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Thomas Bigot is a researcher at the Bioinformatics and Biostatistics HUB of Institut Pasteur in Paris, France. He joined the institution in 2016 and is actively involved in the PAGE expertise group and maintains a privileged partnership with the Pathogen Discovery Laboratory. His work focuses on developing bioinformatics methods for pathogen detection from metagenomic data. Dr. Bigot holds a PhD in Bioinformatics (2013) from the University of Lyon, France, where his research focused on detecting topological patterns in phylogenetic trees. He also earned a Master's degree in Health and Populations with a specialization in Bioinformatics, Biostatistics, and Genome from Université Lyon 1 in 2009. His doctoral work led to the development of a formalism for describing tree patterns and a software suite for executing queries on thousands of phylogenetic trees. His research interests span bioinformatics, metagenomics, pathogen discovery, phylogenetics, and viral detection. As a developer proficient in Python and C++, he focuses on optimizing laboratory prototypes and creating user-friendly tools for non-specialized users. His work is particularly crucial in pandemic contexts and rapid diagnosis, enabling the referencing of pathogen sequences and identification of new pathogen-disease combinations. Analysis of his recent publications reveals a strong focus on viral detection through metagenomic approaches, particularly for emerging pathogens. His research spans multiple vector systems including ticks, mosquitoes, and bats, with applications in public health surveillance and understanding viral evolution. The 2023 publication 'Virus Pop' demonstrates his innovative approach to expanding viral databases through protein sequence simulation, addressing the 'dark matter' problem in metagenomics. Dr. Bigot actively contributes to the Bioinformatics program for PhD students at Institut Pasteur, having participated in courses from 2020 through 2024. His collaborative work spans multiple international research groups, reflecting the interdisciplinary nature of modern pathogen discovery research.

• Bioinformatics
• Metagenomics
• Pathogen Discovery
• +4 more

Sylvain Schmitz is a Professor of Computer Science at Université Paris Cité, affiliated with the IRIF institute. He leads the Automata and Applications team and contributes to modeling and verification research. Key Research Areas: Logic, Well Quasi Orders, Program Verification, Formal Languages, Database Theory Grants: ANR BraVAS (2017–2022), ANR PRODAQ (2015–2019), ANR ReacHard (2011–2014), ANR AVeriSS (2007–2009) His work focuses on the algorithmic complexity of well-quasi-orders, with applications to vector addition systems, Petri nets, and complexity hierarchies. He has delivered lectures on Algorithmic Aspects of WQO Theory (MPRI), Model-Checking Finite Structures (LMFI), and logic for the agrégation preparation. Recent publications analyze complexity bounds for vector addition systems and lossy counter machines, often leveraging ideal decompositions of well-quasi-orders. His 2024 ICALP paper with Lia Schütze addresses strongly monotone descending chains over ℕᵈ, while 2024 LICS work with Anand et al. explores unboundedness verification. Scientific Awards: IUF junior fellowship (2018–2023) He supervises PhD students Hector Buffière (2024–present), Aliaume Lopez (2019–2023), Anthony Lick (2016–2019), and Simon Halfon (2015–2018). He has served on steering committees for STACS (co-chair) and GT-Verif, and organized conferences like Highlights 2014.

• Logic
• Well Quasi Orders
• Verification
• +6 more

Işıl Dillig is an Associate Professor in the Department of Computer Science at the University of Texas at Austin, leading the UToPiA research group. She focuses on programming languages, with emphasis on static analysis, verification, and synthesis to enhance software reliability and security. Education: BS, MS, PhD in Computer Science from Stanford University Her research addresses critical challenges in: Program analysis for security and correctness Automated program synthesis for complex tasks Verification of concurrent and database-driven systems Recent publications span neurosymbolic synthesis, database integration, and smart contract optimization. Trends include hybrid AI-formal methods approaches and domain-specific language design. Scientific recognition includes: Sloan Fellowship NSF CAREER award She actively contributes to academic leadership as committee member, program chair, and keynote speaker in top conferences like PLDI, OOPSLA, and POPL.

