جست‌وجوی استاد و پژوهشگر

Loris D'Antoniمشاهده پروفایل

دانشیار

• Programming Languages
• Software Verification
• Program Synthesis
• +۴ مورد دیگر

Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD) . He is also a Visiting Academic at Amazon Web Services (AWS) . His research focuses on helping people write trustworthy software through techniques in program synthesis, formal verification, and machine learning robustness. Bachelor and Master in Computer Science from University of Torino (2008, 2010) PhD in Computer Science from University of Pennsylvania (2015) His research integrates programming languages , automata theory , and formal methods to ensure software reliability. Recent work explores semantics-guided synthesis and specification-aligned LLMs , with applications in network security, machine learning fairness, and automated code repair. Key trends in his publications include program synthesis , formal verification , and trustworthy AI systems . He has contributed to tools like AutomataTutor and SemGuS , a framework for customizable synthesis problems using constrained Horn clauses. Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award NSF CAREER Award Microsoft Research Faculty Fellowship Google and Facebook Faculty Awards Best Paper Award at ICDCN 2023 Distinguished Paper Award at SBES 2021 D'Antoni actively contributes to academic community service as a committee member in PLDI , OOPSLA , POPL , and CAV . He leads the Programming Systems Group at UCSD and collaborates with SemGuS research team on synthesis frameworks.

Guillaume Chiavassaمشاهده پروفایل

استاد

• Wave Propagation
• Numerical Analysis
• Computational Fluid Dynamics
• +۶ مورد دیگر

Guillaume Chiavassa is a Professor in Applied Mathematics at Ecole Centrale de Marseille, affiliated with the Laboratoire M2P2 (Mechanics, Modeling and Physical Processes Laboratory). He leads research in the Thermodynamics, Waves, Digital, Interfaces and Combustion team, focusing on advanced computational methods for complex physical phenomena. His research spans wave propagation in porous media, numerical modeling of plasma flows in Tokamak configurations, multilevel schemes for conservation laws, penalization methods for compressible flows, and wavelets in numerical analysis. Chiavassa's work demonstrates exceptional mathematical rigor applied to challenging physical systems, particularly in nonlinear wave dynamics and computational fluid mechanics. His methodologies bridge theoretical mathematics with practical engineering applications. Analysis of his recent publications reveals a strong focus on wave propagation phenomena across diverse media, with significant contributions to numerical methods for nonlinear systems. His work consistently addresses the mathematical challenges of modeling complex physical behaviors including material softening, fractional attenuation in porous media, and plasma dynamics in fusion devices. The interdisciplinary nature of his research connects applied mathematics with mechanical engineering, geophysics, and nuclear fusion technology. Chiavassa leads the PROSPERO Software project and participates in the ANR Espoir research initiative and the Consortium SEISCOPE. His teaching activities include courses on hyperbolic equations, finite elements, and heat transfer, with practical computational components developed for student instruction. He maintains an active research program through Laboratory M2P2, where his team develops advanced numerical methods for simulating complex physical phenomena with applications ranging from environmental engineering to nuclear fusion research.

• Explainable Artificial Intelligence
• Industrial Machine Learning
• Business Process Management
• +۶ مورد دیگر

Adeel AHMAD is an active Associate Professor (Maître de Conférences) conducting cutting-edge research at the intersection of artificial intelligence, industrial applications, and business process management. His academic work demonstrates strong interdisciplinary connections between computer science, industrial engineering, and business informatics. Dr. AHMAD's research interests span Explainable Artificial Intelligence (XAI), Industrial Machine Learning, Business Process Management, Ontology-Based Reasoning, and Logistics Optimization. His work focuses on developing practical AI solutions for industrial contexts, particularly in Industry 4.0 environments where human-AI collaboration is essential. He has made significant contributions to meta-learning approaches for automated algorithm selection and configuration, with particular emphasis on making these systems transparent and interpretable for domain experts. His publication record shows a clear trajectory toward integrating explainability into industrial AI systems, with recent work focusing on conversational recommendation systems for cyber-physical environments. The research demonstrates consistent evolution from foundational work in business process analysis toward sophisticated AI applications in industrial settings. Active research leadership in Explainable AI for industrial applications Significant contributions to meta-learning frameworks for automated machine learning Interdisciplinary approach bridging computer science, industrial engineering, and business processes Strong publication record in top-tier conferences and journals Dr. AHMAD demonstrates strong collaborative research patterns, frequently working with colleagues including Mourad Bouneffa, Moncef Garouani, and other researchers in the French academic community. His work shows particular relevance to manufacturing, logistics, and cyber-physical systems where AI must work alongside human domain experts.

