جست‌وجوی استاد و پژوهشگر

• Static Program Analysis
• Abstract Interpretation
• Programming Languages
• +۹ مورد دیگر

Caterina Urban is a Research Scientist at INRIA, affiliated with École Normale Supérieure in Paris, where she is a member of the ANTIQUE research team. She holds a PhD in Computer Science from École Normale Supérieure and completed her undergraduate and graduate studies at Università degli Studi di Udine with summa cum laude honors. She previously conducted postdoctoral research at ETH Zurich. PhD in Computer Science, École Normale Supérieure, Paris, France (2015) MSc in Computer Science, Università degli Studi di Udine, Italy (2011) BSc in Computer Science, Università degli Studi di Udine, Italy (2009) Her research spans formal methods, abstract interpretation, and static analysis, with a strong emphasis on ensuring reliability, fairness, and robustness in machine learning and data science software. She leads projects such as Lyra (on data science software), Libra (fairness certification), ForML, and SAIF. Her work bridges theoretical foundations with practical tool development, targeting real-world software challenges in AI safety. Her recent publications reveal a consistent focus on applying abstract interpretation to machine learning, data leakage, fairness, and program termination. Key themes include certified robustness, input usage analysis, and perfectly parallel verification, demonstrating both theoretical depth and practical impact. She has developed open-source tools like Libra and FuncTion. Best Poster Award at SOFSEM 2011 Artifact Evaluation Badges at OOPSLA 2020 Mentored award-winning undergraduate research (Global Undergraduate Awards 2022) She advises multiple PhD and postdoctoral researchers and has supervised numerous master’s and bachelor’s students. She is actively involved in the academic community, serving on program committees for PLDI, OOPSLA, and CAV, and is the general chair of iFM 2025. She teaches advanced courses at MPRI and international summer schools on abstract interpretation and formal methods. She leads research on data science software analysis, fairness in ML, and static verification of neural networks, often in collaboration with institutions across Europe. Her lab, part of the ANTIQUE team, fosters interdisciplinary work combining formal methods with AI and software engineering.

Patrick Massotمشاهده پروفایل

استاد

• Symplectic Geometry
• Contact Geometry
• Formalized Mathematics
• +۵ مورد دیگر

Patrick Massot is a Professor in the Department of Mathematics at the Faculty of Sciences of Orsay, University of Paris-Saclay, France. His work bridges pure mathematics and formal verification, with a focus on symplectic and contact geometry, and the formalization of advanced mathematical theories using the Lean proof assistant. His research interests include symplectic geometry , contact geometry , formalized mathematics , and differential topology . He has contributed significantly to the formalization of perfectoid spaces, the h-principle, and sphere eversion. His recent work emphasizes the educational use of proof assistants in teaching undergraduate mathematics. The most recent publications reflect a strong trend toward formal verification in mathematics, combining geometric intuition with rigorous computational proof. These works span topics such as convex integration, holonomic approximation, and the use of Lean for pedagogy. The underlying themes include flexibility in geometry, foundational rigor, and interdisciplinary collaboration between mathematics and computer science. Program Committee Member, CPP 2025 Author, Formalising the h-principle and sphere eversion (CPP 2023) Patrick Massot has advised no publicly listed students, and no specific grants are mentioned. However, his collaborative work with prominent mathematicians (e.g., Buzzard, Commelin, Giroux, Etnyre) suggests active research funding and participation in major projects such as the Liquid Tensor Experiment. He leads a formalized mathematics working group and was involved in a 2015–2016 working group on sheaf theory applied to Lagrangian submanifolds. These groups serve as hubs for collaborative research in formalization and geometric topology.

• Formal Methods
• Proof Theory
• Theoretical Computer Science
• +۱ مورد دیگر

Quentin Canu is a researcher affiliated with Inria and École Polytechnique. He presented a paper titled 'A Formal Disproof of Hirsch Conjecture' at the CPP track of POPL 2023, indicating his expertise in formal methods and proof theory. His research focuses on theoretical computer science, particularly formal verification and logic in computer science. His work at the intersection of discrete mathematics and computational logic highlights his contributions to rigorous proof systems and algorithmic analysis.