• Programming Languages
• Static Analysis
• Program Verification
• +4 more

Pinjia He is an Assistant Professor and Presidential Young Fellow at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, where he leads research in software engineering, AI systems, and log analysis. He received his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong and his B.E. from South China University of Technology. His research focuses on three interconnected areas: AI for Software Engineering : Developing LLM-based tools for automated logging, code repair, and AIOps Software Engineering for AI : Ensuring safety and reliability of AI systems through testing and validation Software Systems : Log analysis, anomaly detection, and performance optimization in distributed systems Recent publications demonstrate strong emphasis on testing methodologies for AI systems (LLMs, vision-language models), automated log analysis techniques, and AI reliability. His work consistently appears in top-tier venues including ICSE, FSE, ASE, and ICLR. He has received significant recognition including: Most Influential Paper Award (ISSRE) IEEE Open Software Services Award IEEE CS TCSE Rising Star Award He leads a substantial research team with 9 PhD and 8 Master's students, focusing on AI/SE integration. The group maintains active industry collaborations and open-source projects with over 5,000 GitHub stars.

• Software Engineering
• Artificial Intelligence
• Natural Language Processing
• +5 more

Xinyu Wang is an assistant professor in the Computer Science and Engineering Department at the University of Michigan, Ann Arbor. Their research focuses on program synthesis, formal methods, programming languages, and AI-assisted programming. They previously worked as a researcher at Microsoft and earned their PhD from the University of Texas at Austin. Current affiliation: University of Michigan (College of Engineering) Education: PhD in Computer Science from University of Texas at Austin Research interests center on AI-assisted programming and program synthesis, combining formal methods, programming languages, AI/ML, and human-centered computing. Key areas include optimizing database queries, neuro-symbolic automation, and synthesis-aided software systems for diverse domains. Recent publications span SQL optimization (2025–2024), web automation (2023–2022), and synthesis algorithms (2021–2020). Work addresses scalability, domain-specific insights, and formal reasoning techniques for complex workloads. Scientific awards include: Distinguished Paper Award at OOPSLA 2024 Best Paper Nomination at VLDB 2024 Best Paper Honorable Mention at UIST 2022 Distinguished Paper Award at OOPSLA 2020 Advises PhD students and postdoctoral fellows in program synthesis and AI-assisted systems. Serves on program committees for PLDI, POPL, and OOPSLA. Develops synthesis-aided tools for database optimization, web automation, and computational science.

• Programming Languages
• Formal Methods
• Software Engineering
• +7 more

Alvin Cheung is an Associate Professor at the University of California at Berkeley, affiliated with the Department of Electrical Engineering and Computer Sciences (EECS). He leads the Data Systems and Foundations group, Programming Systems group, Sky Lab, and SLICE Lab, while also serving as a faculty affiliate at the Berkeley Institute for Data Science. His research focuses on integrating data management, programming languages, and software systems to develop tools for scalable data processing pipelines and improved data programming experiences. PhD students advised: Sahil Bhatia, Mick Kittivorawong, Jongseok Park Research keywords include Data Management , Programming Languages , Program Synthesis , Formal Verification , and Machine Learning . His recent work explores Verified Lifting techniques for database applications, stencil computations, and cloud systems, alongside novel programming paradigms for geospatial video analytics and speculative decoding. His publications from 2023-2025 demonstrate trends in LLM-driven code optimization , automated SQL equivalence , and neural code generation across domains like tensor operations and geospatial video systems. Notable scientific achievements include the Dahl-Nygaard Prize (2024) , VLDB Early Career Award (2023) , and CHI Best Paper Award (2021) .

