جستجوی استاد راهنمای دانشگاهی

Zeina ELRAWASHDEHمشاهده پروفایل

پژوهشگر

• Measurements and Controls
• Fiber-Optic Sensors
• Artificial Intelligence of Things (AIoT)
• +۳ مورد دیگر

Zeina ELRAWASHDEH is a Researcher Lecturer at the Institut Catholique d'Arts et Métiers (ICAM), based at the Grand Paris Sud campus. Her research focuses on Measurements and Controls, with a particular emphasis on fiber-optic sensors, multi-agent systems, and IoT integration for smart infrastructure. She collaborates with prestigious research laboratories globally to develop innovative solutions in energy optimization, smart cities, and precision engineering. Her expertise spans applied research in fiber-optic displacement sensors, algorithm optimization for sensor performance, and user-centric building automation systems. Zeina’s work bridges theoretical advancements with practical applications in manufacturing, energy, and urban systems. She actively contributes to international academic discourse through peer-reviewed publications and participates in ICAM’s strong industry partnerships for applied research outcomes. Zeina’s research portfolio demonstrates a trajectory toward integrating AI and IoT with traditional engineering challenges, addressing technical gaps in sensor networks, multi-agent coordination, and precision machining. Her recent work highlights advancements in smart city infrastructure and energy-efficient building systems. While no awards are explicitly mentioned, her involvement in ICAM’s research initiatives underscores her commitment to impactful, industry-relevant science. Collaborations with global companies and academic institutions position her at the forefront of applied engineering research.

Maryam Mehri Dehnaviمشاهده پروفایل

دانشیار

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +۳ مورد دیگر

Maryam Mehri Dehnavi is an Associate Professor in the Department of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing and leads the ParaMathics research group. Research focuses on high-performance computing , machine learning , sparse matrix optimizations , and compiler design for heterogeneous systems. Her work develops domain-specific languages , scalable numerical libraries , and auto-vectorization techniques for cloud and GPU platforms. Recent publications address LLM compression , sparse code translation , GPU kernel synchronization , and control flow optimization . Scientific recognition: Ontario Early Researcher Award (2021), NSF CRII Grant, NSERC New Frontiers in Research Fund. Current students: Mushegh Shahinyan , Martin Phan , Maryam Haghifam , and others. Former advisees: Kazem Cheshmi (NJIT), Zachary Blanco (MIT Lincoln Lab), Yuanxi Li (Amazon).

• Programming Languages
• Formal Verification
• Compilers
• +۵ مورد دیگر

Clément Pit-Claudel is an Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the SYSTEMF lab focused on programming languages, formal methods, and systems engineering. His work bridges mathematical formalisms with practical system development to achieve full assurance in critical software and hardware. PhD in Computer Science from MIT (2016) William A. Martin Memorial Thesis Award recipient Former Senior Applied Scientist at Amazon AWS Teaching accolades including the Frederick C. Hennie III Teaching Award Research spans three axes: extensible proof-producing compilers for performance-critical systems, verified hardware compilation with cycle-accurate semantics, and interactive theorem prover tooling for democratizing verification technology. Key projects include Kôika for hardware verification, Alectryon for Coq proof visualization, and Fiat for correct-by-construction program synthesis. Recent publications address JavaScript regex verification (ICFP 2024), cryptographic server integration (PLDI 2024), and hardware simulation optimization (ASPLOS 2021). Articles demonstrate expertise in functional-to-imperative translation, domain-specific compiler extensions, and hardware-software co-verification. Scientific contributions recognized through: Distinguished artifact award (SLE 2020) MIT William A. Martin Thesis Award Frederick C. Hennie III Teaching Award Teaching philosophy emphasizes hands-on lab instruction , oral assessment , and automated tooling . Courses taught include Software Construction (undergraduate) and Interactive Theorem Proving (graduate) at EPFL. Research service includes program committee roles at Dafny, POPL, and SPLASH conferences.

