Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She previously served as a University Lecturer in Programming Languages at the University of Cambridge and was a key contributor to the Scala programming language at EPFL during her PhD. Her research at the Metareflection Lab focuses on neurosymbolic systems that integrate programming languages (PL) and artificial intelligence (AI) to enable correct-by-construction software in domains like program synthesis and precision medicine. Her work emphasizes three pillars: Safer systems via formal verification and type theory; Faster development through generative programming and multi-stage interpreters; Easier access by bridging neural (learnable) and symbolic (interpretable) representations. She has contributed to the POPL, ICFP, PLDI, and OOPSLA conferences, with recent work on Dafny proof assistants and Persimmon's polymorphism techniques. Scientific awards include: Fellow of Jesus College (Cambridge, 2017-2019); Michigan Cambridge Research Initiative Grant (2018); Teaching Assistant Team Award (EPFL, 2015); 6.170 Letter of Commendation (MIT, 2005); ArsDigita Prize Finalist (1999). She has advised committees at ICFP, PLDI, POPL, and SPLASH , and taught courses like Neurosymbolic Programming (2025) and Advanced Semantics of Programming Languages (2022). Her lab explores language design through projects like Persimmon , LURK , and DafnyBench .
Ştefania-Gabriela Dumbravă is an Associate Professor in Computer Science at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE), part of Institut Polytechnique de Paris. She leads the ACMES team at Samovar Laboratory (Télécom SudParis) and participates in international working groups including the Property Graph Schema Working Group and European Research Network on Formal Proofs. Education: PhD in Computer Science, Université Paris-Sud (2016) MSc in Computer Science, Jacobs University Bremen (2012) BSc in Mathematics, Jacobs University Bremen (2010) Research Focus: Her work centers on formal methods for designing and verifying graph database algorithms, with emphasis on: certified database engines, property graph schemas, threshold queries, progressive querying techniques, and knowledge graph evolution. She integrates theorem proving (Coq/Isabelle) with practical database applications. Publication Trends: Her recent works demonstrate strong focus on graph database foundations (schemas, query processing) and practical verification techniques. Publications frequently appear in top-tier venues (VLDB, SIGMOD, ICDE) and emphasize both theoretical rigor and real-world applications in areas like bioinformatics, transportation, and networking. Awards & Honors: EASST Best Software Science Paper (ICGT 2025) ICDE/SIGMOD Distinguished Reviewer Awards (2025) SIGMOD Best Paper & Research Highlight (2023) VLDB Best Paper Runner-Up (2022) Students & Grants: Supervises Master's interns on graph database applications. Leads the ANR JCJC VERDI project (2025-2029) on verified distributed graph systems. Actively recruits PhD candidates for this initiative. Labs & Service: ACMES team at Samovar Lab. Serves on editorial boards (TODS, TGDK) and program committees (VLDB, SIGMOD, ICDE). Coordinates VLDB 2026 Demonstrations Track and co-organizes multiple workshops (GRADES-NDA, TGD).
