Khadija Bousselmi Ep ARFAOUI is a Lecturer at Savoie Mont Blanc University and a researcher at the LISTIC laboratory. She holds a PhD in Computer Science from the University of Tunis El Manar and completed a postdoctoral fellowship at LAMSADE, Paris Dauphine. Her research focuses on optimizing data-intensive systems, including cloud-based workflow scheduling, Big Data architecture, and energy-efficient CNN design. She teaches modules like Optimization Methods, Networks, and Graph Theory at the IUT Annecy campus. PhD: Approche scalable pour l’ordonnancement des workflows scientifiques dans un environnement Cloud , 2017 Postdoc: Multi-Objective Cloud Workflow Scheduling , 2019-2020 (LAMSADE) Research Interests: Parallel computing, distributed systems, machine learning applications in data warehousing and disaster prediction, and green computing strategies. Notable contributions include frameworks like DR-SWDF for dynamic workflow deployment and decision support systems for Big Data pipelines. Recent work emphasizes ML in healthcare (speech disorder diagnosis) and environmental analytics (avalanche forecasting).
Dmitry Berenson is an Associate Professor in the Robotics Department at the University of Michigan, Ann Arbor. Previously, he served as an Assistant Professor at Worcester Polytechnic Institute from 2012 to 2016, following a postdoctoral position at the University of California, Berkeley in 2011-2012. His educational background includes: B.S. in electrical engineering from Cornell University (2005) Ph.D. in robotics from the Robotics Institute, Carnegie Mellon University (2011) Dr. Berenson's research focuses on advancing robotic manipulation capabilities, particularly in challenging scenarios involving deformable objects and complex environments. His work bridges the gap between theoretical motion planning algorithms and practical robotic applications, with emphasis on developing methods that can handle uncertainty, occlusion, and complex physical interactions. He has made significant contributions to contact-rich manipulation, deformable object handling, and the integration of learning with classical planning approaches. His research has important applications in manufacturing, agriculture, surgery, and everyday human environments where robots need to interact with complex objects. His recent publications demonstrate a strong trend toward integrating machine learning techniques (particularly diffusion models and other deep learning approaches) with traditional motion planning and manipulation methods. This hybrid approach allows for more robust handling of complex scenarios like deformable object manipulation, occluded environments, and long-horizon tasks that have traditionally challenged robotics systems. Among his notable achievements, Dr. Berenson has received the prestigious IEEE RAS Early Career Award and the NSF CAREER award, recognizing his significant contributions to robotics research and education. As an academic advisor, Dr. Berenson has mentored numerous students who have gone on to contribute to the field of robotics. His research has been supported by various grants that have enabled his team to pursue innovative approaches at the intersection of motion planning, manipulation, and machine learning. His work often involves interdisciplinary collaboration across computer science, mechanical engineering, and applied mathematics. Dr. Berenson leads a research group at the University of Michigan focused on robotic manipulation and motion planning. His lab develops algorithms that enable robots to perform complex manipulation tasks in unstructured environments, with particular expertise in handling deformable objects and reasoning about physical interactions. The group maintains strong connections with industry partners and other academic institutions, fostering a collaborative environment for advancing the state of the art in robotics.
Emmanuel MELIN is a Lecturer at the University of Orléans, affiliated with the PaMDA team at LIFO (Laboratory of Fundamental Computer Science of Orléans). He teaches primarily at the IUT (University Institute of Technology) of Computer Science in Orléans. His research focuses on parallel computing, virtual reality, and scientific visualization using PC clusters and distributed architectures. Research Interests: His work spans parallel algorithms for geospatial processing, high-performance computing for environmental simulations, and distributed virtual reality systems. He has contributed to large-scale VR frameworks and open-source software like FlowVR, NetJuggler, and SoftGenLock. Publications: Dr. MELIN has authored 31 HAL publications, with 15 most recent covering GPU-accelerated finite element methods, tele-immersive grids, distributed application mapping, and geospatial data processing. His work intersects computer science, environmental modeling, and 3D rendering. Labs & Collaborations: He collaborates with INRIA and has participated in projects like RNTL Geobench, ANR DALIA, and Extengis. His software tools are available as open-source projects.