Filippo Alberto Edoardo Nuccio Mortarino Majno di Capriglioمشاهده پروفایل

مدرس

• Formalized Mathematics
• Algebraic Number Theory
• Proof Assistants
• +۶ مورد دیگر

Filippo Alberto Edoardo Nuccio Mortarino Majno di Capriglio is a Lecturer (Maître de Conférences) in Pure Mathematics at Université Jean Monnet Saint-Étienne, France. He is actively engaged in research at the intersection of formal mathematics and algebraic number theory, particularly through the Lean theorem prover. His research interests include: Formalized Mathematics Algebraic Number Theory Proof Assistants Lean Theorem Prover Number Theory Functional Analysis The recent publication in CPP 2024 on the formalization of complete discrete valuation rings and local fields reflects a strong trend in using interactive theorem proving to rigorously verify foundational results in algebraic number theory. His work contributes to the growing mathlib4 library, emphasizing correctness and formal verification in advanced mathematics. He has no listed scientific awards in the provided sources. Filippo is actively involved in academic service and education, notably organizing and teaching in the Lean Master’s program in Lyon (2024–2025), as shown by his public teaching repository. He contributes regularly to the Lean 4 and mathlib4 projects on GitHub, demonstrating strong engagement with the formal methods community. There is no mention of research grants in the provided texts. He is associated with the formal mathematics community through GitHub and conference participation (CPP, POPL), contributing to open-source developments in theorem proving and formalized algebra.

• Category Theory
• Higher Category Theory
• Formal Methods
• +۳ مورد دیگر

Jonathan Weinberger is affiliated with Chapman University, where he conducts research at the intersection of mathematics and computer science, specializing in formal verification of advanced categorical structures. Research Interests: Weinberger's work centers on: Category Theory Higher Category Theory Formal Methods Theorem Proving Homotopy Type Theory ∞-Categories He develops computational frameworks for verifying complex mathematical theorems using proof assistants, with emphasis on foundational structures in higher-dimensional category theory. His recent publication at CPP 2024, "Formalizing the ∞-categorical Yoneda lemma", demonstrates his focus on bridging abstract mathematical concepts with machine-checkable proofs, advancing reliability in theoretical computer science.

• Programming Languages
• Formal Verification
• Compilers
• +۳ مورد دیگر

Clément Chavanon is a researcher at INRIA (French National Institute for Research in Digital Science and Technology), actively contributing to programming languages and formal methods research as demonstrated by his publication at CPP 2024. His work focuses on bridging formal verification with networking systems, specifically developing mathematically proven compilers for network packet filtering. He leverages Binary Decision Diagrams to ensure correctness in security-critical infrastructure, targeting applications in network security and reliable software systems. This research intersects theoretical computer science with practical systems engineering challenges. Chavanon's recent publication exemplifies a growing trend in applying formal methods to real-world networking problems, emphasizing verifiable correctness in compiler design. His contributions advance the fields of programming languages and systems security, with implications for building tamper-proof network infrastructure through rigorous mathematical foundations.

Jeff Grayمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Development
• Domain-Specific Languages
• +۷ مورد دیگر

Jeff Gray is a University Distinguished Professor in the Department of Computer Science at the University of Alabama, where he also directs the Randall Research Scholars Program. He holds a Ph.D. in Computer Science from Vanderbilt University and has built a distinguished career in software engineering, model-driven development, and computing education. Education: Ph.D. in Computer Science, Vanderbilt University, 2002 M.S. in Computer Science, West Virginia University, 1993 B.S. in Computer Science (summa cum laude), West Virginia University, 1991 Dr. Gray's research focuses on model-driven software development, domain-specific languages, blocks-based programming environments, and computer science education outreach. His work bridges theoretical software engineering with practical applications in K–12 education and accessibility. He has developed tools like Spherly, Pixly, and Tunely to make programming more accessible. His research interests include software modeling, tool integration, debugging techniques, and inclusive programming environments. His recent publications reflect a strong trend in computing education, particularly in blocks-based programming, unit testing for beginners, and accessible language design. He has also contributed significantly to model-driven engineering and domain-specific language research. Scientific Awards: NSF CAREER Award Professor of the Year (Alabama, 2008) – Carnegie Foundation McMahon-Pleiad Prize (2025) Morris L. Mayer Premier Award Distinguished Member of the ACM IEEE Computer Society Distinguished Contributor (Inaugural Class) Dr. Gray has advised numerous doctoral students and has served as Editor-in-Chief of Software and Systems Modeling (Springer). He has secured over $24 million in external funding as PI or co-PI from agencies including NSF, Department of Education, and Google. He is actively involved in national initiatives such as the AP CS Principles course and Alabama’s Computer Science Advisory Council. He leads outreach programs including the Alabama Summer Computer Camps, Alabama Robotics Competition, and Code.org workshops. He also co-chairs the Development Committee for the College Board's AP CS Principles course and serves on Alabama Governor Kay Ivey’s Computer Science Advisory Council.

