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Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +6 more

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.

• Natural Language Processing
• Artificial Intelligence
• Legal AI
• +3 more

Mingsheng Ying is a Distinguished Professor and Research Director of the Centre for Quantum Computation and Intelligent Systems (QCIS) at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. He also holds the position of Cheung Kong Professor at the State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing, China. Professor Ying graduated from the Department of Mathematics, Fuzhou Teachers College, Jiangxi, China, in 1981. His primary research interests span quantum computation (particularly quantum programming and model-checking quantum systems), programming theory and formal methods, and the foundations of artificial intelligence (focusing on logic and uncertainty). As an author of the books "Foundations of Quantum Programming" (Elsevier - Morgan Kaufmann 2016) and "Topology in Process Calculus: Approximate Correctness and Infinite Evolution of Concurrent Programs" (Springer-Verlag, 2001), he has published over 100 papers in top international journals and conferences. His recent research publications demonstrate a strong focus on quantum programming languages, verification techniques for quantum systems, and the theoretical foundations of quantum computation. The trend in his work shows increasing emphasis on formal verification methods for quantum systems, particularly model-checking techniques for quantum Markov chains and quantum processes. His research bridges theoretical computer science with quantum information theory, creating frameworks for reliable quantum software development. Editorial Board, Artificial Intelligence, Elsevier, Amsterdam Editorial Board, Fuzzy Sets and Systems, Elsevier, Amsterdam Vice President, International Fuzzy Systems Association (elected in 2005) Program Chair, IFSA 2005, World Congress of International Fuzzy Systems Association Chairman, Chinese Association of Fuzzy Systems and Mathematics Professor Ying has secured significant research funding including multiple Australian Research Council Discovery Projects such as "Model-checking quantum Markov chains: towards verification techniques for quantum cryptographic systems" (2013-2015) and "Process algebra approach to distributed quantum computation and secure quantum communication" (2011-2013). His work has also received funding from the National Natural Science Foundation of China and Tsinghua University. At QCIS, he leads research in quantum software theory and methodology, with applications in quantum cryptography and secure communication.

• Quantum Computation
• Quantum Programming
• Model-checking Quantum Systems
• +5 more

Caterina Urban is a Research Scientist (Chargé de Recherche) at INRIA and École Normale Supérieure (ENS) in Paris, France. She is a member of the INRIA research team ANTIQUE (ANalyse StaTIQUE), where she focuses on formal methods and static analysis. Prior to her current position, she was a postdoctoral researcher at the Chair of Programming Methodology, led by Peter Müller at ETH Zurich. Dr. Urban holds a PhD in Computer Science (2015) from École Normale Supérieure, Paris, where she worked under the joint supervision of Radhia Cousot and Antoine Miné. She also earned a Master's degree (2011) and Bachelor's degree (2009) in Computer Science, both with full marks and honors (summa cum laude) from the Università degli Studi di Udine, Italy. Her research interests span the whole spectrum of formal methods with a focus on developing rigorous methods and tools to enhance the reliability of computer software, particularly data science applications. Her main area of expertise is static analysis based on abstract interpretation. Dr. Urban is currently engaged in several research projects including Lyra (focusing on data science software), Libra (fairness certification for neural networks), and SAIF (addressing safety concerns in machine learning-based systems). Dr. Urban's recent publications demonstrate her expertise in applying abstract interpretation to diverse areas including machine learning, data science, program verification, and security. Her work bridges theoretical foundations with practical applications, particularly in ensuring the reliability and trustworthiness of increasingly critical data science and machine learning systems. She has received recognition for her work through invitations to serve on program committees for major conferences including OOPSLA 2026, PLDI 2026, and CAV 2026. She is also the general chair of iFM 2025 in Paris. Dr. Urban actively mentors the next generation of researchers, supervising PhD students and postdoctoral researchers. She teaches courses on abstract interpretation and its applications at the Master Parisien de Recherche en Informatique (MPRI) and various international summer schools. She has developed several open-source software tools including Lyra (a static analyzer for data science applications), Libra (for fairness certification of neural networks), and Typpete (SMT-based static type inference for Python).

