جست‌وجوی استاد و پژوهشگر

Stephen Chongمشاهده پروفایل

استاد

• Programming Languages
• Information Security
• Language-based Security
• +۳ مورد دیگر

Stephen Chong is a Gordon McKay Professor in the Harvard John A. Paulson School of Engineering and Applied Sciences , where he co-directs the Undergraduate Studies in Computer Science program. His research intersects programming languages and information security , focusing on language-based security frameworks. Education : PhD in Computer Science from Cornell University (2008), B.Sc.(Hons) and B.A. from Victoria University of Wellington (New Zealand). Research : Develops tools like Formulog (Datalog + SMT for static analysis) and Accrue (Java interprocedural analysis), emphasizing security guarantees proportional to programmer effort. Grants : Funded by NSF , DARPA , AFOSR , and Google Faculty Research Award . Recent publications focus on neurosymbolic approaches (e.g., Guess & Sketch ), Datalog synthesis (e.g., Making Formulog Fast ), and quantitative robustness in cyber-physical systems. His group has pioneered formal methods for secure assembly transpilation and sensor attack modeling. Awards : NSF CAREER Award AFOSR Young Investigator Award Sloan Research Fellowship Advising : Supervised numerous PhD and senior thesis students, including Aaron Bembenek , Anitha Gollamudi , and Lucas Waye . Mentored projects like AbcDatalog (multi-threaded Datalog engine) and Shill (secure shell scripting). Labs/Teams : Leads the Programming Languages at Harvard group, collaborating with institutions globally. Organized workshops (e.g., NSF Workshop on Formal Methods for Security ) and chaired committees at conferences like CSF , POPL , and PLDI .

Mario Alvianoمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Combinatorial Search and Optimization
• +۵ مورد دیگر

Mario Alviano is a Full Professor in Computer Science (INF/01) at the University of Calabria, Department of Mathematics and Computer Science. He leads the LAIA lab (Laboratorio di Applicazioni dell'Intelligenza Artificiale) and serves as co-PI in the PRIN project PRODE ('Probabilistic Declarative Process Mining'). Current projects: FAIR ('Future AI Research'), Tech4You ('Technologies for climate change adaptation'), SERICS ('SEcurity and RIghts in the CyberSpace'), CAL.HUB.RIA , RADIOAMICA , and STROKE 5.0 His research focuses on Answer Set Programming (ASP), particularly in optimization, nonmonotonic reasoning, and applications to logistics, healthcare, and cybersecurity. He has authored over 120 publications in top venues like AIJ, AAAI, and IJCAI. Recent academic contributions includes work on: Temporal Many-valued Conditional Logics Weighted Knowledge Bases with Typicality Explainable AI via xASP and ASP Chef Defeasible Reasoning Scalability Notable awards: Artificial Intelligence Award 'Marco Somalvico' (2017) ICLP Best Paper Awards (2015, 2016) LPNMR Best Paper Award (2022) CILC Best Paper Award (2023)

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.

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

Christopher Alexander is a Researcher at GE Research , focusing on formal methods and safety-critical systems. His work bridges Computer Science with Aerospace Engineering , emphasizing rigorous verification techniques. Research Areas : Answer Set Programming (ASP) SMT Solving Compliance Checking Conflict Detection Formal Verification Optimization in Autonomous Systems Recent publications highlight his contributions to 2023 Symposium on Practical Aspects of Declarative Languages and NASA Formal Methods 2024 . His work spans theoretical frameworks for deep learning and practical applications in aerospace systems.

René Quiniouمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Mining
• Online Monitoring
• +۴ مورد دیگر

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.

Michael Greenbergمشاهده پروفایل

پژوهشگر

• Programming Languages
• Static Analysis
• Type Systems
• +۴ مورد دیگر

Michael Greenberg is a researcher at Stevens Institute of Technology. He actively contributes to programming language design, static analysis, and formal methods, with a focus on Datalog, SMT solvers, and type systems.

Ruben Martinsمشاهده پروفایل

استادیار

• Program Synthesis
• Software Verification
• Security
• +۷ مورد دیگر

Ruben Martins is an Assistant Professor at Carnegie Mellon University, focusing on constraint programming, program synthesis, analysis, and verification. His work aims to enhance programmer productivity and automate data science tasks through synthesis techniques. Education: Ph.D. (2013) from Technical University of Lisbon; Postdoc at University of Oxford (2014-2015) and UT Austin (2015-2017). Research Interests: Program Synthesis, Software Verification, Security, Decision Procedures, Boolean Satisfiability, MaxSAT, and their applications in real-world optimization. Article Trends: Recent publications emphasize integrating large language models (LLMs) with synthesis techniques for automated repair and refactoring, alongside advancements in MaxSAT solvers like Open-WBO. His work spans venues such as POPL, PLDI, FSE, and SAT. Scientific Awards: Distinguished Paper Award at PLDI 2018; Gold Medals in MaxSAT Competitions for Open-WBO. Current Roles: Committee Member in OOPSLA Review Committees across multiple conferences.