• Reactive Control Strategies
• Supervision and Prognosis
• Large Scale Systems
• +۱۱ مورد دیگر

Eric Duviella is a Professor at IMT Nord Europe, specifically within the Department of Automatic Control and Computer Sciences. He has been a permanent faculty member since 2007 and was promoted to full Professor in 2015. His academic affiliations include strong ties with the University of Lille, Universitat Politècnica de Catalunya, and the University of Seville through collaborative research and joint Ph.D. supervision. Education: Diplôme d'Ingénieur, Ecole Nationale d'Ingénieurs de Tarbes (ENIT), 2001 M.S., Institut National Polytechnique de Toulouse (INPT), 2001 Ph.D. in Industrial Systems, Institut National Polytechnique de Toulouse, 2005 HDR (Habilitation à Diriger des Recherches), University of Lille 1, 2014 His research interests center on Reactive Control Strategies , Supervision and Prognosis , and the Modeling of Large-Scale and Environmental Systems , particularly Hydrographical and Water Systems . His work aims at predictive maintenance, adaptive management under climate change, and improving navigation and water quality through advanced control techniques. The 15 most recent publications reflect a strong trend toward Model Predictive Control (MPC) , distributed and decentralized control architectures , leak detection in water networks , and resilience of inland waterways under climate stress . Keywords across these works include control engineering, water systems, fault diagnosis, and data-driven modeling, indicating a multidisciplinary approach combining automation, environmental science, and computational intelligence. Scientific Awards: Best paper award at CODIT 2017 Eric Duviella has actively supervised numerous Ph.D. and Master’s students from institutions including IMT Lille Douai, Universitat Politècnica de Catalunya, and the University of Seville. He has led significant research grants such as GEPET-Eau, CHEEF2, and CASTR-Eau, focusing on sustainable water management, hydropower optimization, and robotic monitoring. His work often involves partnerships with agencies like VNF, EDF, and local water authorities. He is involved in key research labs and teams including the Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), and collaborates internationally through mobility programs with UPC and the University of Seville. His leadership in organizing special sessions at major conferences (e.g., IFAC, HIC) underscores his role in shaping discourse in water system control and supervision.

José Netoمشاهده پروفایل

استاد

• Combinatorial Optimization
• Mathematical Programming
• Operations Research
• +۳ مورد دیگر

Prof. José Neto is a Professor at Télécom SudParis, part of the Institut Polytechnique de Paris, affiliated with the SAMOVAR research department. His work focuses on combinatorial optimization, mathematical programming, and graph theory. He has contributed to polyhedral studies of cut and assignment polytopes, spectral bounds for graph partitioning, and optimization algorithms for mixed-variable and blackbox problems. His research spans topics like network pricing complexity, domination in graphs, and robustness verification in neural networks. He has published extensively in top journals such as Mathematical Programming, Discrete Applied Mathematics, and Networks. Key contributions include developing efficient algorithms for combinatorial pricing, analyzing optimization relaxations, and exploring structural properties of discrete mathematical objects. His work bridges theoretical foundations and practical applications in operations research, with recent interests in binarized neural network verification and mixed-variable optimization. He has collaborated on projects related to cloud virtual machine mapping and combinatorial pricing models. Labs/Teams: Active member of the SAMOVAR laboratory, specializing in applied mathematics and optimization research.

Roua TOUIHRIمشاهده پروفایل

مدرس

• Software-Defined Networking (SDN)
• Data Center Networks
• Cloud Computing
• +۴ مورد دیگر

Roua TOUIHRI is a Lecturer at Telecom SudParis and an associate member of the ACMES group. She is affiliated with the SAMOVAR research laboratory, which focuses on networking and distributed systems. She holds a PhD in Hardware Architecture from Paris-Est University (2021), where her thesis addressed optimizing routing and resource allocation in SDN-based server-only data center networks for private cloud architectures. Her research interests center on Software-Defined Networking (SDN), cloud computing, and service-level agreement (SLA) management. She explores topics such as autonomous multi-cloud SLA adaptation, process mining for SLA reporting, and optimizing routing protocols in data center networks. Her work often intersects with network protocol design, resource allocation strategies, and improving network throughput in distributed systems. Her publications span conferences like IEEE BigData, IEEE ICC, and GLOBECOM, focusing on SDN-based solutions for data center networks, multi-cloud SLA management, and protocol optimization. She has contributed to developing novel routing schemes like M-CRP and CRP for CamCube server-only architectures, addressing latency reduction and resource utilization challenges. Roua TOUIHRI’s academic contributions are anchored in practical applications of SDN and cloud technologies, with a focus on scalable and efficient network designs for modern distributed systems.