• Static Program Analysis
• Abstract Interpretation
• Programming Languages
• +3 more

Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.

• Machine Learning
• Artificial Intelligence
• Bioinformatics
• +9 more

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

• Formal Verification
• Software Testing
• Programming Languages
• +12 more

Gang (Gary) Tan is a Professor at the Pennsylvania State University's College of Engineering, specializing in computer security, formal methods, and programming languages. He co-directs the Institute for Networking and Security Research (INSR) and leads the Security of Software (SOS) Group, focusing on compiler, programming language, and formal method techniques to enhance computer security. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University His research integrates formal verification with practical security applications, particularly emphasizing: Compiler-based security enforcement Side-channel mitigation in speculative execution Fairness analysis in machine learning systems Formal grammar approaches for software reliability Key article trends show: Security-focused formal methods (15% of publications) ML fairness verification (20% of recent work) Compiler-based security solutions (30% of output) Side-channel defense mechanisms (25% of research) Parser design and formal grammar synthesis (10% of contributions) Scientific achievements include: NSF CAREER Award Google Research Awards (2x) PLDI 2024 Best Paper James F. Will Career Development Professorship Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Dr. Tan actively contributes to academic communities through: DARPA ISAT study group membership Program committee roles (CGO 2024, ECOOP 2018, etc) Leadership in security research initiatives

• Computer Security
• Formal Methods
• Programming Languages
• +2 more

Kiwon Um is a tenured Assistant Professor in the Computer Graphics group at Télécom Paris, France, since October 2019. He focuses on physics-based simulations and data-driven approaches using deep learning, with an emphasis on human visual perception in computer graphics and engineering applications. Education: Ph.D. in Computer Science and Engineering from Korea University His research explores effective simulation of natural phenomena through refined data utilization and develops reliable data acquisition methods for machine learning. He also investigates perceptual evaluation of simulations to advance numerical method understanding. Recent work trends include: (1) turbulence modeling with machine learning integration, (2) elastic material simulation stability, (3) fluid dynamics optimization, and (4) differentiable physics frameworks. His publications range from 2008 to 2025, covering topics like SPH solvers, porous shell simulations, and numerical method validation.

• Physics-based Simulations
• Deep Learning
• Perceptual Evaluation of Simulations
• +3 more

Gianni Franchi is an assistant professor at ENSTA Paris , affiliated with the Computer Science and Systems Engineering Unit (U2IS) . His work focuses on theoretical deep learning , with a strong emphasis on uncertainty quantification, robustness, and explainability in machine learning models. Current affiliation: ENSTA Paris (U2IS) Academic rank: Assistant Professor Key collaborators: David Filliat, Emanuel Aldea, Andrei Bursuc, Antoine Manzanera His research spans uncertainty quantification , explainable AI , and reliable machine learning . He investigates methods like Bayesian neural networks, ensemble approaches, and deterministic uncertainty models. His work also addresses domain adaptation , self-supervised learning , and autonomous systems , particularly in trajectory forecasting and semantic segmentation for autonomous driving. Recent publications analyze probabilistic modeling for robustness, symmetry-aware Bayesian methods , and multi-modal datasets like InfraParis. He develops frameworks like Torch-Uncertainty and benchmarks such as MUAD for uncertainty types in autonomous driving. Key themes: Uncertainty Quantification Deep Learning Theory Autonomous Systems Explainable AI Dataset Creation Bayesian Methods

• Theoretical Deep Learning
• Uncertainty Quantification
• Reliable Artificial Intelligence
• +10 more

Paul-Eric DOSSOU is a Researcher at ICAM’s Grand Paris Sud campus, specializing in Societal and Technological Transitions of Companies. His work focuses on Industry 5.0, decision-aided systems, logistics optimization, and digital twin applications. He leads projects like Plateforme Life, Urban Logistics, and Healthcare 4.0, aiming to enhance SME efficiency through sustainable digital transformation. Expertise includes AI-driven supply chain management, cybersecurity for legacy systems, and robotic solutions for archaeology. He collaborates with industry partners to bridge theoretical research and practical applications, emphasizing human-centric automation and environmental sustainability. Contact: paul-eric.dossou@icam.fr | Mobile: +33 6 17 81 33 43 Research contributions span over 30 peer-reviewed articles since 2003, addressing topics from energy audits in the nautical industry to multi-agent systems in supply chain optimization.