Caio Corro is a Maître de Conférences (Associate Professor) at INSA Rennes, conducting research in the LinkMedia team of the IRISA laboratory. His work bridges theoretical computer science with practical applications in natural language understanding, with particular expertise in structured prediction problems for NLP systems. Dr. Corro's research program spans multiple interconnected domains: Natural Language Processing, with special focus on named entity recognition (including nested and discontinuous entities) Machine Learning algorithms for linguistic structure modeling Combinatorial optimization approaches to NLP challenges Application of AI techniques to linguistic research (AI for science) Development of efficient algorithms balancing theoretical soundness with practical performance His recent publication trajectory shows increasing emphasis on domain adaptation and multilingual capabilities while maintaining rigorous theoretical foundations. Corro has pioneered approaches that reduce computational complexity in structured prediction tasks without sacrificing accuracy, with significant contributions to named entity recognition methodologies. His work on EuroBERT demonstrates commitment to European language technologies, while his insurance domain research shows practical application focus. Dr. Corro maintains an active supervision portfolio: Current PhD students: Ayoub Hammal (co-supervised with Pierre Zweigenbaum and Miguel Couceiro), Benno Uthayasooriyar (co-supervised with Franck Vermet and Antoine Ly) Former students include Alban Petit (PhD graduate), Nicolas Devatine (now PhD student at IRIT), and several master's students who have contributed to his research program As a core member of the LinkMedia team at IRISA, Dr. Corro collaborates across institutional boundaries on cutting-edge NLP challenges, with particular emphasis on adapting techniques to specialized domains where data constraints present unique challenges.
Pascal Richard is a Full Professor specializing in Real-Time Systems at the Institute of Technology, University of Poitiers. He is affiliated with the LIAS (Laboratoire d'Ingénierie des Applications de la Résolution des Systèmes) laboratory, which has locations at both ENSIP (École Nationale Supérieure d'Ingénieurs de Poitiers) in Poitiers and ISAE-ENSMA (Institut Supérieur de l'Aéronautique et de l'Espace - École Nationale Supérieure de Mécanique et d'Aérotechnique) in Chasseneuil. His research spans over two decades with continuous publication from 1999 through 2023. Professor Richard's primary research interests focus on real-time scheduling theory, embedded systems, and avionics. His work particularly emphasizes AFDX networks , cache-related preemption delays , self-suspending tasks , and worst-case response time analysis . His research bridges theoretical foundations with practical applications in safety-critical systems, particularly in the aerospace domain. He has made significant contributions to the understanding of scheduling anomalies, feasibility analysis, and the development of approximation schemes for complex real-time problems. His publication record demonstrates strong collaboration with researchers across France and internationally, with consistent contributions to major real-time systems conferences including RTSS, ECRTS, and RTNS. His work shows an evolution from foundational scheduling theory toward increasingly complex systems including multiprocessor platforms, mixed criticality systems, and integrated modular avionics architectures. Professor Richard has contributed to the real-time community through numerous journal publications in Real-Time Systems , IEEE Transactions on Computers , and IEEE Transactions on Industrial Informatics , among others. His most recent work continues to address cutting-edge challenges in real-time scheduling for modern computing platforms.
Wissam Antoun is a PhD Researcher at ALMAnaCH, a research team within INRIA (Institut National de Recherche en Informatique et en Automatique) in Paris. Specializing in Natural Language Processing with a focus on Arabic and French language models, he has developed several influential models including AraBERT (the first Arabic BERT), AraGPT2 (the first Arabic LLM), and CamemBERTa (a French language model based on DeBERTa V3). Prior to his current position, he served as a Research Engineer at ALMAnaCH, a Senior Machine Learning Engineer at Siren Analytics in Beirut, and co-founded the Machine INtelligence Development (MIND) Lab at the American University of Beirut. Wissam's research focuses on developing state-of-the-art NLP technologies for languages displaying high variability, particularly Arabic dialects used on social media. His work spans multilingual language modeling, tokenization techniques for morphologically rich languages, and the development of comprehensive language model suites. Recent projects include Gaperon (a French LLM suite with 1.5B, 8B, and 24B parameters), ModernCamemBERT (the first non-English ModernBERT model), and pioneering work on detecting French AI-generated text. His research demonstrates expertise in model training, evaluation, and practical implementation for real-world NLP applications. His publication record shows consistent high-impact contributions, with AraBERT becoming the most cited Arabic AI paper and most starred Arabic GitHub repository, with over 10 million downloads on Hugging Face. His work has been published at major venues including Findings of ACL 2023 and preprints on arXiv. The trends in his recent articles show a progression from foundational Arabic language models to more sophisticated French language modeling and analysis of AI-generated content. Wissam has received multiple prestigious awards including First Place in the Arabic Sentiment Analysis competition at KAUST (2021) and Second Place in the OSACT4 Shared task on Offensive Language Detection (2020). His technical capabilities span the full AI stack from research to deployment, with expertise in major frameworks, software tools, and programming languages. As an educator, Wissam has served as a Graduate Teaching Assistant at the American University of Beirut, teaching courses in Software Tools, Parallel Programming, and Data Structures and Algorithms. He has also provided NLP instruction through workshop series and supported contestants in the Stars of Science program. His lab work centers around the ALMAnaCH research team at INRIA, where he contributes to advancing French language modeling capabilities through active development on GitHub repositories.