Olivier Gauwin serves as an Assistant Professor at the University of Bordeaux, holding dual roles in academic instruction and research. He teaches within the Computer Science Department at the University Institute of Technology (IUT), while conducting research as a core member of LaBRI's Numeric and Sustainability team. His institutional presence spans both the IUT campus in Gradignan (office 111) and LaBRI's research facilities in Talence (office 311), reflecting his integrated contributions to theoretical computer science and applied sustainability initiatives. His educational trajectory demonstrates deep theoretical foundations: Habilitation à diriger des recherches (HDR) from University of Bordeaux (2020) titled Transductions: resources and characterization PhD in Computer Science from Université Lille 1 (2009) titled Streaming Tree Automata and XPath , conducted at LIFL/INRIA Master's degree (DEA) from Centre de Recherche en Informatique de Lens (2004) titled Fusion itérée de croyances Gauwin's research program bridges abstract theory and practical applications, with early work establishing fundamental results in automata theory for XML stream processing. His investigations into visibly pushdown automata, nested words, and transducers created novel frameworks for efficient query answering in data streams. Recent years show strategic expansion into sustainability, where he adapts formal methods to environmental modeling challenges. This evolution maintains rigorous theoretical grounding while addressing contemporary computational sustainability needs through the Numeric and Sustainability team. Analysis of his 15 most recent publications reveals consistent methodological excellence across theoretical computer science. Core themes include automata minimization (notably proving NP-completeness for visibly pushdown automata), logical characterizations of transductions, and streamability analysis for nested structures. His work demonstrates exceptional coherence—advancing from foundational XML processing (2008-2013) to resource-optimized transducers (2015-2018) and current sustainability applications, always maintaining focus on computational efficiency and formal verifiability. Dr. Gauwin actively mentors the next generation of computer scientists: Supervised PhD completion of Nathan Lhote (2015-2018) on logical characterizations of transductions Guided PhD research of Félix Baschenis (2014-2017) on transducer minimization and resource optimization His research is institutionally supported through LaBRI (UMR 5800), a joint CNRS-University of Bordeaux laboratory, though specific external grants aren't detailed in available materials. Current work continues through the Numeric and Sustainability team, where he integrates automata theory with environmental computation challenges in collaborative projects spanning theoretical innovation and real-world sustainability applications.
Louis Jachiet is an Assistant Professor in Computer Science at Télécom Paris, affiliated with the Data, Intelligence and Graphs (DIG) team within the Information Processing and Communication Laboratory (LTCI) and the Computer Sciences and Networks (Infres) department. His research focuses on algorithms, databases, programming languages, and logic. His work spans query optimization distributed SPARQL evaluation graph algorithms formal language theory program synthesis data provenance with a strong emphasis on bridging theoretical and applied research. Analysis of his publications reveals expertise in database systems graph processing automata theory query enumeration SPARQL optimization probabilistic databases across both theoretical and practical applications.
René Quiniou is a Researcher at INRIA, affiliated with the DREAM team within the IRISA laboratory in Rennes, France. His work focuses on Online Monitoring , Change Detection , Machine Learning , and Data Mining , with applications in agriculture, environmental science, and healthcare. He leads research on extracting temporal patterns from sequences and developing adaptive learning algorithms for dynamic environments. Key research areas include incremental learning, anomaly detection in cybersecurity, and health monitoring systems. His publications span topics like multimedia data analysis, bovine disease detection, and intrusion detection in unlabeled data streams. He collaborates extensively with teams in computer science and applied mathematics, contributing to projects such as flood event clustering and cardiac surveillance systems. Articles emphasize practical applications of machine learning and data mining, such as symbolic time series representation (1d-SAX) and hierarchical skyline queries for database optimization. His work bridges theoretical advancements with real-world challenges in agriculture, healthcare, and cybersecurity.
Véronique Benzaken is a Full Professor at the University of Paris-Saclay , affiliated with the Computer Science Department and member of the VALS (Verification of Algorithms, Languages, and Systems) research group at LRI (CNRS) and the Toccata group at INRIA-Saclay. Her research focuses on Data-Centric Programming Languages and Deep Specification with Proof Assistants , particularly SQL/XML formalization and Coq-based verification systems. Her work includes the development of the ℂDuce XML-centric functional programming language and the Datacert project (2016-2021) for certifying data-intensive systems using Coq and Why(3). She collaborates with Oracle Labs on multi-lingual query interfaces (QIR) and formalizes languages like XQuery, Datalog, and SQL execution plans. She has received significant funding through the ANR grants for the Typex (2016) and Datacert (2016-2021) projects. Her publications in venues like CPP, ESOP, ITP, and SPLASH reflect her leadership in formal methods for data systems. Education : Habilitation (1996), PhD (1990), DEA in Theoretical Computer Science (1986), and degree in Singing/Opera (1983). Employment : Full Professor at Paris-Sud 11 since 1998; Assistant Professor at Paris 1 Panthéon-Sorbonne (1990-1998); INRIA researcher (1986-1990).
