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Daniel Varro is a Professor affiliated with McGill University (Faculty of Engineering, School of Computer Science), with strong ties to Budapest University of Technology and Economics and Linköping University. He is a leading researcher in model-driven engineering, cyber-physical systems, and software engineering, actively contributing to top-tier conferences such as MODELS, ICSE, and ASE. His research focuses on model-based systems engineering (MBSE) , automated model generation , model transformations , and constraint-based consistency checking . Recently, his work has expanded into integrating large language models (LLMs) and machine learning into modeling workflows, including model querying, domain modeling, and bug detection. The recent publications reveal a strong trend toward AI-augmented modeling, logic-based solvers (e.g., Refinery), and safety assurance of autonomous systems (e.g., COLREGs compliance). His work bridges formal methods with practical software engineering challenges in industrial and safety-critical domains. Scientific Awards: No specific awards mentioned in the text. Advising and Grants: While no explicit list of students or grants is provided, his mentorship in the Doctoral Symposium and repeated leadership roles suggest active supervision and likely grant funding. He has led projects on automated model generation, model quality, and AI integration in modeling. Labs and Teams: Daniel Varro is associated with research groups focused on model-driven engineering and software evolution, likely leading or co-leading teams working on the VIATRA and Refinery frameworks for model transformation and solving.

• Model-Based Systems Engineering
• Cyber-Physical Systems
• Software Engineering
• +12 more

Alastair F. Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he leads the FastPL research group. His work bridges formal methods, software testing, and programming languages, with a focus on enhancing the reliability of high-performance and parallel software systems. He has held key roles including Director of Research (since 2023) and previously served as Lecturer (2011–2014), Senior Lecturer (2014–2017), and Reader (2017–2020) before being promoted to Professor in 2020. His research interests include formal verification, compiler testing, GPU programming, concurrency, and fuzzing. He has made significant contributions to the verification of GPU kernels, metamorphic testing of graphics drivers, and the development of tools like GPUVerify and GraphicsFuzz. His work combines theoretical rigor with practical impact, demonstrated by the acquisition of his startup GraphicsFuzz by Google in 2018 and his subsequent roles as Senior Software Engineer and Visiting Researcher at Google. His recent publications reflect a sustained focus on compiler and system reliability, with trends in fuzzing, formal specification, and automated testing of complex systems such as WebGPU, CXL cache coherence, and large language models for code generation. His work increasingly integrates empirical validation with formal techniques to uncover subtle bugs in real-world systems. Scientific awards and recognitions include: 2017 BCS Roger Needham Award EPSRC Early Career Fellowship Fellow of the British Computer Society Best Paper awards at EuroSys 2024, MET 2021, IISWC 2019, IWOCL 2019, and ICST 2016 Best Industry Paper at ICST 2024 ACM SIGSOFT Distinguished Paper at ISSTA 2023 ACM SIGPLAN Most Influential OOPSLA Paper Award (2012 paper), awarded in 2022 Best Student Paper at PPoPP 2014 He has advised numerous PhD students and leads a vibrant research group. He has secured significant research funding and collaborates extensively with industry and academia. His service includes leadership roles such as General Chair of PLDI 2020, PC Chair of ECOOP 2019, and Steering Committee Chair of PLDI (2022–2025). He also serves on the advisory board of PACM-PL and on program committees for top venues including POPL, OOPSLA, PLDI, ICSE, and ISSTA. He leads the FastPL research group, which focuses on the design and implementation of programming tools and techniques for reliable software. The group conducts cutting-edge research in compiler testing, formal methods, and high-performance systems, fostering collaboration across academia and industry.