Ammar Mianمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Machine Learning
• Riemannian Geometry
• +۴ مورد دیگر

Ammar Mian is an Associate Professor at Université Savoie Mont Blanc, affiliated with the LISTIC lab and Polytech Annecy-Chambéry. He holds a PhD from CentraleSupélec (2016-2019) and conducted postdoctoral research at Aalto University (2019-2020). His research focuses on statistical signal processing, machine learning, and Riemannian geometry with applications in remote sensing and frugal computations. He leads the Qanat project, an experiment tracking tool for reproducible research. Research interests include covariance-based methods for SAR image analysis, robust detection algorithms for sonar and GPR systems, and optimization on Riemannian manifolds. His work emphasizes reproducibility in ML and efficient computational techniques for resource-constrained environments. Key contributions include real-time SAR time-series change detection, robust classification using second-order deep learning models, and novel methods for handling missing data in EEG signals. His recent articles (2023-2025) explore reproducibility frameworks, GPR-based object classification, and Riemannian geometry applications. No awards listed, but maintains active collaborations through LISTIC and industry partnerships. Advises students via internship programs (e.g., Federated ML energy cost analysis). Lab work involves developing open-source tools like Qanat for experiment management and reproducibility.

• Computer Science
• Databases
• Knowledge Representation & Reasoning
• +۷ مورد دیگر

Dr. Yacine Sam is a Lecturer in Computer Science at the Polytechnic School of the University of Tours (EPU), affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). His research focuses on databases, knowledge representation & reasoning, web services, and semantic web technologies. Doctorate in Computer Science, Paul Cézanne University Aix-Marseille 3 (2008) Master 2 Research in Computer Science, Claude Bernard University Lyon 1 (2005) Dr. Sam's research spans multiple subfields including: Trustworthy execution of adaptive business processes Privacy ontologies for Web of Things Deep learning applications in personalized service recommendations Linked open data frameworks for semantic integration Blockchain implementations in distributed systems Health analytics using collaborative IoT data Contact: yacine.sam@univ-tours.fr

Paul Thevenonمشاهده پروفایل

استادیار

• GNSS Signal Processing
• Navigation Systems
• Satellite Communication
• +۳ مورد دیگر

Dr. Paul Thevenon is an Assistant Professor at ENAC (National Civil Aviation School) in Toulouse, France, since July 2013. He holds a Habilitation à Diriger les Recherches (2023) and focuses on GNSS signal processing, integrity monitoring, and hybrid sensor systems. His research integrates advanced algorithms for cycle slip detection, multipath mitigation, and hybrid navigation filters using 5G/ LTE signals. Education: Bachelor's in Electronic Engineering from École Centrale de Lille (2004) Master's in Space Telecommunications from ISAE (2007) PhD in Signal Processing from ENAC (2010) Research Themes: GNSS robustness against malicious signals (e.g., evil waveforms) Multi-receiver systems for RTK positioning enhancement Integration of non-GNSS signals (5G, LTE) for hybrid navigation Factor graph approaches for carrier phase processing Awards: 2023: Habilitation à Diriger les Recherches Technical Contributions: Developed CNN-based multipath detection systems Advanced RTK ambiguity resolution via multi-rover arrays Validated TDCP measurements in low-cost PPP-IMU filters Labs/Teams: Core member of the SIGNAV lab, specializing in signal processing and navigation systems.

