جست‌وجوی استاد و پژوهشگر

• Optimization
• Complex Networks
• Statistical Inference
• +۳ مورد دیگر

Stefano NASINI is an Associate Professor at the University of Lille 3, specializing in Quantitative Methods within the Economics and Mathematics Sciences. He holds a HDR (Habilitation à Diriger des Recherches) from the University of Lille 3 (2021), a Ph.D. in Statistics and Operations Research from the Polytechnic University of Catalonia (2015), and a Master in Statistics (2011). His research focuses on optimization, complex networks, statistical inference, and microeconomic applications. He has held academic positions including a post-doctoral role at IESE Business School (2014–2016) and a visiting researcher role at the University of Lisbon (2014). His work spans scheduling optimization, network analysis, financial contagion modeling, and energy system planning. Key contributions include specialized algorithms for large-scale optimization problems and frameworks for decentralized portfolio management. He is a member of the LEM research group and teaches courses in optimization, econometrics, and social network analysis at the Grande École and MSc levels. Publications highlight interdisciplinary applications, including network-based diffusion models, multi-market financial strategies, and dynamic choice analysis. His research bridges theoretical advancements in operations research with practical challenges in economics, energy, and transportation systems. No scientific awards are explicitly listed in the provided materials. His advising roles and grants are not detailed here, but his extensive publication record reflects active collaboration within academic and applied domains.

Yvain Brunedمشاهده پروفایل

استاد

• Singular Stochastic Partial Differential Equations
• Regularity Structures
• Renormalisation Theory
• +۵ مورد دیگر

Yvain Bruned is a Professor of Mathematics at Université de Lorraine, Nancy, France, where he leads research in singular stochastic partial differential equations and related fields. He serves as Principal Investigator for the ERC Starting Grant LoRDeT (2023-2028), which focuses on advancing the theory of decorated trees and Hopf algebraic structures for solving singular SPDEs and dispersive PDEs at low regularity. Previously, he was a Lecturer at the University of Edinburgh (2019-2022) and completed postdoctoral work at Imperial College London and University of Warwick under Martin Hairer. His educational background includes: PhD in Mathematics (2012-2015), UPMC (Paris 6), on "Singular KPZ type equations" under Lorenzo Zambotti Master 2 in Probability and Statistics, ENS Cachan / Rennes 1, with honors Master 1 in Mathematics, ENS Cachan, with honors Bachelor in Mathematics and Computer Science, University of Rennes 1, with honors Student at ENS Cachan Brittany extension (2009-2013) Classes Préparatoires in Mathematics and Physics (2007-2009) Bruned's research centers on singular stochastic partial differential equations, with particular focus on Regularity Structures, renormalization theory, and their connections to Hopf algebras. His work bridges theoretical mathematics with applications in quantum field theory, wave turbulence, and numerical analysis. He has developed novel approaches using decorated trees to handle renormalization procedures for singular SPDEs and has extended these methods to dispersive PDEs with random initial data. His research program aims to establish existence and uniqueness results for quasilinear and dispersive SPDEs while developing algebraic tools through deformations of Hopf algebras. His extensive publication record demonstrates consistent contributions to the field of singular SPDEs, with a clear trajectory from foundational work on Regularity Structures to more recent applications in dispersive PDEs and numerical methods. The publications reveal a strong collaborative network with leading researchers in stochastic analysis, mathematical physics, and algebra. His work shows increasing sophistication in handling renormalization procedures through algebraic structures, with recent papers exploring connections between different mathematical frameworks. His major scientific recognition includes: ERC Starting Grant LoRDeT (2023-2028) Bruned actively supervises a large group of researchers, currently advising 4 PhD students and 2 postdoctoral researchers at Université de Lorraine, with several former PhD students having completed their degrees at the University of Edinburgh. His ERC grant has enabled him to organize multiple international workshops in Nancy, fostering collaboration between researchers in singular SPDEs, algebraic structures, and numerical analysis. The grant also supports the development of software platforms for decorated trees and their Hopf algebraic structures. As Principal Investigator of the ERC LoRDeT project, Bruned leads a vibrant research team based at the Elie Cartan Institute of Lorraine, which includes postdocs, PhD students, and visiting researchers. The team regularly organizes specialized workshops on topics including operads, symmetries for quantum field theory, and normal forms for singular dynamics, creating a dynamic research environment that bridges multiple mathematical disciplines.