• Formal Verification
• Software Testing
• Programming Languages
• +12 more

Bryan Parno is a Professor at Carnegie Mellon University in the Departments of Electrical & Computer Engineering and Computer Science . He is the recipient of the Kavčić-Moura Chair and leads the Secure Foundations Lab , focusing on end-to-end secure systems through formal verification. Research spans secure systems , applied cryptography , distributed systems , and zero-knowledge proofs Developed Verus (verified Rust systems) and Project Everest (verified HTTPS stack) Key contributions include Ironclad , Flicker , and Pinocchio , with impacts on Intel CPUs and Windows/iOS security models His work emphasizes open-source tools and reproducibility , often published in top venues like POPL, PLDI, and IEEE S&P. Recent projects address WebAssembly security and formal verification of complex distributed systems . Major Awards Jay Lepreau Best Paper Award (OSDI 2025) IEEE Cybersecurity Award for Practice (2024) Sloan Research Fellowship (2018) Test-of-Time Awards (IEEE S&P 2023, IEEE S&P 2020) Best Paper Awards at USENIX Security, OOPSLA, and PLDI

• Secure Systems
• Formal Software Verification
• Verifiable Computation
• +7 more

Craig Lee is a Professor of Oceanography at the University of Washington, where he also serves as Senior Principal Oceanographer and Assistant Director for Research at the Applied Physics Laboratory. His work focuses on physical oceanography with emphasis on observational studies and instrument development. Lee leads research programs studying upper ocean dynamics, coastal processes, and high-latitude oceanography across diverse regions including the Arctic, North Atlantic, and South China Sea. Dr. Lee's educational background includes: B.S. in Electrical Engineering and Computer Science from the University of California, Berkeley (1987) Ph.D. in Physical Oceanography from the University of Washington (1995) Lee's primary research interests center on three interconnected areas: (1) upper ocean dynamics, particularly mesoscale and submesoscale fronts and eddies; (2) interactions between biology, biogeochemistry and ocean physics; and (3) high-latitude oceanography in changing Arctic environments. His work often combines field observations with instrument development to address fundamental questions about ocean circulation and its role in climate systems. He has pioneered approaches using autonomous platforms to study difficult-to-access regions like ice-covered waters. Analysis of Lee's recent publications reveals a strong focus on Arctic oceanography, upper ocean mixing processes, and the application of autonomous observing technologies. His research spans multiple ocean basins with particular emphasis on the Arctic, North Atlantic, and western Pacific. A notable trend is the increasing integration of biogeochemical measurements with physical oceanography to understand coupled systems. His work often addresses climate-relevant questions about ocean circulation, heat transport, and ecosystem responses to environmental change. Dr. Lee provides leadership through service on science steering committees for large research programs and advisory panels for U.S. Arctic efforts. He actively supports and advises graduate students while teaching courses on ocean circulation observations and experimental design. His team has developed innovative technologies including autonomous gliders for ice-covered waters, high-performance towed vehicles, and lightweight mooring systems. Lee leads a research team pursuing diverse field programs including Arctic PISCES, Stratified Ocean Dynamics of the Arctic (SODA), and studies of the Kuroshio Current. His group collaborates extensively with institutions worldwide and contributes to major international research initiatives focused on understanding ocean processes and their climate implications.

• Physical Oceanography
• Upper Ocean Dynamics
• Coastal Ocean Processes
• +7 more

Aurèle Maheo is a Research Fellow at the Computer Science Department of Telecom SudParis, affiliated with the Parallel and Distributed Systems (PDS) group. His research focuses on distributed systems, parallel computing, and hardware acceleration, with applications in machine learning and high-performance computing. He has contributed to projects such as SpeedyLoader for optimizing machine learning pipelines and FPGA memory bandwidth improvements. His work bridges theoretical distributed systems principles with practical implementation challenges in large-scale environments. Affiliations: PDS Group, Computer Science Department, Telecom SudParis Research interests include distributed consensus algorithms, system scalability, and hardware-software co-design for accelerating computational workflows. Recent work highlights include presentations on machine learning pipelining techniques and FPGA-based memory optimization strategies.