Raja Appuswamy is an Assistant Professor in the Department of Data Science at EURECOM, focusing on data management on modern hardware and molecular information storage. His research integrates cutting-edge storage technologies with bioinformatics, particularly in DNA-based data storage. He teaches courses in cloud computing, distributed systems, and databases. His work spans Optimizing storage hierarchies with DNA archival systems Hardware-conscious algorithms for GPUs and heterogeneous architectures Error correction in molecular storage Long-term database preservation strategies Key research interests include Cross-architecture data joins (e.g., OneJoin, XJoin) Cold storage innovation with CMOSS and OligoArchive Integration of bio-inspired storage with traditional computing and explores interdisciplinary challenges at the intersection of computer science and molecular biology. Publications emphasize scalable solutions for DNA storage density, error tolerance, and archival systems. Awards highlight contributions to DNA-based image encoding, storage reliability, and page cache checksums. Current projects focus on enzymatic DNA ligations for storage density improvement, motif-based error correction, and hardware acceleration for knowledge graphs. His work targets both theoretical advancements and practical implementations in storage systems.
Simon Pierre Dembele is a Lecturer (ATER) at ISAE-ENSMA, France, affiliated with the LIAS laboratory—a joint research unit between ISAE-ENSMA and the University of Poitiers with locations in Poitiers and Chasseneuil. He is a core member of the Data Engineering Team within LIAS, focusing on sustainable data management systems. Dr. Dembele holds a PhD and specializes in green computing for database systems. His research interests include: Database Systems Green Computing Energy Efficiency Big Data Machine Learning His work addresses carbon footprint reduction in data-intensive applications through innovative query optimization techniques, with emphasis on parallel processing and machine learning integration. Recent publications (2018-2020) demonstrate consistent advancements in energy-efficient database deployment and auditing methodologies. Scientific Awards: No awards documented in source material Advising and Grants: No information provided regarding student supervision, research grants, or funding sources in the available text. Dr. Dembele operates within the Data Engineering Team at LIAS, which collaborates closely with the Automatic Control and Real Time research teams on cross-disciplinary sustainable computing initiatives.
Simone Rossi is an Assistant Professor in the Data Science department at EURECOM. His research focuses on Bayesian methods, generative models, and deep learning, with particular emphasis on uncertainty quantification, diffusion processes, and scalable probabilistic models. He has contributed to foundational work on functional priors for Bayesian neural networks, conformal prediction for in-context learning, and optimization of diffusion models. Education: Not explicitly mentioned in the provided text. His research interests span machine learning, probabilistic modeling, and theoretical computer science. He explores topics like score-based generative models, continuous-time diffusion processes, and efficient Bayesian inference techniques. Recent work includes analyzing scaling laws for uncertainty in deep learning and improving LLM reasoning capabilities for Text2SQL tasks. His publications reflect contributions to top conferences like NeurIPS, ICML, and AABI, with a focus on both theoretical advancements and practical applications. Notable achievements include a Runner-up Best Paper Award at PAM 2024 for work on data augmentation in traffic classification. Scientific Awards: Runner-up Best Paper Award at PAM 2024 Dr. Rossi collaborates actively with industry and academia, contributing to open-source tools and foundational research in probabilistic deep learning. His work bridges theory and practice, addressing challenges in scalable Bayesian methods and generative model efficiency.
Walid Gaaloul is a Professor at Télécom SudParis, part of the Institut Polytechnique de Paris (IP Paris) and Institut Mines Télécom. He serves as Deputy Director of the SAMOVAR research laboratory and leads the ACMES research team. He is also a member of the DIEGO group within the Computer Science Department at Télécom SudParis. Previously, he was a researcher at the Digital Enterprise Research Institute (DERI) and an adjunct lecturer at the National University of Ireland, Galway (NUIG). He holds an M.S. (2002) and Ph.D. (2006) in Computer Science from the University of Lorraine, France, and a habilitation (2014) from Pierre et Marie Curie University, Paris. His research focuses on Business Process Management, Process Mining, Cloud Computing, and Service-Oriented Computing. He has authored over 200 publications in these domains and actively contributes to international conferences and journals as a reviewer and committee member. His work spans topics like cloud resource allocation, process discovery from emails, IoT service optimization, and blockchain-based process execution. His articles explore cutting-edge topics such as energy-efficient IoT service migration, trustworthy decentralized auctions, and formal verification of edge service monitoring. He collaborates on national and European projects addressing cyber-physical systems and distributed cloud-edge infrastructures.