Pierre Marionمشاهده پروفایل

پژوهشگر

• Deep Learning Theory
• Optimization of Neural Networks
• Generative Models
• +۲ مورد دیگر

Pierre Marion is a Researcher at INRIA Paris, working within the Sierra research team since September 2025. His work focuses on the theoretical foundations of deep learning and he is beginning to explore applications of AI in mathematics. Marion has established collaborations across multiple institutions including EPFL, Sorbonne Université, and Google DeepMind. His educational background includes: Engineering degree from École polytechnique (2015-2018) with specialization in Applied Mathematics Master's degree from Sorbonne Université (2019-2020) PhD from Sorbonne Université (2020-2023) under the supervision of Gérard Biau and Jean-Philippe Vert Postdoctoral research at EPFL (2024) supervised by Lénaïc Chizat Marion's research interests primarily focus on the theory of deep learning, where he investigates the optimization and statistical properties of various neural network architectures. His work spans from shallow networks to complex generative models, with a particular emphasis on understanding the mathematical foundations that govern deep learning performance. Recently, he has begun exploring applications of AI in mathematical research, aiming to bridge the gap between theoretical machine learning and mathematical discovery. His research often combines rigorous theoretical analysis with practical implications for training deep neural networks. Analysis of Marion's recent publications reveals several key trends in his research. He has made significant contributions to understanding the role of large learning rates in optimization dynamics, demonstrating how they can accelerate convergence in logistic regression and prevent memorization in score-based generative models. His work on attention mechanisms has provided theoretical guarantees for their effectiveness in specific tasks like single-location regression and clustering. Additionally, Marion has extensively studied the connections between residual networks and neural ordinary differential equations , establishing generalization bounds and exploring scaling properties in the large-depth regime. His earlier work included contributions to natural language processing and quasi-Monte Carlo methods, reflecting a broad mathematical foundation that informs his current deep learning research. Marion has received several notable scientific awards: Runner-up PhD Award of AFIA (French Association for Artificial Intelligence) in 2024 Google PhD Fellowship in 2022 Ecole polytechnique Grand Prize of Research Internships in 2018 As an advisor, Marion currently supervises PhD student Yu-Han Wu (since 2024), with whom he has co-authored multiple publications on large learning rates and denoising score matching. Previously, he co-supervised several Master's students including Seorim Park, Yerkin Yesbay, and Nathan Doumèche. Marion has been actively involved in the machine learning community through conference organization (NeurIPS@Paris meetups), session chairing (ICSDS 2022), and extensive reviewing activities. He has served as a reviewer for top journals including JASA and Annals of Statistics, and conferences including NeurIPS and ICLR, where he was recognized as a top reviewer at NeurIPS 2023. Marion is a member of the Sierra research team at INRIA Paris, which focuses on machine learning theory and applications. He has also collaborated with researchers at CREST (Center for Research in Economics and Statistics), as evidenced by his participation in seminars organized by Anna Korba and Karim Lounici. His work often bridges theoretical computer science, statistics, and applied mathematics, reflecting the interdisciplinary nature of modern machine learning research.

Stéphane Doncieuxمشاهده پروفایل

استاد

• Cognitive Robotics
• Developmental Robotics
• Evolutionary Robotics
• +۱۶ مورد دیگر

Stéphane Doncieux is a University Professor in Computer Science at Sorbonne University, where he is affiliated with the Institute of Intelligent Systems and Robotics (ISIR), a joint research laboratory with CNRS. Since January 2024, he has served as Director of ISIR, following a term as Deputy Director from 2019 to 2023. He leads the ASIMOV research team and is based at the Pierre and Marie Curie Campus in Paris. His primary research interests lie in cognitive and developmental robotics, with a strong focus on open-ended learning, evolutionary algorithms, and adaptive systems. He investigates how robots can autonomously learn diverse skills through mechanisms such as novelty search, quality-diversity optimization, and intrinsic motivation. His work bridges theoretical foundations in artificial life and practical applications in robotic manipulation, perception, and control. The recent publications highlight a consistent trend in advancing robotic learning under sparse rewards and in open-ended environments. Key themes include quality-diversity optimization for grasping, state representation learning, sim-to-real transfer, and the development of behavioral repertoires. These works are published in high-impact journals such as IEEE Transactions on Robotics, Evolutionary Computation, and Frontiers in Robotics and AI. Coordinator, DREAM FET H2020 project (2015–2018) Principal Investigator, ANR projects on Creative Adaptation by Evolution, Learning Movement Skills, and Grasping with Multimodal Feedback Involved in European initiatives including VeriDREAM and HumanE-AI-Net He has supervised numerous PhD and Master’s students, including Leni Le Goff, Giuseppe Paolo, Alban Laflaquière, and Achkan Salehi, often in collaboration with leading researchers like Olivier Sigaud and Jean-Baptiste Mouret. He teaches computer science and robotics at both undergraduate and graduate levels at Sorbonne University. Doncieux has been instrumental in shaping research directions in evolutionary and developmental robotics, notably through his leadership in the IEEE Task Force on Evo-Devo-Robotics and his editorial contributions. His lab, ASIMOV, fosters interdisciplinary research integrating computer science, neuroscience, and engineering to create more autonomous and intelligent robotic systems.