• Distributed Systems
• Parallel Computing
• Machine Learning Systems
• +3 more

Shadi Ibrahim is a Researcher at the Inria Centre at Rennes University , specializing in Big Data management since 2013. His work focuses on optimizing data flow processing in cloud environments, particularly through the KerStream project (2017–2022), which addressed limitations in platforms like Spark and Flink by developing methods for task lag detection and energy-efficient allocation. Key research areas: Big Data, Cloud computing, Erasure codes, HPC convergence Notable collaborations include industry partnerships with OVH and startup Hive (2021–2022) to reduce data duplication via erasure codes. He received the ACM Distinguished Member honor (2022) and the IEEE TCSC Excellence Prize (2020). His work also intersects with exascale computing initiatives via partnerships with Argonne National Laboratory and Lawrence Berkeley National Laboratory .

• Big Data management
• Cloud computing
• Distributed systems
• +3 more

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +3 more

Karinne Miqueu is a CNRS Research Director at the Université de Pau et des Pays de l'Adour (UPPA), affiliated with the Institute of Analytical Sciences and Physico-Chemistry for the Environment and Materials (IPREM). She holds a PhD in Physical Chemistry (2000) and a HDR (2011). Her research focuses on molecular architectures, electronic structure, spectroscopic properties, and reaction mechanisms, with a strong emphasis on computational chemistry and photoelectron spectroscopy. She leads projects in organometallic chemistry, catalysis, and material science. Education: 2011: HDR in Chemistry-Physics, UPPA 2000: PhD in Physical Chemistry, UPPA 1996: DEA (Master 2R) in Chemistry-Physics, UPPA Research Interests: Karinne Miqueu's work combines experimental and theoretical approaches to study molecular systems, including unusual bonding modes, reactive intermediates, and catalytic systems. Key themes include gold chemistry, main group chemistry, and the development of novel catalysts for organic synthesis. Projects & Awards: Lead ANR projects on gold chemistry (GOLD-III), polymerization (CARBENOPOL), and catalytic systems Recipient of CNRS Bronze Medal (2010), Premium Scientific Excellence (2011-2014), and 'Talented Woman' award (2012) Leadership Roles: Elected member of UPPA's Academic Council (2020-2024) Member of IPREM's management board and MCIA High-Performance Computing Mesocentre Chair of the French Chemical Society, Aquitaine Section (2015-2020)

• Organometallic Chemistry
• Computational Chemistry
• Photoelectron Spectroscopy
• +7 more

Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +3 more

Nicolas Lachiche is a Professor in Computer Science at the University of Strasbourg, where he serves as Deputy Head of the SDC (Data Science and Knowledge) team within the Engineering, Computer and Imaging Sciences Laboratory (ICube - UMR 7357). His research focuses on machine learning and data/knowledge management across various application domains. Professor Lachiche's primary research interests include data science, relational data mining, sequential and temporal data mining, anomaly detection, and NoSQL databases. His work spans healthcare applications (ICU with Vincent Castelain), high-performance computing (with Florina Ciorba), materials science (with Yves-André Chapuis), and smart factories (with Technology & Strategy). His research trajectory shows evolution from early foundational work in scope classification (94-97) and the PRIMUS family of systems (97-04) to current cutting-edge projects addressing modern data challenges across multiple sectors. Professor Lachiche maintains an extensive service record in the scientific community, having organized numerous workshops and conferences including the MODA series (2020-2024), PFIA 2023 (as Local chair), and various international workshops on data mining and machine learning. He has served on program committees for prestigious conferences such as AAAI (2018, 2020-2024), ECAI, ICDM, and SDM. Additionally, he has reviewed for top journals including Artificial Intelligence Journal, Machine Learning Journal, and IEEE Transactions. His teaching portfolio includes data science, data mining, and machine learning; big data technologies, business intelligence and data warehouses; advanced databases (OLAP, NoSQL, GIS); and foundational courses on databases, data persistence, web programming, and user interfaces.