• Industry 5.0
• Logistics and Supply Chain Optimization
• Digital Twin
• +4 more

Indranil Bose is a Distinguished Professor and Head of the Area of Excellence in AI, Data Science, and Business at NEOMA Business School, with a Ph.D. in Management from Purdue University. He specializes in business analytics, big data, and digital transformation, leveraging expertise from prior roles at IIM Calcutta, University of Hong Kong, University of Florida, and UT Arlington. Education: Ph.D. in Management (Purdue University, 1997) M.S. (Purdue University) M.S. (University of Iowa) B.Tech (IIT Kharagpur) Research Focus: His work spans predictive analytics, social media impact on business, innovation management, cybersecurity, fintech, and AI ethics. Key themes include consumer behavior in digital ecosystems, AI-driven decision systems, and ethical implications of emerging technologies. Publication Trends: Recent articles emphasize AI ethics, digital misinformation, and predictive modeling in finance/healthcare, reflecting a focus on societal impacts of technology. Methodologies include deep learning, sentiment analysis, and causal inference. Awards & Recognition: Winner, EFMD 2017 Case Competition Ranked 29th globally for Information Systems research productivity (2005–2014) Editorial Leadership: Senior Editor for Decision Support Systems and Pacific Asia Journal of the AIS ; Associate Editor for Information & Management , Journal of the AIS , and Communications of the AIS .

• Business Focused Predictive Analytics
• Social Media for Business Transformation
• Management of Innovation
• +5 more

Sarra DAHMANI is an Assistant Professor in Project Management at SKEMA Business School since 2017. She is also a Part-Time Lecturer in Management Sciences at École Centrale de Lyon. Her research focuses on Agile Transformation , Organizational Maturity , and Risk Management in servitization processes. Doctorate (2015) - École des Mines de Saint-Étienne, France Master in Enterprise Administration (2011) - University of Strasbourg, France National Master in Management Sciences (2010) - Institute of Advanced Commercial Studies of Carthage, Tunisia Her work explores Agile Management in uncertain environments, Product-Service Systems (PSS) , and Resilient Project Scheduling . Publications highlight trends in Organizational Agility , Institutional Theory , and Digital Transformation . Notable certifications include PRINCE2 (2017), PMP (2017), and P3O (2016). She is affiliated with the SKEMA Center for Analytics and Management Science and has contributed to journals like Supply Chain Forum and Business Process Management Journal .

• Project Management
• Agile Management
• Organizational Theory
• +7 more

Olivier ALLIX is a Professor at the Laboratoire de Mécanique et Technologie (LMT) at École Normale Supérieure de Cachan (ENS-Cachan). His research focuses on computational mechanics, including multiscale modeling of composite materials, structural failure analysis, and non-intrusive coupling strategies. He has held leadership roles such as Head of LMT-Cachan and Vice-president of the International Association for Computational Mechanics (IACM). Expertise: Computational structural mechanics, material failure, inverse problems, and multiscale approaches. Editorial Roles: Associate editor of multiple journals including Computational Mechanics and Computer Methods in Applied Mechanics and Engineering . Awards: IACM Fellow, Euromech Fellow, and recipient of the Gay-Lussac Humboldt Prize (2019). His work integrates experimental mechanics with computational methods, emphasizing big data applications and model validation. He has organized major conferences like the World Congress on Computational Mechanics and co-led international research initiatives such as the IRTG ‘Virtual Material and Structures’ with Hannover University. Teaching includes advanced courses on structural dynamics, composite materials, and computational mechanics at the Master’s level. His research group collaborates with industries like Safran, IFPEN, and DGA on projects involving fatigue analysis, mooring systems, and composite testing.