François Baccelliمشاهده پروفایل

استاد مهمان

• Applied Probability
• Stochastic Geometry
• Wireless Networks
• +۴ مورد دیگر

François Baccelli is a Research Director at INRIA Paris and Visiting Professor at Télécom Paris, leading the ERC Advanced Grant NEMO project on network mathematics. He holds a Doctor Honoris Causa from Heriot-Watt University and is a Member of the French Academy of Sciences. His career spans key roles including head of the Simons Center on Communication, Information and Network Mathematics (2012–2019), and faculty positions at École Polytechnique (1991–2003) and the University of Texas at Austin (2012–2021) as Simons Math+ECE Chair. Education: PhD in Applied Mathematics from Université Paris-Sud (1983), advised by Erol Gelenbe. Graduate of Télécom Paris (1977). Research Interests: Baccelli’s work bridges applied probability, stochastic geometry, and network dynamics with applications to telecommunications. He pioneered spatial stochastic networks, stochastic geometry modeling of wireless systems, and max-plus algebra for discrete-event systems. His contributions include foundational theories on queuing networks, Palm probabilities, and latency-constrained system design. Awards & Recognition: 2024 Blackwell Prize (INFORMS Applied Probability Society). 2014 ACM Sigmetrics Achievement Award. 2014 IEEE Stephen O. Rice and Leonard G. Abraham Prizes. Collaborations: Co-founder of the LINCS joint laboratory (INRIA/IMT/Nokia Bell Labs/Sorbonne/SystemX), fostering interdisciplinary research in communications and networks. Supervised numerous graduate students who now lead academic and industrial research globally. Labs & Teams: LINCS laboratory and ERC NEMO project, focused on network mathematics and large-scale system dynamics.

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +۴ مورد دیگر

Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.

• Probability Theory
• Stochastic Processes
• Graph Signal Processing
• +۴ مورد دیگر

Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.

Michalis Vazirgiannisمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Graph Analysis
• +۱۱ مورد دیگر

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique in France, where he leads the Data Science and Mining group (DaSciM). He holds a degree in Physics and a PhD in Informatics from Athens University (Greece), and a Master's degree in AI from Heriot Watt University, Edinburgh (UK). His academic career spans multiple prestigious institutions including Fraunhofer and Max Planck MPI in Germany, INRIA/FUTURS in Paris, AUEB in Greece, Telecom-Paristech, ENS in France, Tsinghua and Jiaotong Shanghai in China, and Deusto University in Spain. Professor Vazirgiannis's research focuses on machine and deep learning methods for graph analysis, including community detection, graph clustering, node embeddings, and influence maximization. His work in text mining encompasses Graph of Words, word embeddings with applications to web advertising and marketing, event detection, and summarization. He has active collaborations with industrial partners in analytics and machine learning for large-scale data repositories across various application domains such as recommendations, meeting summarization, influence metrics for scientific and social networks, and predictive maintenance. His recent publications demonstrate a strong emphasis on Graph Neural Networks, multilingual NLP (particularly for French and Arabic), and applications of deep learning to diverse domains including social networks, legal text, and biomedical data. There's a clear trajectory toward developing more efficient, explainable, and specialized models that address real-world challenges in data analysis. ERCIM fellowship Marie Curie EU fellowship Tencent "Rhino-Bird International Academic Expert Award" (2017) Best Paper Award at IJCAI 2018 Best Paper Award at CIKM 2013 Professor Vazirgiannis has supervised 29 completed PhD theses and has attracted significant R&D funding from national and international sources, including research agencies and industrial partners such as Google, Airbus, Huawei, Deezer, BNP, and LVMH. He leads or has led several academic research chairs including DIGITEO (2013-15), ANR/HELAS (2020-25), and AXA (2015-2018). The DaSciM research group, which he leads at École Polytechnique, has extensive experience in real-world R&D projects involving large-scale data mining. The team maintains active collaborations with major industrial partners including AIRBUS, Google, BNP, Tencent, and Tradelab, working on cutting-edge machine learning projects. The group has co-organized major conferences such as ECML PKDD 2011 and ECML/PKDD 2017 and participates in the senior organization of AI and data mining events like AAAI and IJCAI.