Mathieu BACOUمشاهده پروفایل

مدرس

• Cloud Computing
• Distributed Systems
• Performance Optimization
• +۳ مورد دیگر

Mathieu BACOU is a Lecturer at Telecom SudParis, affiliated with the SAMOVAR research laboratory. His work focuses on cloud computing, distributed systems, and performance optimization, with particular emphasis on virtualization, data center management, and computer architecture challenges. He holds a PhD from the National Polytechnic Institute of Toulouse, where his thesis addressed resource management in multi-virtualized cloud environments. Education: PhD in Networks and Telecommunications, National Polytechnic Institute of Toulouse (2020) Research Interests: BACOU explores scalable systems, including function-as-a-service (FaaS), nested virtualization, and energy-efficient data center designs. Recent work addresses memory architecture limitations (e.g., 128-bit addressing) and containerization transparency. His projects often involve collaboration with industry partners to bridge academic research and practical deployment. Key Contributions: His work on Drowsy-DC introduced smartphone-inspired power management for data centers, while OFC caching systems improved FaaS efficiency. Recent papers like FaaSLoad and 128-bit address extensions highlight his focus on future computing challenges. Labs & Affiliations: Active member of SAMOVAR lab, Telecom SudParis, and collaborator with RISC-V Summit and EUROSYS conferences. Engaged in experimental practices bridging systems research and real-world infrastructure.

Alexey Gotsmanمشاهده پروفایل

استاد پژوهش

• Distributed Systems
• Software Verification
• Consensus Algorithms
• +۵ مورد دیگر

Alexey Gotsman is a tenured Research Professor at the IMDEA Software Institute in Madrid, Spain, where he leads research at the intersection of software verification and distributed computing. He also works part-time as a visiting academic at Amazon Web Services. His educational background includes a Ph.D. from the University of Cambridge, where he was previously a postdoctoral fellow. His research spans theoretical foundations and practical implementations of distributed systems, with particular emphasis on fault tolerance, consensus algorithms, and consistency models. His research interests focus on the theoretical and practical aspects of distributed systems, including Byzantine fault tolerance, state machine replication, distributed transactions, and consistency models. His work bridges the gap between formal verification techniques and real-world distributed systems implementations, with numerous publications in top-tier venues like PODC, DISC, and NSDI. His recent publications show a clear progression from theoretical foundations to practical implementations, with increasing focus on real-world applications, performance considerations, and fault tolerance in large-scale distributed systems. The research demonstrates strong connections between formal methods and practical system design, particularly in the areas of consensus protocols, replication strategies, and transaction processing. Best paper award at OPODIS'23 Best paper award at DISC'18 Best paper award at CONCUR'12 EAPLS Best Dissertation Award Runner-up prize in the BCS Distinguished Dissertation Competition ERC Starting Grant 'RACCOON: A Rigorous Approach to Consistency in Cloud Databases' (2017-2022) Ramón y Cajal Fellowship (2017-2022) Alexey actively mentors PhD students including Alejandro Naser Pastoriza, Fedor Ryabinin, and Antonio José Fernández Pinto, while his alumni have gone on to faculty positions at institutions like University of Paris 7 and University of Colorado Boulder, as well as industry roles at companies like ARM and Informal Systems. He has received significant funding including an ERC Starting Grant and served on program committees for numerous top conferences including POPL, DISC, and PODC. He currently co-organizes the International Symposium on Distributed Computing (DISC) and has been instrumental in organizing workshops focused on consistency in distributed systems. His research group at IMDEA Software Institute focuses on developing rigorous approaches to consistency in cloud databases and distributed systems, with active projects spanning theoretical analysis, protocol design, and practical implementation of fault-tolerant distributed systems.

Tarik Grabaمشاهده پروفایل

مدرس

• Hardware Security
• Embedded Systems Design
• Integrated Circuit Reliability
• +۳ مورد دیگر

Tarik Graba is a Lecturer at Télécom Paris and a member of the Secure and Safe Hardware (SSH) research team within the Information Processing and Communication Laboratory (LTCI) department. His work focuses on hardware security, embedded systems, and cryptographic implementation. Education: Not explicitly mentioned Current Affiliations: Télécom Paris (Faculty), LTCI, SSH Team His research explores designing secure and reliable integrated systems , with emphasis on managing complexity, power consumption, and flexibility in embedded environments. He contributes to side-channel analysis , lightweight cryptographic algorithms , and hardware-based security mechanisms . The 15 most recent publications highlight his work in lightweight cryptography , hardware security , and embedded system optimization , particularly focusing on RISC-V extensions , side-channel leakage , TRNG circuits , and secure cryptographic processors . Scientific Awards Academic Palms award, Télécom Paris (2021) Tarik Graba collaborates extensively in hardware security research and has advised multiple projects on physical security countermeasures and embedded cryptographic systems . He works with teams like the SSH Laboratory to develop secure hardware architectures.