• Data Science
• Relational Data Mining
• Sequential/Temporal Data Mining
• +4 more

Kévin Bailly is a Lecturer at Sorbonne University, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and part of the Machine Learning and Artificial Intelligence (MLIA) team. His research focuses on computer vision, deep learning, and their applications in facial expression recognition, neural network optimization, and medical imaging. Dr. Bailly's research interests span multiple areas including: Computer Vision and Image Analysis Deep Learning and Neural Network Optimization Facial Expression and Action Unit Recognition Model Compression and Quantization Techniques Medical Applications of Artificial Intelligence His recent publications demonstrate a strong focus on neural network optimization, with particular emphasis on quantization, pruning, and compression techniques that maintain model performance while reducing computational requirements. His work spans both theoretical advancements in deep learning and practical applications in healthcare, human-computer interaction, and affective computing. He has developed novel approaches like PowerQuant for non-uniform quantization, RULe for real-time face alignment in degraded conditions, and RED++ for data-free pruning of deep neural networks. Dr. Bailly has published extensively in top-tier venues including ICLR, NeurIPS, IEEE TPAMI, and IEEE TAC, with a consistent output of high-impact research from 2022-2024. His work bridges theoretical computer vision with practical applications, particularly in medical diagnostics and human-computer interaction systems. He actively collaborates with researchers across multiple institutions, including Arnaud Dapogny, Edouard Yvinec, and Matthieu Cord, and has contributed to interdisciplinary projects that apply AI techniques to medical domains such as fracture classification and obstetrics.

• Computer Vision
• Facial Expression Recognition
• Deep Learning
• +4 more

Ronan Vicquelin is a University Professor (1st Class) at CentraleSupélec, Paris-Saclay University, affiliated with the EM2C Laboratory (CNRS). He serves as Head of the Department of Aeronautics, Space and Transport and co-supervises the High Performance Computing Mésocentre. His academic appointments include previous roles as University Professor (2nd class) and Head of Aerospace programs. Education includes Habilitation (University of Rouen Normandy, 2018), PhD in Energetics (École Centrale Paris, 2010), M.Sc. in Mechanical Engineering & Aerospace (École Centrale Paris, 2006), and Engineering Diploma (École Centrale Paris, 2006). Research focuses on turbulent reacting flows with emphasis on: numerical simulation of combustion systems, LES/DNS methodologies, uncertainty quantification, hydrogen combustion dynamics, conjugate heat transfer, and radiative energy transfer. Current investigations explore flame stabilization mechanisms, multi-physics coupling, and high-performance computing applications for aerospace propulsion systems. Publications predominantly address combustion science, with recent works (2021-2025) emphasizing hydrogen flame dynamics, NOx emission control, advanced numerical methods for reactive flows, and experimental validation of turbulent combustion models. Thermal radiation effects and multi-phase flow interactions constitute emerging themes. Advises multiple PhD candidates with projects funded by ANR, EU programs (ACHIEVE, SOPRANO), and industry partnerships (Safran, Air Liquide). Research grants include PEPR OXY3C, ANR HyMaX, and ANR OXYTEC focusing on zero-emission combustion technologies. Leads experimental and computational research at EM2C Laboratory, coordinating teams working on turbulent combustion diagnostics, high-fidelity simulations, and development of the Mésocentre HPC infrastructure for large-scale CFD.