Veronique Benzaken is a Full Professor (Professeur de classe exceptionnelle) at University of Paris Sud 11, where she is a member of the LRI (Laboratoire de Recherche en Informatique), UMR 8623 - CNRS. She is currently a member of the VALS (Verification of Algorithms Languages and Systems) research group, a joint team between LRI and the Toccata group at INRIA - Saclay. Her research focuses on data-centric programming languages and systems, with particular expertise in XML processing, type systems, and formal verification of database systems. Her academic background includes: Dec 1996: Habilitation à diriger des recherches, University Paris Sud 11 (UFR des Sciences - Orsay) Jan 1990: PhD in Computer Science, University Paris Sud 11 (UFR des Sciences - Orsay) Sep 1986: DEA d'Informatique fondamentale, University Denis Diderot Paris 7 (Master in Theoretical Computer Science) June 1983: Diplomée de Chant et d'Art-Lyrique, Conservatoire National de Région de Grenoble Professor Benzaken's primary research interests lie at the intersection of database systems, programming languages, and formal methods. She has made significant contributions to XML-centric programming through the design and development of ℂDuce, an XML-centric general purpose functional programming language developed under an MIT license. Her work emphasizes type-safe and fast query and transformation of XML documents. More recently, she has focused on the formalization of data intensive management systems using the Coq proof assistant, particularly in the context of the Datacert project (2016-2021) which aims to certify and verify data intensive systems such as RDBMS's and XML processing engines. Her research spans several interconnected areas including type systems for data languages, formal semantics of query languages, verification of database systems, and language-integrated query processing. She has led significant research projects such as the ANR project Blanc SIMI2 Typex (Typeful certified XML) and has collaborated with Oracle Labs on developing intermediate representations for multi-lingual querying interfaces. Her publication record shows a clear trajectory from XML processing and type systems toward increasingly rigorous formal verification of database technologies. Professor Benzaken has been actively involved in the academic community through service on program committees for major conferences including ESOP, ICDE, VLDB, and others. She has also been an invited speaker at workshops such as the Coq workshop (CoqWS@FLOC) in 2018. Her research is supported by significant grants including the ANR project Datacert (2016-2021) and the ANR project Blanc SIMI2 Typex. She has collaborated extensively with researchers such as Évelyne Contejean, Chantal Keller, and Stefania Dumbrava on formal verification projects, producing notable publications at ITP 2017 and ITP 2018 on Datalog and SQL formalization. Professor Benzaken is a member of the PCRI research group within LRI, focusing on programming, systems, and their applications. Her work bridges theoretical computer science with practical database system implementation, contributing to both the academic understanding and industrial application of data management technologies, particularly in the areas of XML processing, query languages, and formal verification of database systems.
C.-H. Luke Ong is Professor of Computer Science and Director of Graduate Studies at the Department of Computer Science, University of Oxford, and Tutorial Fellow at Merton College. He holds a BA in Mathematics (1984, Triple First) and a Postgraduate Diploma in Computer Science (1985, Distinction) from University of Cambridge, and PhD in Computer Science (1988) from Imperial College University of London. After positions at National University of Singapore (1991) and Trinity College Cambridge (1992-1993), he joined Oxford in 1994, becoming Reader in 2002 and Professor in 2004. His research spans multiple areas of theoretical computer science with recent focus on probabilistic programming, higher-order model checking, and semantics of computation. His work bridges theoretical foundations with practical applications in program verification and analysis. Ong has made significant contributions to game semantics, lambda calculus, and type theory, with recent work extending into algorithmic game theory and probabilistic computation. Ong's publication record shows a clear evolution from foundational work in semantics toward practical applications in verification and probabilistic programming. His recent articles demonstrate increasing focus on bridging theoretical computer science with practical problems in machine learning, probabilistic inference, and program analysis, particularly through higher-order model checking techniques applied to modern programming paradigms. General Chair of ACM/IEEE Symposium on Logic in Computer Science (LICS) Vice Chair of ACM Special Interest Group in Logic and Computation (SIGLOG) Member of European Association of Theoretical Computer Science (EATCS) Chairman of Singapore's Expert Panel on Mathematics and Informatics (2006-2014) Member of Singapore's Academic Research Council (since 2013) Ong has supervised 20 doctoral students to completion and currently co-supervises 9 doctoral candidates. His research has been supported by numerous grants from EPSRC and international collaborations. He has served as PC Chair for major conferences including LICS 2007, CSL 2005, FoSSaCS 2010, and TLCA 2011, demonstrating significant leadership in the theoretical computer science community. He leads research in the Centre for Metacomputation at Oxford, with active projects in higher-order model checking, algorithmic game semantics, and verification of concurrent systems. His work with the Games for Design and Verification research network has fostered international collaboration across Europe and Asia.