Marc Snirمشاهده پروفایل

استاد

• Parallel Computing
• Computer Architecture
• High Performance Computing
• +۳ مورد دیگر

Marc Snir is an Israeli-American Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). He holds a Doctor Honoris Causa from École Normale Supérieure de Lyon (2018). His career includes leadership roles such as Head of the Computer Science Department at UIUC (2001–2007) and Director of the Mathematics and Computer Science Division at Argonne National Laboratory (2011–2016). He is a Fellow of AAAS, ACM, and IEEE, and recipient of the IEEE Seymour Cray Award. Education: PhD in Mathematics from the Hebrew University of Jerusalem (1979). Research focuses on parallel algorithms, high-performance computing (HPC), distributed systems, and fault tolerance. He co-developed the MPI standard for parallel programming and contributed to IBM's Blue Gene supercomputer architecture. His Erdős number is 2, reflecting collaborative links to mathematician Paul Erdős. Awards include the IEEE Award for Excellence in Scalable Computing and leadership in advancing supercomputing through publications like The Future of Supercomputing (2004).

• Wave Propagation
• Computational Mathematics
• Numerical Analysis
• +۶ مورد دیگر

Vanessa Mattesi is a Researcher affiliated with the Inria Team MAGIQUE-3D and CERFACS . She holds a Ph.D. in Applied Mathematics from the University of Pau and the Adour Region (2011-2014), advised by Sébastien Tordeux. Her postdoctoral research focuses on multiscale modeling of wave propagation in complex media, with emphasis on numerical methods like Trefftz discontinuous Galerkin and boundary element approaches. Education: Ph.D. in Applied Mathematics (2011-2014), University of Pau M.Sc. in Mathematical Engineering (2011), University of Toulouse III Research Interests: Her work centers on wave propagation phenomena, particularly in heterogeneous media. Key areas include: High-order numerical methods for Helmholtz and acoustic equations Equivalent source modeling of small heterogeneities Domain decomposition and absorbing boundary conditions Her methods integrate discontinuous Galerkin formulations with boundary element techniques for enhanced accuracy in complex geometries. Grants & Activities: Member of Inria Bordeaux-Sud Ouest committee. Active contributor to conferences such as ACOMEN (2017), EAGE (2016), and Waves (2013). Labs/Teams: MAGIQUE-3D project team (Inria) and CERFACS collaborative research groups.

Hanjun Kimمشاهده پروفایل

پژوهشگر

• Compiler Design
• Machine Learning
• Parallel Computing
• +۳ مورد دیگر

Hanjun Kim is a researcher at Yonsei University, focusing on compiler design, machine learning optimization, and hardware-aware programming techniques. His work bridges theoretical research with practical implementations in embedded systems and security domains. Research Interests Compiler-driven optimization for PIM (Processing-in-Memory) architectures Homomorphic encryption compiler design Parallel computing for DNN/LLM inference Network function program analysis Recent research trends include: application of compiler techniques to optimize resource utilization in heterogeneous computing environments, particularly for AI workloads and secure computation. His publications demonstrate expertise in tackling performance bottlenecks through architectural and compiler co-design. Conference Service 2025 SPLASH OOPSLA Review Committee 2025 LCTES Program Committee 2024 CGO Program Committee 2023 LCTES Program Committee 2022 CGO Organization Committee 2020 LCTES Program Committee

Adlen KSENTINIمشاهده پروفایل

استاد

• Mobile and Wireless Networks
• Software Defined Networking (SDN)
• Mobile Edge Computing (MEC)
• +۷ مورد دیگر

Adlen KSENTINI is a Professor at EURECOM's Communication Systems department, specializing in advanced networking technologies. His research focuses on Mobile and Wireless Networks, Software Defined Networking (SDN), Mobile Edge Computing (MEC), Network Function Virtualization (NFV), and Content Delivery Networks (CDN), with an emphasis on performance evaluation and network virtualization. He has contributed to projects like AC3 and 6G-BRICKS, exploring cloud-edge continuum integration and 6G infrastructure. Key research interests include virtualized mobile core networks, carrier cloud systems, and AI-driven network management. He has received Best Paper Awards at IEEE WCNC 2018 and IWCMC 2016 for works on network slicing and LTE modeling accuracy. His work often integrates machine learning for optimization, sustainability, and security in 5G/6G networks. Distinctions: Two Best Paper Awards Labs/Teams: Involved in EU-funded projects like AC3 and 6G-BRICKS Grants: Not explicitly listed, but active in collaborative research initiatives