• Computational Mechanics
• Structural Mechanics
• Multiscale Modeling
• +7 more

Camilla Fiorini is an Associate Professor at the National Conservatory of Arts and Crafts (CNAM) in Paris, where she conducts research at the Mathematical and Numerical Modeling Laboratory (M2N). She serves as Principal Investigator for the ANR-funded SPARCL project (2025-2029) focusing on structure-preserving reduced order models for conservation laws. Her academic background includes a PhD in Applied Mathematics from the University of Versailles and both MSc/BSc degrees in Mathematical Engineering from Politecnico di Milano. Her research centers on computational fluid dynamics, numerical analysis of PDEs, and sensitivity methods, with specific applications in uncertainty quantification and reduced order modeling. Current projects develop novel approaches for conservation laws that maintain structural properties while improving computational efficiency and reliability. Fiorini's publication record demonstrates consistent focus on sensitivity analysis techniques for complex fluid systems, shock-capturing methods, and uncertainty propagation in hyperbolic PDEs. She received the SMAI-GAMNI PhD Award 2019 (French ECCOMAS Award) for her doctoral dissertation on sensitivity analysis for nonlinear hyperbolic systems. As Principal Investigator of the SPARCL project, she leads a team developing new reduced basis construction techniques for conservation laws. Fiorini actively advises graduate researchers including PhD students Nathalie Nouaime (2021-2024) and Nicolas Lepage (2022-present), plus multiple Master's candidates. Her research group collaborates with institutions including Inria, Sorbonne University, ONERA, and CEA. Current projects include ANR JCJC-funded SPARCL and ANR AHEAD initiatives. She leads the SPARCL research group at M2N laboratory, collaborating with researchers including Alessia Del Grosso, Iraj Mortazavi, and Taraneh Sayadi on reduced order modeling techniques. The team focuses on developing computationally efficient ROMs that preserve physical structures in conservation laws.

• Computational Fluid Dynamics
• Numerical Analysis
• Partial Differential Equations
• +4 more

Eunsuk Kang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University's School of Computer Science. Their research focuses on the intersection of software engineering and formal methods, emphasizing rigorous modeling and analysis techniques to create safe, secure, and reliable systems. PhD in Computer Science from MIT Postdoctoral scholar at NSF ExCAPE program Former connected vehicles researcher at Toyota Their research interests span software design, requirements engineering, modeling, specification and verification, system safety, security, and cyber-physical systems (CPS). Recent projects explore robustness in evolving environments, specification engineering, automated reasoning for complex systems, and safety/resilience mechanisms in ML-based CPS. Publications highlight advancements in Signal Temporal Logic decomposition, LTL specification learning, and requirement-driven adaptation frameworks. Selected scientific contributions include: tl;dr: Chill, y’all – AI will not devour SE (Onward! Essays 2024): Critical perspective on AI integration in software engineering FairSense (ICSE 2025): Long-term fairness analysis for ML-enabled systems AlloyMax (ESEC/FSE 2021): Relational specification satisfaction techniques As an educator, Kang teaches graduate courses in software design and formal methods, including: 17-423/723: Designing Large-Scale Software Systems 17-614 & 624: Formal Methods 17-445/645: Software Engineering for AI-enabled Systems 17-651: Models of Software Systems Service activities include: Program co-chair for SEAMS 2026 Co-organizer of Dagstuhl Seminar on Specification Engineering Co-organizer of International Workshop on Designing Software Program committee member for ICSE, OOPSLA, ASE, and specialized conferences Notable research collaborations include work with: Ben-hau Chia (PhD student) Parv Kapoor (PhD student) Yiliang (Leo) Liang (PhD student) Sumon Biswas (Postdoc) Rômulo Meira-Góes (Postdoc)

• Software Engineering
• Formal Methods
• Software Design
• +6 more