• Turbulent Combustion
• Numerical Simulation
• Heat Transfer
• +3 more

Souvik Paul is an Assistant Professor in the School of Physics at Indian Institute of Science Education and Research Thiruvananthapuram, where he leads the Computational Materials Science (CMS) Laboratory established in 2023. His research employs advanced computational techniques to investigate fundamental properties of magnetic materials and topological phenomena. Education: Ph.D. (2015), Indian Institute of Technology Guwahati, India M.Sc. (2008), Presidency College, Kolkata, India B.Sc. (2006), University of Calcutta, India Dr. Paul's research focuses on computational materials science with particular emphasis on magnetism in two and three dimensions, topological magnetic quasiparticles like skyrmions, surface physics, strongly correlated systems, and multifunctional materials including Heusler alloys. His work primarily utilizes Density Functional Theory (DFT) to predict and explain material properties at the atomic scale, bridging computational predictions with experimental observations through international collaborations. His publication record reveals a consistent trajectory in magnetic skyrmions research, transition metal systems, and Heusler alloys, with significant contributions to understanding spin interactions, stability mechanisms, and electronic properties. His work frequently appears in high-impact journals including Physical Review Letters, Nature Communications, and npj Computational Materials, demonstrating both theoretical depth and practical relevance to materials design. Scientific Awards: Prime Minister Early Career Research Grant (2025) from Anusandhan National Research Foundation Departmental Postdoctoral Fellowship (2015), Uppsala University Doctoral fellowship (2009), IIT Guwahati Graduate Aptitude Test in Engineering (GATE) (2009), MHRD, India Dr. Paul actively mentors graduate students including Moinak Ghosh and Bipin Babu. Through the International PhD Program, he has established formal collaborations with Prof. Stefan Heinze at CAU Kiel, Germany, providing students with international research opportunities, access to high-performance computing facilities, and extended research stays at partner institutions. His recently awarded Prime Minister Early Career Research Grant supports innovative work on antiferromagnetic skyrmions. The Computational Materials Science Laboratory employs Density Functional Theory to investigate structural, electronic, magnetic, and optical properties of materials at the atomic level. The lab maintains strong international collaborations with research groups at CAU Kiel and Forschungszentrum Jülich in Germany, focusing on discovering novel materials, explaining fundamental material behaviors, and developing predictive materials theory with applications in electronics and energy technologies.

• Magnetism in two and three dimensions
• Topological magnetic quasiparticles
• Surface physics
• +5 more

Tej Chajed is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, focusing on formal verification of systems software. His research bridges theoretical foundations and practical implementations to ensure software correctness in concurrent and crash-safe systems. Research interests include formal verification, concurrency, crash safety, and programming languages, particularly using Coq, Perennial, and Goose frameworks. He has contributed to systems like DaisyNFS, a verified file system with sequential reasoning, and Verus, a foundation for systems verification. His work appears in top venues like SOSP, OSDI, and PLDI. 2025: Dafny PC Member 2024: PLDI Committee Member, CoqPL Co-chair 2023: CoqPL Co-chair, POPL Program Committee He actively mentors students and develops tools for systems verification education, including extensive Coq-based course materials.

• Formal Verification
• Systems Software
• Programming Languages
• +4 more

Christine Jeoffrion is a Professor of Occupational Psychology at Université Grenoble Alpes, where she directs the Doctoral School of Human Sciences and leads the "Risk and Change Accompaniment" axis at the Inter-university Psychology Laboratory. With extensive international engagement through organizations like EAOHP and IAAP, her work bridges occupational health, organizational psychology, and psychosocial risk prevention. Key affiliations: Université Grenoble Alpes, Université de Tours, INRS, CNAM Leadership roles: Doctoral School Director (2024+), CNU member (2024-2028), HCERES expert (2023+) Her research focuses on workplace psychological and physical health, examining organizational factors affecting well-being through 15+ funded projects like ANR PacBot (€670k) and CDP BOOT (€850k). Current PhD students explore virtual reality interventions, prison worker coping mechanisms, and organizational transformations for meaningful work. Recent publications analyze cobot impacts on work quality, telework dynamics, and bullying consequences. She has co-edited special issues for International Journal of Environmental Research and Public Health and Psychologie du travail et des Organisations . Her 2023 Légion d'honneur recognition underscores her field leadership. As a thesis advisor, she has directed 34+ doctoral candidates across France and Gabon, including works on intercultural competence, healthcare worker stress, and managerial innovation. She contributes to European research networks through symposium organization and journal editorial boards.

• Occupational Health Psychology
• Organizational Psychology
• Psychosocial Risk Prevention
• +5 more