Francois Trahayمشاهده پروفایل

دانشیار

• High Performance Computing
• Storage Systems
• Parallel and Distributed Systems
• +۲ مورد دیگر

François Trahay is an Associate Professor at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His research focuses on high-performance computing systems, storage optimization, and performance analysis tools for parallel and distributed environments. He completed his PhD at Université Bordeaux I (2009) and Habilitation (HDR) at Institut Polytechnique de Paris (2021). Research Interests: Trahay specializes in optimizing storage systems (SSDs, RAID configurations), developing performance analysis frameworks (e.g., EZTrace, NumaMMA), and enhancing energy efficiency in HPC. Key areas include I/O performance, parallel runtime systems, and adaptive computing for machine learning workloads. Publication Trends: His recent work (2018–2025) demonstrates strong focus on: (1) SSD/RAID management techniques for modern storage hardware, (2) HPC performance tools for tracing and analysis, and (3) optimization strategies for distributed deep learning systems. Laboratory Affiliation: Member of SAMOVAR Laboratory (UMR CNRS), conducting research in distributed systems, networks, and computational efficiency.

• Chaos Control
• Nonlinear Dynamics
• Power Electronics
• +۳ مورد دیگر

Cristina MOREL is a Permanent lecturer-researcher at ESTACA since 2020, specializing in electrical engineering and control systems. Previously, she held academic positions at ESEO (2016-2020) and the IUT of Angers (2000-2020), and began her career at the Technical University of Cluj-Napoca (1992-1999). Her research focuses on nonlinear dynamics, chaos control, and applications in power electronics and renewable energy systems. Dr. MOREL holds a Doctorate in Automatics and Applied Computer Science from the University of Angers (2005) and a Habilitation to Direct Research (2016). She earned her Graduate Engineer degree in Automation and Computer Science from the Technical University of Cluj-Napoca (1992), where she was Vice-major of her promotion. 2016: Habilitation to Direct Research in Electronics, University of Angers 2005: Doctorate in Automatics and Applied Computer Science, University of Angers 1992: Graduate Engineer in Automation and Computer Science, Technical University of Cluj-Napoca Her research spans chaos control , nonlinear dynamics , and their applications in power electronics and renewable energy systems . She pioneered anticontrol of chaos to reduce electromagnetic interference in power converters and developed novel maximum power point tracking (MPPT) algorithms for photovoltaic systems under partial shading. Current work focuses on fault diagnosis for electric vehicle powertrains using entropy analysis and neural networks. Recent publications (2022-2025) show strong focus on chaos applications for MOSFET thermal management, entropy-based fault diagnosis in inverters, and energy storage optimization for electric vehicles and drones. There is clear evolution toward data-driven approaches (neural networks) for fault detection and real-world applications in e-mobility. Dr. MOREL has received several academic honors early in her career: Winner of the National University Assistant Competition (Romania, 1996) Winner of the National University Preparation Competition (Romania, 1993) Winner of the national entrance exam to the Polytechnic School of Cluj-Napoca (1987, 15th out of 700 candidates) She has supervised five PhD theses and three Master's research projects on topics including fault diagnosis in polyphase machines, energy management for battery-ultracapacitor vehicles, and MPPT for photovoltaic systems. Her research is supported by collaborations with University of Angers, ESEO, and ESTACA laboratories. Dr. MOREL led the Automation and Electrical Engineering research group at ESEO (2016-2018) and the Energy and Environment option (2016-2020), conducting experimental work in power electronics and control systems laboratories focused on converter design and EV powertrain validation.

• Building Energy Performance
• Smart Building Controls
• Occupancy Modeling
• +۵ مورد دیگر

Kevin MICHENEAU is a Teacher-Researcher at CESI School of Engineering, affiliated with the LINEACT research laboratory in Guipavas, France. His work bridges building energy systems and experimental particle physics, focusing on data-driven optimization of smart buildings and dark matter detection. Education: PhD in Subatomic Physics, University of Nantes (2018): "Study of residual electrons in the XENON100 experiment" Master's degree in Research in Subatomic Physics, University of Nantes (2014) Research Focus: Dr. MICHENEAU develops advanced models for building energy performance with emphasis on occupancy behavior impact and smart control systems . His methodology combines sensor fusion and multi-objective optimization to balance energy efficiency with occupant comfort. Previously, he contributed to XENON dark matter experiments through signal reconstruction and background modeling. Publication Evolution: His research trajectory shows a strategic pivot from particle physics (2017-2019) to building energy systems (2024), applying rigorous data analysis techniques across domains. The 2024 MPC optimization study demonstrates transferable methodology from high-precision physics to sustainable engineering. Mentorship: Currently supervising PhD candidate BOURGOIN on "Towards modeling the impact of occupancy on the energy behavior of smart buildings" (2023-2026). Research Ecosystem: Member of the "Engineering and Digital Tools" team within LINEACT, teaching Computer Science, Mechanics, and Physics across preparatory and engineering cycles while contributing to PhD training at University of Nantes.