David Bertoinمشاهده پروفایل

دانشیار

• Representation Learning
• Computer Vision
• Reinforcement Learning
• +۲ مورد دیگر

David Bertoin is a Research Engineer at Photoroom, focusing on training diffusion models for image editing. He concurrently serves as a part-time Associate Professor at INSA Toulouse, where he teaches and conducts research. Previously, he worked as a Research Scientist at IRT Saint-Exupéry, contributing to the DEEL and ANITI research programs. His research interests center on representation learning, computer vision, and reinforcement learning, with a particular emphasis on improving generalization and robustness in these domains. Bertoin actively participates in academic conferences, presenting work such as Local Feature Swapping at ICLR 2022 and studies on ReLU activation functions at Neurips 2021. His GitHub repositories reflect his engagement in projects like CLOP (Local Feature Swapping) and teaching modules for M2 MAPI3. He maintains an active presence in open-source contributions related to deep learning and reinforcement learning frameworks. Education details are not explicitly provided in the text, but his professional trajectory suggests advanced qualifications in computer science or artificial intelligence. His research outputs span theoretical advancements in neural networks and practical applications in autonomous systems, cybersecurity, and drone technology. Collaborations within ANITI and DEEL highlight his involvement in interdisciplinary AI initiatives.

• Natural Language Processing
• Argumentation Mining
• Information Extraction
• +۵ مورد دیگر

Elena Cabrio is a Full Professor at Université Côte d'Azur, with affiliations at Inria, CNRS, and I3S. She is a member of the Wimmics Research Team and a Chairholder at the 3IA, actively contributing to interdisciplinary research in artificial intelligence and natural language processing. Her research focuses on Natural Language Processing , Argumentation Mining , and Information Extraction . She aims to design debating technologies for advanced decision support systems, facilitating the exchange of information and opinions in critical domains such as healthcare and politics. Her work leverages machine learning and interdisciplinary approaches to model human argumentation in textual data. Elena Cabrio defended her HDR (Habilitation to Direct Research) in October 2020, marking a significant milestone in her academic career. As a 3IA Chairholder, she leads cutting-edge research initiatives and contributes to the development of AI technologies with societal impact. She is also involved in doctoral and postdoctoral training through seminars and collaborative research programs. She is affiliated with multiple research entities, including the Wimmics Research Team at Inria, which specializes in Web-based Information Systems and Knowledge Management. Her work bridges computer science, linguistics, and social sciences, aiming to create interpretable and human-centric AI systems.

Daniele Antonioliمشاهده پروفایل

استادیار

• Applied System Security
• Privacy
• Wireless Communication Security
• +۱۳ مورد دیگر

Daniele Antonioli is an Assistant Professor at EURECOM, affiliated with the Software & System Security (S3) group. His research spans applied system security and privacy, focusing on wireless communication (Bluetooth, Wi-Fi), embedded and mobile systems (e.g., smartphones, cars, fitness trackers), and cyber-physical systems like industrial control systems. His research interests include Applied System Security , Privacy , Wireless Communication Security , Embedded Systems Security , Automotive Security , IoT Security , Applied Cryptography , and Threat Modeling . His work frequently targets real-world protocols and proprietary systems, uncovering critical vulnerabilities in Bluetooth (e.g., KNOB, BIAS, BLURtooth), FIDO2 (CTRAPS), and IoT ecosystems (BreakMi, E-Spoofer). Recent publications demonstrate a strong trend in automotive and IoT security , protocol-level attacks , and privacy-preserving technologies , with work published in top venues like IEEE S&P, ACM CCS, NDSS, and USENIX Security. He has contributed to the DP3T/GAEN contact tracing framework, now used in Android and iOS. Mille E Una Lode Award CLUSIT Award'22 He has advised multiple PhD and Master’s students and received grants from the German Research Foundation (DFG). He teaches courses such as Mobile System Security and a Bluetooth-focused course at EURECOM. He has also been active in the research community, serving as Associate Editor for ACM TOPS and co-chairing NDSS'26 Artifact Evaluation. He leads or contributes to research projects including the AttackDefense Framework (ADF) , ENCOPIA , and ORSHIN , focusing on threat modeling, privacy assurance, and resilient IoT systems.

