Prof. Anya Belz is Full Professor of Computer Science at Dublin City University's School of Computing and Science Lead at ADAPT Research Centre. A leading NLP researcher with PhD-level expertise, she specializes in natural language generation, evaluation methodologies, and multimodal systems. Recipient of multiple best paper awards and NAACL Test of Time Award nomination. Research innovations include foundational work on statistical language generation (deployed in weather forecasting systems), comparative evaluation frameworks, vision-language integration, and reproducibility quantification. Current EPSRC-funded ReproHum project coordinates 20 global labs studying evaluation consistency. Achievements : Developed industry-deployed generation systems for accessibility applications Pioneered cross-modal alignment techniques for image description Authored 100+ publications spanning generation, evaluation, and reproducibility
Nils Holzenberger is an Assistant Professor at Télécom Paris, France, since February 2023, affiliated with the Data Intelligence Graphs (DIG) research team within the Information Processing and Communication Laboratory (LTcI). His work bridges artificial intelligence, natural language processing, and legal domains through neuro-symbolic approaches to statutory reasoning, particularly in tax law. Education: PhD in Computer Science, Johns Hopkins University (2017-2022) Master's in Engineering, Mines ParisTech (2013-2017) Preparatory Classes, Lycée Louis-le-Grand (2011-2013) Holzenberger's research centers on legal artificial intelligence with emphasis on statutory reasoning limitations in large language models. He pioneered the SARA dataset for tax law reasoning and LegalBench benchmark, developing hybrid symbolic-neural frameworks that expose LLMs' shortcomings in precise legal interpretation. His work integrates Prolog solvers with NLP techniques to create executable tax code mappings and contract analysis tools, establishing foundational methods for verifiable legal AI systems. Analysis of his 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) Tax law reasoning benchmarks exposing LLM hallucinations, (2) Neuro-symbolic integration for statutory interpretation, and (3) Low-resource template extraction for legal documents. His work consistently demonstrates that pure neural approaches fail at precise legal reasoning, necessitating symbolic grounding for reliable legal AI applications. The DIG research team at Télécom Paris, where Holzenberger leads legal AI initiatives, is actively hiring faculty for neuro-symbolic projects. While specific grant details aren't public, his collaborations with HEC Paris, Copilex startup, and featured podcast appearances indicate substantial industry-academia engagement in legal tech development. Holzenberger directs the legal AI vertical within LTcI's DIG team, focusing on data intelligence for statutory reasoning. His group develops tools for tax minimization strategy discovery, contract analysis, and legal information extraction, maintaining close ties with legal practitioners through projects like the Prolog-based tax code interpreter. The team's infrastructure supports both academic research and startup partnerships in computational law.
Andrew D. White is an Associate Professor of Chemical Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of Washington (2013). His research focuses on automating scientific discovery through AI, particularly leveraging large language models (LLMs) and deep learning techniques in chemistry. His lab develops agents that integrate literature analysis, hypothesis generation, and experimental design to advance fields like molecular dynamics and drug discovery. Education: PhD in Chemical Engineering, University of Washington, 2013 BS/MS (not explicitly stated in text, inferred from career timeline) Research Interests: Large language models for scientific automation Deep learning applications in chemistry and materials science Molecular dynamics simulations Scientific agents and autonomous systems Publications: His work includes groundbreaking studies on closed-loop AI systems for chemistry, federated learning in molecular property prediction, and multi-agent systems for drug discovery. Recent highlights include the Robin system and ChemCrow tools. Awards: Recipient of the NSF Career Award (2018), NIH Outstanding Investigator Award (2020), and the Curtis Teaching Award (2019). He also advises biotech companies and serves on the National Academy of Sciences' Chemical Sciences Roundtable. Grants & Funding: Supported by DOE, NSF (multiple grants including CBET-1751471), NIH (R35GM137966), and LLNL projects. Collaborates with institutions like Argonne National Lab and Qubit Pharmaceuticals. Labs & Teams: Leads the White Lab at Rochester and co-founded FutureHouse, a nonprofit advancing AI-driven scientific discovery. Supervises a multidisciplinary team of PhD students and postdocs in computational chemistry, AI, and biophysics.