Sohrab Faramarzi Oghaniمشاهده پروفایل

استادیار

• Sustainable Supply Chain Management
• Supply Chain Resiliency
• Facilities Design
• +۳ مورد دیگر

Sohrab Faramarzi Oghani is an Assistant Professor in the Supply Chain Management & Information Systems Academic Area at Rennes School of Business (France). He holds a Ph.D. in Computer Science from Université de Lille (2018) and degrees in Industrial Engineering from the University of Tehran and Tabriz. His roles include Programme Manager for the MSc in Supply Chain Management and teaching courses such as Principles of Supply Chain Management, International Sourcing & Procurement, and Data Science for Advanced Production Systems. His research focuses on Sustainable Supply Chain Management , Supply Chain Resiliency , and Clinical Laboratory Design . He has published in journals like International Journal of Production Research and presented at conferences like META’2018. His work integrates mathematical modeling and simulation to optimize facilities design, particularly in healthcare and logistics sectors. He has contributed to patents in clinical laboratory optimization, including methods for layout design, instrument selection, and cost reduction. His recent studies emphasize sustainable practices and resiliency in supply chains, with a 2023 review paper on meta-heuristics in this domain. Professional activities include organizing international conferences (META, BIOMA, OLA) and collaborating with Inria Lille Nord Europe during his PhD. He advocates for applying advanced analytics to solve real-world operational challenges in both industrial and healthcare environments.

• Automated Theorem Proving
• Machine Learning Integration
• Formal Verification
• +۷ مورد دیگر

Konstantin Korovin is a Reader at the Department of Computer Science, University of Manchester, with over 20 years of experience in automated reasoning and formal verification. He previously held roles as Senior Lecturer (2015-2023) and Royal Society University Research Fellow (2007-2015). His research spans automated theorem proving, machine learning integration, and constraint solving for hardware/software verification. Doctoral thesis: Knuth-Bendix orders in automated deduction (2003, University of Manchester) Current teaching: Logic and Modelling (COMP21111), Automated Reasoning (COMP60332) Research interests include: Automated Theorem Proving (ATP) and SMT solving Combining machine learning with formal verification Nonlinear constraint analysis via δ-complete procedures DNA computing and bio modeling applications Recent publications focus on symbolic ML verification (SMLP), conflict-driven quantifier elimination (VIRAS), and enhanced C++ model checking (ESBMC). His work has been recognized with best paper awards at FroCoS'19 and CP'09. Supervised: 9 PhD/PostDocs including Maryam Abdullah, Franz Brausse, and Andrzej Kucik Collaborated with industry (Intel, Google, MathWorks) on verification tools Contact: konstantin.korovin@manchester.ac.uk

Wenxi Wangمشاهده پروفایل

استادیار

• Formal Methods
• Machine Learning
• Software Engineering
• +۳ مورد دیگر

Dr. Wenxi Wang is an Assistant Professor in the Department of Computer Science at the University of Virginia, where he leads the Hiprel research group. He completed his PhD at the University of Texas at Austin under the supervision of Sarfraz Khurshid, with close collaborations with Kenneth McMillan and Darko Marinov. His research bridges software engineering, formal methods, and machine learning, focusing on enhancing software security and reliability through innovative techniques. Research Interests: Wang's interdisciplinary work explores: (1) Integration of deep learning (LLMs, GNNs) with automated reasoning tools like SAT/SMT solvers; (2) Enhancement of software verification tools (Verus, Dafny) using LLMs; (3) Improvement of ML model/framework reliability; and (4) Advanced code generation quality through verification. His group develops methods to combine formal methods with machine learning for robust software systems. Recent Publications: Wang's 15 most recent publications (2018-2024) demonstrate consistent focus on ML-formal methods integration, particularly in SAT solving, software verification, and testing. Key trends include GNNs for constraint solving, automated repair of security vulnerabilities, and novel testing methodologies for formal tools. His work appears in top venues including ICLR, ASE, ESEC/FSE, and ICSE. Awards and Honors: George J. Heuer, Jr. Ph.D. Endowed Graduate Fellowship (2023-2024) Rising Stars in EECS (2022) Research Group: Leads the Hiprel Group with 6 members: PhD Students: Zichen Xie (ML for verification), Lize Shao (LLMs for SE) Master's: Chaitanya Shahane (LLMs for testing) Undergraduate: Carter Opperman (SAT solving) Interns: Tianyi Huang (ML for SAT), Mrigank Pawagi (LLMs for protocol verification)