• Data Management
• Programming Languages
• Software Systems
• +3 more

Denis Kuperberg is a CNRS researcher at LIP (Laboratoire de l'Informatique du Parallélisme), ENS Lyon, where he is part of the Plume research team. His work bridges theoretical computer science with interdisciplinary applications, particularly in systems biology. His primary research interests include automata theory, synthesis, verification, games, logics, decidability procedures, complexity, and proof theory. Kuperberg's work often explores the connections between formal methods and practical applications, as evidenced by his recent interdisciplinary publications on thermodynamic consistency of autocatalytic cycles. His research demonstrates a strong focus on both theoretical foundations and practical implementations, with several software tools developed for research purposes. Kuperberg's publication record shows a clear evolution from purely theoretical work in automata and logic toward more interdisciplinary research. His recent papers (2022-2025) demonstrate increasing engagement with biological applications while maintaining strong theoretical foundations. Key themes across his publications include history-determinism in automata, cyclic proof systems, positive fragments of logic, and applications to verification problems. Best Paper Award at ICALP 2025 (with Thomas Colcombet and Amina Doumane) Kuperberg actively supervises multiple students at various levels, including PhD candidates (Iris Magniez–Papillon, Émile Hazard, Laureline Pinault), postdoctorates (Thomas Kosc, Marc Bagnol), and numerous interns. His teaching portfolio includes advanced courses on Graphs, Machines and Logics at ENS Lyon Master 2 program, Theory of Regular Languages at EPITA Lyon, and specialized topics like Mathematical Aspects of Automata Theory. He leads the development of research software including EmergeNS (for simulating chemical reaction networks and tracking autocatalytic dynamics), Stamina and Acme (for solving decision problems from automata theory), and Electrum (a specification language analyzer). These tools demonstrate his commitment to bridging theoretical computer science with practical implementations.

• automata theory
• synthesis
• verification
• +6 more

Luigi Santocanale is a Full Professor in Computer Science at Aix-Marseille University, affiliated with the Laboratoire d'Informatique et Systèmes (LIS), UMR CNRS 7020, and member of the LSC research group. He is responsible for the 3rd year of the Licence Informatique, parcours Informatique Mathématique at Aix-Marseille University. His academic career includes previous affiliations with LIRICA@LIS, MOVE@LIF, LABRI, CPSC@UCalgary, BRICS, and UQAM. Professor Santocanale's research spans three interconnected domains: Order Theory, Category Theory and Universal Algebra (including Lattice theory, Topos Theory, and Enriched Category Theory); Combinatorics (focusing on Ordered sets, species theory, and game theory); and Logic in Computer Science (covering Intuitionistic logic, mu-calculus, decision problems for free algebraic objects, semantics of programming languages, and algebraic structures of distributed computing). His current research directions include lattice theory applications to databases, logics with fixed points, ordered sets in combinatorics, and circular proofs in mu-bicomplete categories. He has served as project coordinator for the ANR SOAPDC (Order Structures and Applications to Distributed, Parallel and Concurrent Computing) project and has been involved in numerous other research initiatives including LAMBDACOMB, RECIPROG, TICAMORE, MFL, TRECOLOCOCO, CHOCO, and TAGADA. Professor Santocanale is actively engaged with the academic community as a Program Committee member for major conferences including RAMiCS 2024 and TACL 2024. His significant contributions include: Decidability of the equational theory of relational lattices with applications to database query optimization Characterization of closure ordinals in modal mu-calculus Work on equational theories of permutation lattices (Permutohedra) and associahedra Development of circular proof systems for mu-bicomplete categories Professor Santocanale has organized and participated in the Marseille Lattice Working Group and has collaborated extensively with researchers including Fred Wehrung, M. J. Gouveia, and Nathalie Caspard. His work bridges theoretical computer science, mathematical logic, and algebraic structures, with implications for programming language semantics and distributed computing.

• Order Theory
• Category Theory
• Universal Algebra
• +12 more

Nicolas Garcelon leads the data science platform at the Imagine Institute , specializing in developing biomedical data infrastructure for translational research between hospitals and research institutions. The platform manages 82 phenotypic databases covering 480,000 patients and has developed multiple software tools including eCohorte for database creation, BioPancarte for lab result visualization, and Dr. Warehouse for comprehensive clinical data integration. Director of a platform managing 36 million biological results Developed software registered with Program Protection Agency Open-source contributor with GNU GPL licensed Dr. Warehouse His research focuses on automated phenotypic data processing , clinical text mining , and patient similarity metrics for rare disease diagnosis. The platform enables Google-like search across 4 million clinical documents and provides diagnostic support through patient similarity calculations. Key publication themes include: Biomedical data warehouse architecture Phenotypic similarity algorithms Clinical decision support systems Unstructured clinical data processing Rare disease patient recruitment As part of the Necker-Enfants Malades Hospital collaboration, his team manages a data warehouse containing: 4 million clinical documents 36 million biological results 35 million extracted phenotypes Over 1000 completed studies