Samuel Thibaultمشاهده پروفایل

استاد

• Parallel Computing
• High Performance Computing
• Task-Based Runtime Systems
• +۳ مورد دیگر

Samuel Thibault is a Professor at University of Bordeaux, affiliated with Laboratoire Bordelais de Recherche en Informatique (LaBRI) and Inria Bordeaux -- Sud-Ouest. He is a member of the SATANAS team at LaBRI (theme: High Performance Runtime Systems for Parallel Architectures) and the STORM research team at Inria Bordeaux (previously RunTime). His work bridges academic research and practical implementation of high-performance computing systems. Thibault's research focuses on task-based runtime systems, particularly StarPU, for heterogeneous and parallel computing architectures. His work addresses critical challenges in scheduling algorithms, memory management under constraints, data locality optimization, and performance modeling for complex NUMA architectures. He has made significant contributions to the field of parallel computing through the development and analysis of runtime systems that efficiently manage tasks across diverse hardware resources including CPUs, GPUs, and other accelerators. His research has practical applications in scientific computing, deep learning inference, and large-scale simulations requiring extreme computing power. His recent publication trends show a strong emphasis on optimizing task-based runtime systems for heterogeneous architectures with particular attention to memory constraints and data locality. There's a clear progression toward applying these techniques to deep learning inference workloads, as evidenced by his StarONNX project. His work consistently addresses the challenge of balancing throughput and latency in complex computing environments, with increasing focus on recursive task graphs and dynamic adaptation strategies. Thibault is actively involved in European research initiatives including TEXTAROSSA (focusing on exascale technologies) and EXA2PRO (high development productivity on heterogeneous systems). His work has been published consistently in top-tier conferences and journals in parallel and distributed computing. As part of the STORM team at Inria Bordeaux, Thibault contributes to advancing the state of the art in runtime systems for high-performance computing. His team's work on StarPU has become a reference implementation in the field, enabling researchers and practitioners to develop applications that can efficiently utilize heterogeneous computing resources without needing to manage the complexity of different hardware architectures directly.

François Trahayمشاهده پروفایل

استاد

• High Performance Computing
• Runtime Systems
• Performance Analysis
• +۴ مورد دیگر

François Trahay is a Full Professor in the Computer Science department at Télécom SudParis (Institut Mines-Télécom) and a member of the Benagil Inria team. He leads research in high-performance systems, runtime systems, and performance analysis for HPC and distributed systems. He holds an HDR from Institut Polytechnique de Paris and a PhD from University of Bordeaux (2009). His work includes the EZTrace framework for performance analysis and contributions to storage systems optimization. Education: 2021: Habilitation à Diriger des Recherches (HDR), Institut Polytechnique de Paris 2010: PostDoc at Riken, University of Tokyo 2009: PhD in Computer Science, University of Bordeaux 2006: MS in Computer Science, University of Bordeaux Research focuses on runtime system design, HPC trace analysis, and storage efficiency. Recent projects include PALLAS trace format (IPDPS 2025) and GPU performance prediction (Euro-Par 2024). He advises 4 current PhD students and has supervised 2 former students now in industry. Key contributions include: Co-developer of EZTrace performance analysis framework Co-author of 60+ peer-reviewed papers in top venues (IPDPS, IEEE Cluster, ICPP) Technical leadership in Inria's Benagil team and Samovar Lab

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.

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +۴ مورد دیگر

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.

• Additive Manufacturing
• Materials Science
• Metallurgy
• +۲۲ مورد دیگر

Jean-Daniel Penot is a Researcher at CESI's Research and Innovation Department , with expertise in additive manufacturing, materials science, and industrial integration. His work bridges advanced manufacturing technologies with environmental sustainability and educational innovation. Doctorate in Materials Physics (2010) Engineering Degree in Physics (2007) Research Master in Optoelectronics (2007) Penot's research spans Additive Manufacturing and its applications in automotive, nuclear, and construction sectors. He focuses on Laser-Material Interaction , Machine Learning for process optimization, and Sustainable Engineering through life cycle assessments and geopolymer applications. His recent publications emphasize BIM , AM Modular Plants , and Defect Analysis in 3D-printed metals. Penot leads France Additive initiatives and contributes to International Standards as a board member. Penot supervises PhD students including Maryam Houhou and Amal Khabouchi , with a focus on Industrial Security and Energy Transitions . His projects integrate Thermal Comfort , Ultrasonic Inspection , and Quality Assurance in additive manufacturing systems.