Sara Rivaمشاهده پروفایل

دانشیار

• Discrete Dynamical Systems
• Cellular Automata
• Boolean Networks

Sara Riva is an Associate Professor (Maître de Conférences) in Computer Science at Université de Lille, affiliated with the CRIStAL laboratory (UMR 9189). She is a member of the BioComputing research group and the MSV thematic group. Her academic journey includes a PhD jointly supervised by Université Côte d'Azur and Università degli Studi di Milano-Bicocca (2019-2022) and postdoctoral research at Université de Bordeaux (2022-2023). Her research explores Discrete Dynamical Systems , Cellular Automata , and Boolean Networks , with emphasis on equation solving, factorization methods, and dynamics modeling. She develops algorithmic approaches to analyze complex behaviors in computational and biological systems. Publications (2019-2023) demonstrate consistent focus on theoretical foundations of discrete systems, with applications in systems biology and complex modeling. Key themes include Boolean network dynamics, computational pipelines for equation solving, and sensitivity analysis in cellular automata. Awards: First prize for PhD students (Computer Science), STIC doctoral school Teaching: Extensive instructional experience at Université de Lille and Université Côte d'Azur covering: Algorithms & Programming (72+ lab hours) Databases (39+ lab hours) Logic, Graph Theory, Web Technologies (18+ lab hours each) IT Security and Information Coding (18 hours each) Academic Service: Member of CRIStAL's parity commission; Program Committee for AUTOMATA 2024; President of ADSTIC PhD association (2021-2022); Organized summer schools (EJCIM 2022).

Jérémy Froidevauxمشاهده پروفایل

استادیار

• Conservation Biology
• Ecoacoustics
• Bat Ecology
• +۳ مورد دیگر

Jérémy Froidevaux is an Assistant Professor at the University of Franche-Comté, affiliated with the Chrono-environment Research Unit (UMR 6249 CNRS/UFC). His research focuses on conservation biology, eco-acoustics, and bat ecology, with emphasis on evaluating anthropogenic impacts on biodiversity, optimizing monitoring protocols, and assessing conservation measures' effectiveness. Key projects include studies on renewable energy infrastructure (wind turbines, solar farms) and their effects on bat populations. He leads the Silent·Cities initiative, analyzing urban soundscapes during global crises like the 2020 pandemic. Collaborative efforts with international teams address issues like light pollution, electromagnetic field exposure, and biodiversity policy implementation. His academic contributions span 150+ peer-reviewed articles, including work on bat activity patterns at power lines, solar farm impacts, and species-level trait datasets for European bats. Froidevaux advises graduate students, such as Samantha Aguillon, and actively participates in cross-disciplinary initiatives like the Réunion Island Night Environment Observatory. His work bridges ecological research with practical conservation solutions, advocating for evidence-based policies in renewable energy planning and habitat management.

Jean-Rodolphe Rocheمشاهده پروفایل

استاد

• Numerical Analysis
• Domain Decomposition
• Nonlinear Equations
• +۴ مورد دیگر

Professor Jean-Rodolphe Roche is a Full Professor at Université de Lorraine , affiliated with the engineering college Polytech Nancy and the Institute Élie Cartan de Lorraine (IECL). His research focuses on numerical methods for partial differential equations, domain decomposition, nonlinear optimization, and scientific computing. Research Fields : Numerical Analysis, Domain Decomposition, Nonlinear Equations, Scientific Computing, Shape Optimization His recent publications emphasize adaptive numerical techniques for nonlinear boundary value problems, domain decomposition in electromagnetic casting, and radiative-conductive heat transfer systems. Collaborations span institutions in Brazil, France, and international conferences.