Pierre Senellart is a Professor in the Computer Science Department at École normale supérieure (ENS, Université PSL) and deputy director of the DI ENS laboratory, a joint CNRS/Inria/ENS unit. He leads the Valda team at Inria Paris and holds a chair in the PRAIRIE Paris School of AI. University: École normale supérieure (Université PSL) School: Department of Computer Science (DI ENS) Academic Rank: Professor Research Interests: His work bridges theoretical and practical aspects of web data management, including web crawling, archiving, information extraction, uncertainty management, and intensional data systems. He contributes to probabilistic XML data models, provenance tracking, and knowledge base construction. Recent Article Trends: His Google Scholar publications (2025–2023) focus on provability in probabilistic databases, theorem extraction from PDFs, and AI applications in uncertain data analysis. Topics span game theory, knowledge compilation, and multi-modal learning for scientific documents. Scientific Awards: Junior member of Institut Universitaire de France (2020–2025) Distinguished SIGMOD 2017 Program Committee Member ACM HyperText 2014 Douglas Engelbart Best Paper Award Adviser of Clément Genzmer (SIGMOD Programming Contest 2009 winner) Grants & Projects: He secures major funding (e.g., PRAIRIE Paris School of AI: €575k, DesCartes CNRS@CREATE: SGD50M), with roles in research projects like Dissemin (open science platforms) and ARCOMEM (social web archiving).
Nozha Boujemaa is a Research Director at Inria and Director of DATAIA Institute, leading projects in Data Sciences, Algorithmic Transparency, and Societal Impact. She co-founded the Digital Society Institute (ISN) and serves as a Senior Scientific Advisor for AI initiatives. Expert in Large Scale Multimedia Content Search, Pattern Recognition, and Machine Learning Developed methods for visual content enrichment, interactive retrieval, and satellite image analysis Scientific leader of Pl@ntNet (plant identification), CHORUS (multimedia search engines), VITALAS, MUSCLE, and TRENDS projects Her research spans Multimedia Retrieval , Big Data Applications , and Algorithmic Transparency , with over 150 publications. She has supervised 25+ PhD/Master's students and organized conferences like ACM Multimedia 2013 and European Big Data Value Forum 2017. Key scientific awards include: Knight of the National Order of Merit (France) She has contributed to international projects (NSF, European Commission), served on editorial boards for Multimedia Tools and Applications , and co-chaired workshops on visual digital libraries and AI ethics. Her work impacts web search, cybersecurity, biodiversity, and earth observation.
Alexandre Chanson is a Lecturer at the Polytechnic School of the University of Tours (EPU), affiliated with the Blois Computer Science Department within the Fundamental and Applied Computer Science Laboratory (LIFAT, UR 6300). His research focuses on data mining, machine learning, and human-centered AI systems. Academic Focus: Exploratory data analysis, optimization algorithms, and recommender systems Contact: Office 331, Jean Jaurès Campus, Blois | Phone: +33 254 552 168 | Email: alexandre.chanson@univ-tours.fr Research Trends: Alexandre's work bridges mathematical programming with interactive data exploration, emphasizing explainable AI and optimization frameworks like the Traveling Analyst Problem. Key contributions include local explanation clustering, comparison query generation, and personalized data narration. Scientific Awards: No specific honors identified in the current dataset.
