جستجوی استاد راهنمای دانشگاهی

• Software Engineering
• Programming Languages
• Systems
• +۴ مورد دیگر

Manuel Rigger is an Assistant Professor at the National University of Singapore (NUS), leading the TEST Lab (Trustworthy Engineering of Software Technologies) within the PL/SE group at the School of Computing. His research focuses on improving the reliability of data-centric systems through automated testing frameworks and formal methods. Education : PhD from Johannes Kepler University Linz (supervised by Hanspeter Mössenböck), postdoctoral work at ETH Zurich (Advanced Software Technologies Lab under Zhendong Su). Research Interests : Automated testing of database systems Programming language design and verification Incremental build systems Formal methods for software reliability Key Contributions : Developed tools like SQLancer (for finding bugs in databases) and CERT (performance issue detection). His work has uncovered over 800 bugs in real-world systems. Awards : Recipient of the ERC Consolidator Grant (2025) for groundbreaking research in software security and testing. Service Roles : Organizer of ICFP/SPLASH 2025 Outdoor Activities, committee member for OOPSLA Review, PLDI Artifact Evaluation, and ICSE Program Committee. Also actively involved in organizing workshops (e.g., Fuzzing & Software Security Summer School 2025).

Pierre Jourlinمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Programming Languages
• +۳ مورد دیگر

Pierre Jourlin is a Professor at Avignon University, specializing in Computer Science and Artificial Intelligence. He actively contributes to academic events such as workshops on programming education and AI ethics. His research focuses on areas like natural language processing, machine learning, and the societal impact of AI. Jourlin has organized events like a programming workshop for high school students (2022) and authored numerous articles on topics ranging from rule-based event extraction to generative AI in creative writing. His academic contributions include work on semantic disambiguation, medical literature tools (SIMI), and foundational studies in speech recognition and multimedia retrieval. He critiques AI limitations, emphasizing the importance of human oversight in code generation and advocating for accessible education to counteract underfunding in public schools. Jourlin's recent publications (2024–2025) address AI's role in creative fields and the evolution of programming languages. His work often bridges technical innovation with ethical considerations, as seen in his analysis of code-producing AI tools like Copilot. He remains an active member of the academic community, blending research with pedagogical outreach.

Allel Hadjaliمشاهده پروفایل

استاد

• Data Science
• Computational Intelligence
• Soft Computing
• +۱۲ مورد دیگر

Allel Hadjali is a Full Professor in Computer Science specializing in Data Engineering at ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique) in Poitiers, France. He is affiliated with the Laboratory LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) at ISAE-ENSMA. His academic career includes progression from Associate Professor to his current Full Professor position, with extensive teaching experience across multiple computer science domains. Professor Hadjali's research falls within the data science domain, with particular focus on Exploitation, Extraction, and Recommendation (E2R). His work applies Computational Intelligence and Soft Computing techniques to massive data exploitation and analysis, including flexible querying approaches (Skyline, Gradual, and Bipolar queries), modeling and querying uncertain/incomplete data, cooperative answering techniques, and data reduction through linguistic summaries. He also conducts research in recommendation systems (learning-based and group recommendation) and extraction techniques (mining gradual patterns), along with related interests in data quality, intelligent systems, and crowdsourced data management. His publication record demonstrates consistent contributions to top-tier journals and conferences, with recent work focusing on skyline query processing, uncertain data management, RDF knowledge bases, and explainable AI. His research shows a clear trajectory from foundational work in fuzzy logic and uncertain databases toward more applied research in semantic web technologies and machine learning explainability. Professor Hadjali serves on the editorial boards of several prestigious journals including the Journal of Smart Environments and Green Computing, Sensors Journal, and the Universal Journal of Aeronautics and Aerospace Research. He has also organized special issues on topics such as uncertainty in cloud computing and managing uncertain data. At ISAE-ENSMA, Professor Hadjali teaches courses including Formal aspects of software engineering, Language interpretations and compilation, Programming languages, and Data management and exploitation. Previously as an Associate Professor, he taught courses on object modeling, distributed algorithms, operating systems, and advanced databases focusing on preferences and uncertainty. He leads the Data Engineering team within the Laboratory LIAS, which focuses on developing computational intelligence approaches for modern data challenges. His current projects include work on data quality (QDoSSI project funded by CNRS Mastodons 2016-2018) and research actions in GDR MADICS 2018 related to scientific data quality.