Luiz Faria is a research scientist at ENSTA Paris , affiliated with the Applied Mathematics Unit (UMA) and the POEMS (Propagation des Ondes : Étude Mathématique et Simulation) research team at INRIA Saclay. His work focuses on numerical methods for partial differential equations , integral equations , and scientific computing , with applications in wave propagation , electromagnetic scattering , and detonation theory . Ph.D. in Applied Mathematics and Computational Sciences, KAUST (2015) M.S. in Applied Mathematics and Computational Sciences, KAUST (2011) B.S. in Mathematics, Texas A&M University (2010) His research spans: Wave Propagation : Advanced boundary integral equation methods for electromagnetic and water wave scattering, including complex-scaled formulations and field-only approaches Detonation Theory : Asymptotic analysis of detonation instabilities, set-valued solutions, and thermochemical loss modeling Numerical Methods : High-order quadrature techniques, polynomial density interpolation, and convolution quadrature algorithms Recent publications highlight: Development of field-only boundary integral equations for electromagnetic scattering in spherical geometries Efficient polynomial interpolation methods for singular volume potential evaluation Studies on nonlinear elliptic equations with variable exponents and convection terms He contributes to open-source software like DataFlowTasks.jl , and applies his methodologies to problems in inhomogeneous scattering and reactive media dynamics . His work bridges theoretical mathematics with industrial applications in energy , transportation , and defense technologies .
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 .
Jérémie Gaidamour is a CNRS Research Engineer at the Institut Élie Cartan de Lorraine (University of Lorraine), specializing in high-order numerical methods and high-performance computing (HPC) for quantum mechanics simulations. His work focuses on parallel solvers for large sparse linear systems, including hybrid direct-iterative methods and algebraic multigrid (AMG). PhD Thesis: "Conception d'un solveur linéaire creux parallèle hybride direct-itératif" (2009, Université de Bordeaux I) His research interests span Quantum Mechanics (Bose-Einstein condensates via Gross-Pitaevskii equations), Numerical Methods (pseudo-spectral techniques, domain decomposition), and High-Performance Computing (MPI, parallel algorithms). Recent publications highlight advancements in fractional gradient flows and energy-minimizing AMG preconditioners for nonlinear Schrödinger equations. Software development projects include BEC2HPC (parallel spectral methods for BECs), MueLu (AMG solver in Trilinos), and HIPS (hybrid direct-iterative sparse solver). He has contributed tutorials on HPC tools (MPI, OpenMP, GPU/Xeon Phi) and participated in Grid'5000/IDRIS support teams.
Sonia Ikken Benali is a Researcher-Lecturer at CESI (Lille, France), affiliated with the Engineering and Numerical Tools research team. Her work focuses on Big Data, Machine Learning, Distributed Systems, Business Intelligence, and Optimization Problems. She teaches courses in Data Storage/Processing, Business Intelligence, and Operations Research at the Integrated Preparatory Program and Engineering Program levels. Education : Ph.D. in Efficient Placement Design for Big Data in Clouds (Télécom SudParis - Institut Mines-Télécom - Pierre and Marie Curie University, 2018) M.Sc. in Networks and Distributed Systems (Abderrahmane Mira University, Algeria, 2011) State Engineer in Advanced Information Systems (Abderrahmane Mira University, 2010) Her research emphasizes optimizing cloud storage efficiency, cost reduction in distributed systems, and scheduling algorithms for big data workflows. Key contributions include collaborative cloud storage frameworks and Markov model-based I/O scheduling for MapReduce tasks. Lab/Team : Part of the Engineering and Numerical Tools research group at CESI, focusing on practical applications of advanced computational methods.
Nikita Koval is a Researcher at JetBrains , specializing in concurrent programming, Kotlin coroutines, and testing frameworks. His work bridges academic research with industrial application, focusing on synchronization primitives, lock-free data structures, and JVM-based concurrency tools. Academic rank: Researcher Affiliation: JetBrains Research interests include: Concurrent algorithm design and optimization Formal verification of synchronization mechanisms Testing frameworks for JVM-based languages Memory-efficient lock-free data structures Scalability in coroutine communication Distributed systems debugging Publication trends show a focus on Kotlin coroutines, JVM concurrency, and testing frameworks like Lincheck. His work spans algorithm design (union-find, queues), formal verification (CQS), and practical tools for concurrency testing (Lincheck) across conferences like PPoPP, PLDI, and ECOOP. Scientific recognition: Nikita received the Best Paper Award at OPODIS 2019 for his work on concurrent union-find algorithms. His contributions to Lincheck and CQS have been adopted into Kotlin's core libraries. Workshop leadership: He offers a 4-day corporate workshop on concurrent algorithms, covering testing, debugging, and implementation of lock-free data structures, now booked for Q3/Q4 2025. The curriculum includes hands-on Java/Kotlin coding with topics like segment queues, fetch-and-add queues, flat combining, and concurrent hash tables.
