaborations with Bossa Nova Robotics, Egen, UltronAI, and CMKL University Research on facial recognition for stock market prediction and medical imaging privacy risks Labs and Teams: Director of the CyLab Biometrics Center Member of IEEE Biometric Council and contributor to the IEEE Certified Biometrics Professional program

Diego CalvaneseView profile

Visiting Professor

• Artificial Intelligence for Data Management
• Knowledge Representation and Reasoning
• Virtual Knowledge Graphs
• +5 more

Diego Calvanese is a Visiting Professor at Umeå University's Department of Computing Science and holds a full professorship at the Free University of Bozen-Bolzano, Italy. His research focuses on AI for data management, including virtual knowledge graphs (VKG), ontology-based data access (OBDA), and formal methods like description logics. He is part-time at Umeå, balancing roles with his primary position in Italy. Calvanese has received prestigious awards including the AAAI Classic Paper Award (2021), EurAI Fellow (2015), and ACM Fellow (2019). He supervises three doctoral students at Umeå and has authored over 350 publications, with an h-index of 71. His work emphasizes data integration, geospatial systems, and ethical AI applications. Key Roles: Associate Programme Chair (IJCAI 2025), Programme Chair (IJCAI-ECAI 2026), Head of AI for Data Management Research Group Research Interests: Knowledge representation, graph data management, explainable AI, and telemonitoring systems like reCOVeryaID. His research group develops tools like Ontop, a VKG system enabling seamless data access across heterogeneous sources. Current projects include geospatial data integration and temporal OBDA frameworks. Calvanese has served on over 150 program committees and editorial boards, including Artificial Intelligence and JAIR. His work bridges technical advancements with societal impacts, addressing AI's role in healthcare, climate, and democracy.