Gábor Bergmannمشاهده پروفایل

پژوهشگر

• Model-Driven Engineering
• Incremental Computing
• Query Evaluation
• +۵ مورد دیگر

Gábor Bergmann is a researcher at Budapest University of Technology and Economics, Hungary, and a member of the MTA-BME Lendület Research Group on Cyber-Physical Systems. He actively contributes to the fields of model-driven engineering and incremental computing, with a focus on efficient query evaluation and program analysis. His research interests include: Model-Driven Engineering Incremental Computing Query Evaluation Program Analysis using Datalog Static and Whole-Program Analysis Cyber-Physical Systems The recent publications of Gábor Bergmann demonstrate a strong trend in incremental computation techniques applied to program and model analysis, particularly leveraging Datalog and lattice-based frameworks. His work focuses on optimizing performance and scalability of analyses through change-driven evaluation, with applications in model synchronization, inter-procedural analysis, and EMF-based systems. He has served on program committees, particularly in the Foundations Track of the MODELS conference series, indicating active engagement in the academic community. While no formal advising or grant information is available from the provided text, his involvement in a Lendület research group suggests participation in funded research projects. He has not received any explicitly mentioned scientific awards in the provided data.

Nada Aminمشاهده پروفایل

استادیار

• Programming Languages
• Artificial Intelligence
• Generative Programming
• +۱۲ مورد دیگر

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 .

Véronique Benzakenمشاهده پروفایل

استاد

• Data-Centric Programming Languages
• Formal Methods
• Type Systems
• +۳ مورد دیگر

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).

• Cloud Computing
• Distributed Systems
• High-Performance Computing
• +۵ مورد دیگر

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.

• Web Data Management
• Probabilistic Databases
• Provenance in Databases
• +۴ مورد دیگر

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).

• Knowledge Representation and Reasoning
• Description Logic
• Ontology-Mediated Query Answering
• +۳ مورد دیگر

Meghyn Bienvenu is a full-time CNRS Senior Researcher (Directrice de Recherche) at the LaBRI research lab in the University of Bordeaux , France. Since 2023, she co-directs the GDR RADIA (National Research Network on Reasoning, Learning, and Decision in AI). Her research focuses on knowledge representation and reasoning , particularly description logic ontologies and their application to querying data . She spent the 2023-2024 academic year at the Japanese-French Laboratory for Informatics (JFLI) in Tokyo, Japan. Current affiliation: CNRS Senior Researcher at LaBRI, University of Bordeaux Co-director of GDR RADIA since January 2023 Research location: JFLI (2023-2024 academic year) Research Interests Meghyn's work spans knowledge representation, description logics, and ontology-mediated query answering. She explores: Querying inconsistent knowledge bases Paraconsistent reasoning frameworks Shapley value computation in AI Entity resolution and data repair Lightweight description logics (EL, DL-Lite) Connections between data management and AI Recent Publications Her recent publications (2022-2024) focus on: Paraconsistent description logics Cost-based semantics for inconsistent data Shapley value applications in query answering Collective entity resolution frameworks (ASPEn, REPLACE) Counting queries over ontology languages Honors and Awards Best Paper Award at RR 2016 Teaching Activities She has taught courses on knowledge representation, description logics, and ontology-based data access at institutions including: University of Bordeaux (since 2018) Université Montpellier (2015-2016) Universität Bremen (2009-2010) European Summer Schools (Reasoning Web, ESSLLI) Labs and Collaborations Active in the LaBRI research laboratory and the INTENDED AI Chair at University of Bordeaux, she collaborates with institutions in France, Germany, and Japan. Her work intersects AI, databases, and formal reasoning.

Louis Mandelمشاهده پروفایل

پژوهشگر

• Reactive Programming
• Probabilistic Languages
• Query Compilers

Louis Mandel is a Researcher at Inria, specializing in reactive programming languages and probabilistic systems. He co-developed ReactiveML and Q*cert (a verified query compiler), with applications in chatbots and cloud log analysis. His work bridges formal methods and practical tools for embedded systems.