• Biomedical data science
• Translational research
• Clinical informatics
• +3 more

Brice Chardin is an Associate Professor in Data Engineering at ISAE-ENSMA since 2013, affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) Data and Model Engineering team. His work bridges academic research and industrial applications, focusing on data management solutions for critical systems. His research spans clustering algorithms under dissimilarity constraints , RDF query relaxation for explaining empty/overabundant results, pattern mining through the RQL language, and energy data management . Key projects include Chronos (a NoSQL system for industrial sensor data) and collaborations with energy companies SRD and Nexeya for predictive consumption analysis. Recent publications (2021-2024) emphasize constrained clustering techniques and cooperative query processing for RDF knowledge bases, revealing a strong trend toward practical solutions for industrial data challenges. His work integrates machine learning with database theory to address real-world data imperfections. PhD in Computer Science from INSA Lyon (2011) Postdoctoral position at LIRIS (2012-2013) on ANR DAG project Specialized in industrial data management since 2011 EDF collaboration Chardin actively supervises academic projects including drone simulation with Ardupilot and Smart Data mining initiatives. His industrial partnerships focus on energy sector applications, particularly predictive analysis for electricity distribution and storage systems. Current work involves developing clustering algorithms with error bounds and query relaxation frameworks for semantic web technologies.

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +5 more

Sybille Caffiau serves as a Lecturer in Data Engineering at ISAE-ENSMA's School of Engineering, affiliated with the LIAS laboratory across both ENSIP (Poitiers) and ISAE-ENSMA (Chasseneuil) campuses. Her research focuses on Human-Computer Interaction with specialization in task modeling and user interface validation. Her research interests span Human-Computer Interaction , Task Modeling , and User-Centered Design . She develops and evaluates tools like K-MADe for modeling user activities, with emphasis on empirical validation of task modeling approaches. Her work bridges cognitive science principles with practical software engineering applications, particularly in educational technology and interactive system design. Analysis of her publication trends reveals consistent contributions to HCI conferences and journals since 2006, with increasing focus on tool development (notably K-MADe) and empirical validation methodologies. Her recent work demonstrates strong interdisciplinary connections between cognitive modeling, software engineering, and educational technology. No scientific awards or fellowships are documented in the available materials. While her collaborative network includes prominent researchers like Laurent Guittet and Patrick Girard, specific details about student advising or research grants are not provided in the source materials. Her thesis work (2009) established foundations for model-driven interactive application design. She operates within the LIAS laboratory's Data Engineering team, contributing to research on interactive systems and task modeling frameworks. The KMADe environment represents her primary software contribution for activity description and task modeling.

• Human-Computer Interaction
• Task Modeling
• User Interface Design
• +3 more

Ibrahim Dellal is a Lecturer in Data Engineering at ISAE-ENSMA, France, affiliated with the LIAS laboratory. His research focuses on semantic web technologies, particularly query processing over uncertain RDF knowledge bases. He addresses critical problems like empty answers and overabundant query results through cooperative approaches that explain and refine unsuccessful queries, contributing significantly to knowledge representation and database systems. Education: PhD in Computer Science from ISAE-ENSMA (2019) on managing large knowledge bases with incomplete and uncertain data Research Interests: Data Engineering and Semantic Web Technologies Uncertainty Handling in Knowledge Bases Cooperative Query Processing Query Result Explanation and Refinement RDF Systems and Knowledge Representation His 2017-2020 publications demonstrate consistent focus on cooperative query answering for uncertain RDF knowledge bases, specifically tackling the empty answer problem through explanation generation and the overabundant answers problem via query refinement. These works advance semantic web and database research by bridging user interaction with automated query optimization under data uncertainty. He is an active member of the Data Engineering team at LIAS laboratory, which operates across ISAE-ENSMA's Chasseneuil campus and collaborates with teams in Automatic Control and Real Time systems for interdisciplinary engineering research.

• Data Engineering
• Semantic Web
• Knowledge Representation
• +3 more