Mohamed Mekkiمشاهده پروفایل

پژوهشگر ارشد

• Network Management
• 5G and 6G Networks
• Cloud and Edge Computing
• +۴ مورد دیگر

Mohamed Mekki is a Postdoctoral Research Fellow in the Communication Systems Department at EURECOM, Sophia-Antipolis, France, specializing in zero-touch network automation for 5G/6G systems. He completed his PhD at EURECOM in July 2024 under Professor Adlen Ksentini's supervision, following a 2020 Computer Systems Engineering degree from the National School of Computer Science in Algiers and an internship on 5G network slice supervision. Education: PhD in Communication Systems, EURECOM (2024) Computer Systems Engineering, National School of Computer Science, Algiers (2020) Dr. Mekki's research pioneers AI-driven network management, focusing on Explainable AI (XAI) and Large Language Models (LLMs) to enhance trustworthiness in 6G automation. His work addresses critical challenges in cloud-edge continuum management, microservices resilience, and network slicing performance. Key contributions include frameworks for stateful microservice lifecycle management, fine-grained resource autoscaling, and scalable monitoring architectures validated in containerized multi-cluster environments. His six publications (2021-2025) reveal an evolutionary trajectory from foundational 5G slicing monitoring toward advanced AI integration for 6G. The research consistently bridges theoretical AI models with practical network requirements, emphasizing resilience in distributed systems and granular resource control across cloud-edge infrastructures. Scientific Awards: No documented awards or fellowships Advising and Grants: No current student supervision or major grant leadership is indicated; collaborative work suggests participation in EURECOM's industry-partnered projects and European research initiatives focused on 5G/6G validation. Labs and Teams: Dr. Mekki operates within EURECOM's Communication Systems research group alongside Professor Adlen Ksentini, Bouziane Brik, and Christos Verikoukis, contributing to European testbeds for next-generation network management solutions.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۱۲ مورد دیگر

Giovanna Maria Dimitri is an Assistant Professor Tenure Track in Artificial Intelligence at Universitá degli Studi di Milano (Statale), with additional affiliations at the ICE, University of Cambridge, and the Dipartimento di Ingegneria dell'Informazione e Scienze Matematiche (DIISM) at the University of Siena. She earned her PhD in Artificial Intelligence from the University of Cambridge under Prof. Pietro Liò, focusing on multilayer network methodologies for brain data analysis. She holds an MPhil in Advanced Computer Science from Cambridge with distinction and completed her Master’s and Bachelor’s in Computer and Automation Engineering at the University of Siena, both with top honors. PhD in Artificial Intelligence – University of Cambridge, UK MPhil in Advanced Computer Science – University of Cambridge, UK (Distinction) Master’s & Bachelor’s in Computer and Automation Engineering – University of Siena, Italy (110/110 cum laude) Her research spans a broad spectrum of artificial intelligence, including foundational models, deep learning, brain data modeling, and applications in healthcare, environmental science, and sustainability. She is particularly known for her work on GAN detection, emotional image datasets, climate change impact modeling, and AI for Sustainable Development Goals. Her interdisciplinary approach integrates computer science with neuroscience, public health, and social impact. Her recent publications reflect a strong trend in applying AI to real-world problems such as healthcare diagnostics (e.g., Brugada Syndrome detection), environmental monitoring (air quality, climate change on agriculture), and ethical AI (CO2 emissions of ML models). She also contributes to digital humanities and science communication, indicating a commitment to societal engagement and interdisciplinary collaboration. She has received the competitive Ai-Net Fellows Scholarship from DAAD in 2023, enabling collaboration with Prof. Gemma Roig’s lab. She is an Associate Editor for Neurocomputing (Elsevier) and was elected Associate Editor of IEEE Transactions on Technology and Society in May 2024. Dimitri has extensive teaching experience, lecturing Business Intelligence at the University of Siena and serving as a Guest Lecturer in Data Science at the University of Cambridge’s Institute of Continuing Education. She has supervised numerous students and has a publication record of nearly 60 peer-reviewed papers. She is also active in science communication, having been interviewed by Italian media and appearing on Rai Radio 1. She is a life member of Clare Hall College, University of Cambridge, and continues to contribute to academic and public discourse on AI through seminars, workshops, and editorial leadership.