Steve Zdancewicمشاهده پروفایل

استاد

• Programming Languages
• Type Theory
• Logic
• +۱۱ مورد دیگر

Steve Zdancewic is the Schlein Family President's Distinguished Professor and Associate Chair in the Department of Computer and Information Science at the University of Pennsylvania, School of Engineering and Applied Science. He is a leading researcher in programming languages, type theory, logic, and computer security, with extensive contributions to formal verification, program synthesis, and language-based security. Affiliation: University of Pennsylvania, Department of Computer and Information Science Role: Schlein Family President's Distinguished Professor, Associate Chair Research Group: PL Club (with Benjamin Pierce and Stephanie Weirich) His research focuses on foundational and applied aspects of programming languages, including formal verification of compilers and systems, type-driven program synthesis, information-flow security, quantum programming, and mechanized metatheory. He develops tools and frameworks such as Vellvm (verified LLVM semantics in Coq), Interaction Trees (for modeling impure computations), and QWIRE (for quantum program verification). The recent publications highlight a strong trend toward formal verification, semantics engineering, and program synthesis, particularly using interactive theorem provers like Coq. His work spans functional programming, type systems, concurrency, security, and compiler correctness, with a consistent emphasis on mechanized proofs and practical verification tools. Distinguished Paper Award, ECOOP 2023 Schlein Family President's Distinguished Professor (2021) Distinguished Paper Award, POPL 2020 Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching (2018) IEEE MICRO Top Picks (2013) Alfred P. Sloan Fellow (2009–2010) NSF CAREER Award (2004) Best Paper at SOSP (2001) Intel Foundation Graduate Fellowship (2001) Best Paper at ICFP (1999) NSF Graduate Fellowship (1996) Zdancewic actively mentors graduate students and postdocs, many of whom have become faculty or researchers in industry. He has led significant research initiatives including the NSF Expedition on Deep Specification and the Vellvm project. He is currently General Chair for POPL 2025 and has served on numerous program committees for top-tier programming language conferences. He leads the Security-Oriented Languages (SOL) group and is deeply involved in the PL Club at Penn, fostering collaborative research in programming languages. His current projects include verified compiler infrastructure, quantum programming semantics, and expressive security policies.

Aseem Rastogiمشاهده پروفایل

پژوهشگر

• Programming languages
• Type systems
• Program verification
• +۴ مورد دیگر

Aseem Rastogi is a Senior Principal Researcher at Microsoft Research India specializing in programming languages, type systems, program verification, and software security. He is a core designer and developer of F*, a language for program verification, and has made significant contributions to Project Everest, which builds verified secure communications components including cryptographic libraries and parsers. Dr. Rastogi earned his PhD from the University of Maryland, College Park under Michael Hicks and completed his M.S. at Stony Brook University with Rob Johnson. His research spans formal methods, programming language theory, and practical security applications, with recent work focusing on integrating Large Language Models with formal verification techniques. His publication record demonstrates expertise across multiple domains including concurrent separation logic, secure multi-party computation, and verified systems programming. Recent work shows a clear trend toward applying LLMs to traditional verification challenges, with publications on memory safety, Rust compilation error fixing, and loop invariant generation. Dr. Rastogi has served on numerous conference program committees including as PC co-chair for ISEC 2024 and Diversity, Equity, and Inclusion co-chair for POPL 2024. He has organized workshops such as VeriCrypt and taught F* at multiple summer schools, demonstrating strong commitment to academic community building. As part of Project Everest, he collaborates across Microsoft Research labs to develop verified security-critical components. His work on EverParse has hardened attack surfaces through formally proven parsers, while CrypTFlow enables secure medical image analysis through privacy-preserving machine learning techniques.