Gordon Plotkin is a Professor at the School of Informatics, University of Edinburgh, where he is affiliated with the Laboratory for Foundations of Computer Science (LFCS). His research lies at the intersection of theoretical computer science and programming language semantics, with a profound influence on the formal understanding of computation. His research interests include Programming Language Theory, Semantics of Programming Languages, Domain Theory, Operational Semantics, Lambda Calculus, Type Theory, Concurrency Theory, and Algebraic Effects. His seminal work on structural operational semantics and domain theory has laid the foundation for modern semantics of programming languages. His publications span over five decades, showing a sustained and evolving research trajectory from foundational work in lambda calculus and domain theory to recent contributions in algebraic effects, probabilistic computation, and biochemical systems modeling. The articles demonstrate a consistent focus on formal methods, mathematical rigor, and the algebraic structure of computational effects. He has collaborated with leading researchers including Martín Abadi, John Power, Glynn Winskel, and John Reynolds. His work continues to influence both theoretical and practical developments in programming languages and systems. Gordon Plotkin has made foundational contributions to computer science, particularly through his development of structural operational semantics and domain-theoretic models of computation. He has advised numerous researchers and supervised many influential PhD theses, though specific student names are not listed in the provided text. His work has been supported by long-standing affiliations with the Laboratory for Foundations of Computer Science and the University of Edinburgh, and he has contributed to major collaborative projects in programming language design and verification. He is associated with several research groups and labs, most notably the Laboratory for Foundations of Computer Science (LFCS), which serves as a hub for theoretical research in programming languages, semantics, and logic at the University of Edinburgh.
Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA
Daniel Grier is an Assistant Professor jointly appointed in the Computer Science and Engineering and Mathematics departments at the University of California, San Diego (UCSD). His research focuses on quantum complexity theory , particularly exploring near-term quantum computing paradigms and proving quantum advantage over classical systems. He holds a Ph.D. from MIT and was previously a postdoctoral fellow at the University of Waterloo’s Institute for Quantum Computing. Education: Ph.D. in Computer Science, MIT B.S. in Computer Science and Mathematics, University of South Carolina Research Interests: Grier’s work bridges theoretical computer science and quantum computing, emphasizing algorithm design, complexity class separations, and foundational questions about quantum supremacy. He studies how low-depth quantum circuits, boson sampling, and other near-term technologies can achieve computational tasks classically deemed intractable. Recent Article Trends: His publications explore efficient quantum state learning (e.g., classical shadows), hardness results for quantum sampling problems (e.g., bipartite Gaussian boson sampling), and circuit lower bounds (e.g., depth-2 QAC circuits). These contributions highlight his focus on rigorously defining quantum computational advantages. Awards: None explicitly listed in the text. Advising & Grants: Advises at least one student, Jackson Morris. His research is supported by grants exploring quantum complexity and algorithm design. Teaches advanced courses on quantum complexity theory, computability, discrete mathematics, and quantum computing fundamentals. Labs/Teams: Maintains an active lab focused on quantum complexity theory, collaborating with colleagues on topics like interactive protocols and shallow quantum circuits.
Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.
Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.
Dr. Rasmus Ibsen-Jensen is a Lecturer in Computer Science at the University of Liverpool. Previously, he held a Postdoctoral position at IST Austria under Krishnendu Chatterjee and completed his PhD under Peter Bro Miltersen. Research Focus: Algorithmic game theory, strategy complexity in two-player zero-sum games, control flow graph algorithms, edit distance for automata, and theoretical biology applications. Teaching: Module Coordinator for second-year courses in database development (COMP207), C++ programming (COMP282), and industrial placement (COMP299). His work bridges computational game theory and formal verification, with recent publications exploring memory constraints in partial-information games, algebraic path properties in concurrent systems, and evolutionary spatial dynamics. While no scientific awards are explicitly mentioned in the provided text, his contributions to algorithmic complexity and interdisciplinary research (e.g., theoretical biology) highlight his academic impact.