Anne Siegelمشاهده پروفایل

استاد

• Bioinformatics
• Systems Biology
• Computational Biology
• +۵ مورد دیگر

Anne Siegel is a CNRS Research Director based at IRISA (Institute for Research in Computer Science and Random Systems), affiliated with the University of Rennes and CNRS. She currently serves as a Scientific Officer at CNRS Informatics (INS2I), overseeing the transversal mission for gender equality, having previously served as Deputy Scientific Director from 2021 to 2025. Her research focuses on the intersection of computer science and biology, particularly in developing symbolic methods for knowledge representation and integration to analyze large-scale biological systems. Former student of ENS Lyon Associate professor of mathematics Doctor of mathematics from the University of Aix-Marseille (2000) Joined CNRS in 2002 Research Director position since 2010 Anne Siegel's research spans the interface between computer science and biology, with particular emphasis on symbolic approaches to knowledge representation and integration for analyzing large-scale biological networks. Her early work focused on mathematics-computer science interfaces through the study of symbolic dynamic systems. She then transitioned to biology-computer science interfaces, developing methods to analyze metabolic networks of organisms including macro-algae. Her current research contributes to various scientific projects focusing on methods for modeling the metabolism of organisms in interaction with their microbiota. She has made significant contributions to bioinformatics, systems biology, and computational modeling of biological processes. Her publication record demonstrates a strong focus on developing computational methods for biological network analysis, particularly in metabolic modeling and logical signaling networks. The research trends show a consistent pattern of interdisciplinary work combining mathematical rigor with biological applications, with a recent emphasis on microbial communities, metabolic modeling, and the development of computational tools for systems biology. Her work often involves collaborations across multiple institutions and countries, reflecting the interdisciplinary nature of her research. Best paper award (CMSB 2012) Active involvement in gender equality initiatives in science Anne Siegel has supervised numerous PhD students and researchers throughout her career, with a particular focus on interdisciplinary projects at the interface of computer science and biology. Her leadership roles have included creating and leading the Dyliss team (bioinformatics, systems biology) within IRISA laboratory (2012-2019), and serving as head of the 'Data and Knowledge Management' department (2019-2021). She has been actively involved in multiple research projects funded by institutions including ANR, Inria, and CNRS, with focus areas spanning from algal metabolism to plant microbiomes and fermented products. She has been instrumental in establishing and leading the Dyliss team at IRISA, which focuses on bioinformatics and systems biology. Her leadership extends to national committees including the CNRS National Committee and the Inria Evaluation Committee. She has also played a key role in gender equality initiatives within CNRS Informatics, contributing to the creation of the transversal parity-equality mission.

Joaquín Ariasمشاهده پروفایل

دانشیار

• Constraint Logic Programming
• Answer Set Programming
• Event Calculus
• +۴ مورد دیگر

Joaquín Arias is an Associate Professor at King Juan Carlos University since 2025. His career includes 2020-2025 as Assistant Professor at the same institution and prior pre-doctoral research at IMDEA Software Institute (2013-2020). He has collaborated with institutions like University of Texas at Dallas and Aalto University. Education: Ph.D. in Computer Science (2020, Universidad Politécnica de Madrid) M.Sc. in Computer Science (2015) B.Sc. in Computer Science (2014) M.Arch. in Architecture (2002) Research interests revolve around Constraint Logic Programming , Answer Set Programming , and their applications in Event Calculus , Stream Data Analysis , and Value-Aware Systems . He has developed frameworks like s(CASP) for non-grounded reasoning and Mod TCLP for tabled constraints. Recent articles demonstrate expertise in integrating Large Language Models with logic programming, automated legal reasoning , and real-time system verification . His work emphasizes explainability, constraint handling, and semantic coherence in AI systems. Scientific awards include the best paper prize at CAEPIA 2021 . He has contributed to proceedings as editor for ICLP workshops and authored numerous papers in TPLP , PADL , and AI & LAW .

Brice Chardinمشاهده پروفایل

دانشیار

• Data Engineering
• Clustering Algorithms
• Query Relaxation
• +۵ مورد دیگر

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.

Maxime Folschetteمشاهده پروفایل

دانشیار

• Computer Science
• Formal Methods
• Bioinformatics
• +۳ مورد دیگر

Maxime Folschette is an Associate Professor at Centrale Lille Institut, affiliated with the CRIStAL research laboratory (UMR 9189) and the BioComputing research group. His position combines teaching responsibilities at Centrale Lille and its internal school IG2I with active research in computational biology and formal methods. His primary research focuses on the analysis and learning of dynamical biological models, with particular emphasis on formal methods applied to biological regulatory networks. His work spans multiple domains including Parameters Inference, Static Analysis, Polyadic μ-calculus, and Hoare Logic, all applied to biological systems modeling. Folschette's publications demonstrate a strong trend toward developing computational frameworks that bridge theoretical computer science with practical biological applications. His recent work shows increasing focus on hybrid modeling approaches that combine discrete and continuous dynamics, logical learning techniques for network inference, and applications in medical domains such as diabetes modeling and cancer research. ILP best paper award during IJCLR'2021 Best Paper Award for BIOINFORMATICS'2024 As an advisor, Folschette currently mentors two PhD students (Madeleine Eyraud and Karim Raqbi) and has successfully guided two PhD students to defense (Honglu Sun in 2023 and Danilo Dursoniah in 2024). His research is supported through multiple collaborations across France and Japan, particularly with the BioComputing team at CRIStAL. His laboratory work focuses on developing computational tools like Pint, Hybrid Hoare Logic tool, key-pipline, INEX-MED, and pyBRAvo for biological network analysis.