Julien Romeroمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Graphs
• Natural Language Processing
• +۳ مورد دیگر

Julien Romero serves as Maître de Conférences (Associate Professor) at Telecom SudParis, Institut Polytechnique de Paris, where he conducts research within the SAMOVAR laboratory. His work bridges artificial intelligence, database systems, and natural language processing with practical applications in knowledge representation and recommender systems. Romero earned his PhD in Artificial Intelligence from Institut Polytechnique de Paris in 2020 with a thesis on harvesting commonsense knowledge from web services. His research focuses on extracting and refining commonsense knowledge from unconventional sources including children's texts, query logs, and web content. He develops techniques for knowledge graph construction, query rewriting, and heterogeneous graph-based recommendation systems. Analysis of his 12 publications (2019-2024) reveals a strong trajectory from foundational database research toward applied AI systems. His work consistently addresses real-world challenges like job recommendation cold-start problems and commonsense knowledge base cleaning, demonstrating increasing technical sophistication through generative translation methods and heterogeneous graph approaches. Key publication venues include IEEE TKDE, CIKM, ISWC, and EMNLP. Romero actively mentors students through SAMOVAR laboratory projects, with research opportunities spanning knowledge engineering, natural language processing, and educational tool development. His Pyformlang library exemplifies his commitment to making theoretical computer science accessible. Current projects like TIMBRE (2024) indicate active industry-relevant research in professional recruitment systems. The SAMOVAR laboratory provides Romero's research environment, focusing on networks, computer science, and communication systems. His work within this framework emphasizes practical applications of theoretical concepts, particularly in knowledge representation and AI-driven recommendation systems. Ongoing projects suggest continued innovation in leveraging generative AI for knowledge base refinement and heterogeneous information network analysis.

Antoine Amarilliمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Data Management
• Probabilistic Computation
• +۷ مورد دیگر

Antoine Amarilli is an Associate Professor in Computer Science at Télécom Paris, part of Institut Polytechnique de Paris. Since September 2024, he has been on leave to work as an advanced researcher in the LINKS team at Inria Lille. His research spans theoretical computer science, focusing on query evaluation, probabilistic data, knowledge compilation, and computational logic. Member of DIG and LINKS research teams Part of LTCI (Information Processing and Communication Laboratory) Education: PhD in Computer Science (Télécom ParisTech, 2016) Habilitation (HDR) in Computer Science (Institut Polytechnique de Paris, 2023) Research Interests: He investigates efficient query evaluation on uncertain data, dynamic data maintenance, knowledge compilation techniques, and the interplay between circuits, automata, and logical languages. His work intersects database theory, graph theory, and computational complexity. Publications Trends: Recent articles address tractability of queries on probabilistic graphs, complexity classifications for MSO and regular path queries, and knowledge compilation frameworks for enumeration algorithms. Key subfields include probabilistic reasoning, graph constraints, and circuit-based optimizations. Awards: Best Paper at ICDT 2020 Best Paper at ICALP 2021 (Track B) Télécom Paris PhD Prize (2017) Beth Dissertation Award (2017) Teaching & Service: He taught competitive programming, algorithms, and data management at Télécom Paris and represented the institution on the MPRI master’s committee. He co-organized Highlights of Logic, Games, and Automata (2022) and served as a program committee member for ICDT, ICALP, and AAAI. Research Activism: Co-founded TCS4F (Theoretical Computer Scientists for Future) and No Free View? No Review! initiatives to address climate impact and open-access advocacy in academia.

Peisen Yaoمشاهده پروفایل

پژوهشگر

• Program Analysis and Verification
• Program Synthesis and Optimizations
• Logic and Automated Reasoning
• +۲ مورد دیگر

Peisen Yao is an academic researcher affiliated with Zhejiang University in China. His work spans programming languages, software engineering, and cybersecurity, with a focus on developing techniques to enhance software security, usability, and performance. He actively contributes to premier conferences as a committee member (e.g., PLDI, ICSE, ASE, ISSTA, OOPSLA, SAS). Research Focus: Yao's research integrates program analysis, verification, synthesis, and optimization. Key areas include: Advanced static analysis for security vulnerabilities (e.g., buffer overflows, divide-by-zero errors) Efficient symbolic abstraction and SMT solver testing Context-sensitive program analysis using formal language theory Automated invariant generation and code synthesis Publication Trends: His 15 most recent articles (2021–2025) emphasize scalable program analysis techniques, security validation, and practical tools for industry applications. Common themes include static analysis optimizations, formal verification, and novel testing methodologies for complex systems.