Tatsunori Hashimoto is an Assistant Professor of Computer Science at Stanford University, specializing in artificial intelligence, machine learning, and natural language processing. His research focuses on developing robust and ethical language models, addressing challenges in bias mitigation, fairness, and transparency. He leads projects like the Stanford Alpaca, exploring instruction-following models and their societal impacts. Key research interests include generative models, AI ethics, and privacy-preserving techniques. His recent work examines language model behaviors, security risks, and the societal implications of AI systems. Notable contributions include frameworks for auditing language models, improving factual accuracy, and reducing disparities in speech recognition. His publications highlight advancements in long-context processing, few-shot learning, and automated benchmarking. He emphasizes practical applications of AI while addressing dual-use concerns and ensuring alignment with human values.
Mark Steedman is Professor of Cognitive Science at the University of Edinburgh's School of Informatics, with adjunct appointment at University of Pennsylvania. His research spans computational linguistics, AI, and cognitive science, focusing on Combinatory Categorial Grammar (CCG) and its applications. His research examines: Combinatory Categorial Grammar parsing and semantics Language model capabilities and limitations Cross-linguistic semantic inference Brain modeling of language processing Recent publications analyze hallucination sources in large language models, cross-linguistic entailment graphs, and brain-computer parallels in structure-building. He develops computational models integrating symbolic and distributional approaches to semantics. Honors include ACL Lifetime Achievement Award (2018) and George E. Davis Medal (2001). He serves on editorial boards of major linguistics journals and has authored influential books including 'The Syntactic Process' and 'Taking Scope'.
Miao Zhengjie serves as an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU), joining in October 2023 after a research scientist position at Megagon Labs. His work centers on enhancing data science pipelines through innovations in database systems and artificial intelligence. His academic foundation includes: Ph.D. in Computer Science from Duke University (2022) M.S. in Computer Science from Columbia University (2016) B.S. in Computer Science and Technology from Peking University (2015) Dr. Miao's research spans Database Systems , Data Management , Data Curation , and Data Provenance , with emphasis on AI-driven solutions for data pipeline efficiency. His methodology bridges theoretical database concepts with practical data science applications through novel algorithm development. Analysis of his 15 most recent publications reveals persistent focus areas: query explanation systems (35% of works), data augmentation frameworks (27%), and human-AI collaboration tools (20%). These contributions appear consistently in premier venues including SIGMOD, VLDB, and CHI, demonstrating methodological evolution from foundational query debugging (2019) to LLM-integrated annotation systems (2024). He actively participates in the SFU Data Science Research Group , contributing to interdisciplinary initiatives in large-scale data processing. Current information indicates no formal advisees or grant details are publicly documented in his institutional profile.
Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.
Jiao Licheng is a Distinguished Professor and Doctoral Supervisor at Xidian University, leading the School of Artificial Intelligence and the Department of Computer Science and Technology. He holds prominent roles such as Director of the Key Laboratory of Intelligent Perception and Image Understanding (Ministry of Education) and the International Joint Research Center for Intelligent Perception and Computing. His research focuses on Artificial Intelligence, Deep Learning, Evolutionary Computation, and Remote Sensing, with significant contributions to image understanding and brain-inspired computing. Education: B.E. (1982) from Shanghai Jiao Tong University, M.E. (1984) and Ph.D. (1990) from Xi'an Jiaotong University. Postdoctoral research at Xidian University (1990–1992). Research Interests include AI, Machine Learning, Image Processing, and Big Data Analysis. His work bridges theoretical advancements and practical applications, such as medical imaging, SAR image analysis, and autonomous systems. Recent articles emphasize innovations in remote sensing, deep learning architectures, and evolutionary algorithms. Awards include IEEE Fellow, IET Fellow, and the Wu Wenjun Artificial Intelligence Outstanding Contribution Award. Labs/Teams: Key Lab of Intelligent Perception, International Joint Research Center, and leadership in national innovation bases. Active in academic societies, including editorial roles in IEEE Transactions on Cybernetics and Geoscience and Remote Sensing.