Oded Padon is a Senior Scientist (≈Assistant Professor) at the Weizmann Institute of Science , affiliated with the Faculty of Mathematics and Computer Science . He joined Weizmann in September 2024 after prior roles as a researcher at VMware Research Group , a postdoc in Alex Aiken's group at Stanford University , and a PhD student at Tel Aviv University under Mooly Sagiv . Research Interests : Oded's work focuses on developing principled algorithms for automated verification of complex systems, particularly distributed protocols, storage systems, and cluster management. He emphasizes decidable logics, primal-dual methods, and decomposition techniques. His recent interests include verification of deep neural networks , quantum computing , and leveraging large language models for verification tasks. Key projects: Ivy (safety/liveness verification), mypyvy (invariant inference), Verus (Rust verification), TASO (deep learning optimization), Quartz (quantum circuit superoptimizer). Scientific Awards : Azrieli Early Career Faculty Fellowship 2020 ETAPS Doctoral Dissertation Award 2017 Google PhD Fellowship in Programming Languages Radhia Cousot Young Researcher Best Paper Award (SAS 2017) Publications span decidable verification, invariant inference, quantum optimization, and machine learning for verification. His work has received Jay Lepreau Best Paper Award at SOSP 2024 (Anvil) and Distinguished Paper Awards at POPL 2024 (An Infinite Needle) and OOPSLA 2023 (Leaf).
KC Sivaramakrishnan is an Assistant Professor at Indian Institute of Technology Madras and concurrently serves as CTO of Tarides. He works at the intersection of programming languages and systems, focusing on concurrency, distributed systems, and OCaml runtime development. Primary Affiliation: Indian Institute of Technology Madras Co-founder: Tarides His research explores: Concurrency and parallelism in OCaml Effect handlers for modular programming Mergeable Replicated Data Types for distributed systems Weak memory models and consistency guarantees Lock-free algorithms and safe multicore programming Recent publications focus on OCaml 5.0's concurrency features, effect handler integration, and verified CRDT implementations. He contributes to compiler design, runtime optimization, and testing frameworks for multicore systems. Service roles include committee memberships in SPLASH, ICFP, OCaml, and PROPL conferences. He actively bridges functional programming with systems research through practical implementations.
Daniel W. Barowy is an Associate Professor in the Department of Computer Science at Williams College . His research focuses on programming languages , particularly end-user programming , crowdsourcing , and spreadsheet debugging . He integrates program analysis with statistical techniques to improve software usability and robustness. Current affiliations: Williams College (2017-present) Education: University of Massachusetts Amherst (PhD, 2017) His research explores language abstractions for human-computer integration , with notable projects like ExceLint (spreadsheet error detection), FlashRelate (spreadsheet data extraction), and Riker (incremental build systems). He emphasizes artifact verification , securing PLDI 2015 Distinguished Artifact Award and USENIX ATC 2022 Best Paper . Recent publications demonstrate trends in spreadsheet reliability , crowdsourcing frameworks , and scalable educational tools . He has received "Artifact Verified" badges for multiple projects and actively contributes to software tool development , including AutoMan , ExceLint , and CheckCell . USENIX ATC 2022 Best Paper PLDI 2015 Distinguished Artifact Award Multiple "Artifact Verified" badges As an educator, he teaches CSCI 334: Principles of Programming Languages and CSCI 331: Computer Security . His work bridges theoretical PL research with practical applications in spreadsheet programming and crowdsourcing platforms .
Virginie Galtier is a Researcher at the Laboratoire Lorrain de Recherche en Informatique et ses Applications (LLLIA), affiliated with the University of Lorraine. Her work focuses on distributed computing systems, fault-tolerant architectures, and co-simulation methodologies. She has contributed to projects involving grid computing, UAV swarm systems, and hybrid co-simulation frameworks such as DACCOSIM and MECSYCO. Her research integrates interdisciplinary approaches combining computer science with fields like financial engineering, robotics, and energy systems. Key technical contributions include: Development of fault-tolerant distributed frameworks using JavaSpace Co-simulation solutions for FMI-compliant systems Resource prediction in heterogeneous active networks Recent work emphasizes application-driven research in cyber-physical systems (CPS), particularly using UAV swarms for safety-critical applications and smart space heating modeling. Galtier collaborates extensively with academia and industry on projects requiring distributed simulation platforms and parallel processing techniques. Her technical contributions span: Parallel algorithms for large-scale grid computing Middleware design for reconfigurable distributed systems Optimization of resource utilization in heterogeneous environments She actively participates in conference proceedings and maintains close ties with the MECSYCO and DACCOSIM co-simulation ecosystems. Current research directions include advancing hybrid co-simulation interoperability and exploring adaptive frameworks for dynamic computing environments.