Maria Mushtaqمشاهده پروفایل

دانشیار

• Embedded System Security
• Microarchitectural Security
• Artificial Intelligence Applications in Security
• +۲ مورد دیگر

Maria Mushtaq is an Associate Professor at Telecom Paris , affiliated with the Information Processing and Communication Laboratory (LTCI) and the Secure and Safe Hardware (SSH) Lab . She received her PhD in Information Security from the University of South Brittany, France (2019) and completed 2 years of postdoctoral research at LIRMM, University of Montpellier under the CNRS excellence post-doc grant. Research Focus: Microarchitectural vulnerability assessment, runtime mitigation against side/covert-channel attacks, cryptanalysis, OS-based security primitives, and hardware-software interface security Technical Expertise: Cache timing attacks, transient execution attacks (Spectre/Meltdown), Hardware Performance Counter analysis, gem5 simulation Her recent work involves RISC-V security analysis using gem5 simulations and machine learning for attack detection. She serves as Guest Editor for the Journal of Applied Sciences special issue on Side Channel Attacks in Embedded Systems and has been on the Program Committee for the European Test Symposium (2020-2021). 2021 HiPEAC Collaboration Grant recipient Organized IP Paris Winter School on Microarchitectural Security (2022) Active in international conferences as panelist and keynote speaker

Vincent Rocaمشاهده پروفایل

پژوهشگر

• Privacy
• Robust Content Distribution
• Transport Protocols
• +۶ مورد دیگر

Vincent Roca is a permanent researcher at Inria , a French national research institute for digital science and technology. Since 2019, he leads the Privatics research team focusing on privacy, previously part of the Planete team (2000-2012) for Internet protocols and applications. His work spans Privacy in smartphones, IoT, and web browsers Design of the ROBERT and CLEA protocols for the French TousAntiCovid app Standardization in IETF working groups, notably on FEC and network coding Co-author of Inria's cybersecurity white book (2019) Instructor of the popular Privacy in the digital world MOOC (14,006 participants) Research Interests include privacy risks in digital technologies, robust content distribution via FLUTE/ALC transport protocols and LDPC-Staircase FEC codes, and network coding for scalable services. His articles focus on privacy in LoRaWAN, contact tracing protocols, GDPR compliance, and FEC optimization. Scientific Awards include the Third Applied Research Price from FIEEC (2014) Knight Medal of the French Order of Merit (2020) Students supervised include PhD candidates in privacy-preserving IoT, secure authentication, and alumni like Samuel Pelissier (LoRaWAN privacy) and Michael Toth (GDPR compliance). Software Contributions include openfec.org (AL-FEC codes) and FLUTE/ALC implementations .

• Formal Methods
• Verification
• Model Checking
• +۴ مورد دیگر

Nathalie Bertrand is an Inria Researcher and Head of the DEVINE team at Inria Centre Rennes and IRISA lab. She co-heads the working group on Verification of the GDR IFM of the CNRS with Pierre-Alain Reynier, and co-leads the gender equality commission of IRISA and Inria Rennes with Elisa Fromont. She is also an active member of the Inria committee on gender equality and equal opportunity. PhD from ENS Cachan (October 2006) 1-year post-doc at TU Dresden Hired as Inria researcher (October 2007) Habilitation (HDR) from University Rennes 1 (November 2015) Dr. Bertrand's research focuses on formal methods, verification, model checking, parameterized systems, quantitative models, automata, and games. Her work bridges theoretical computer science with practical applications in distributed systems, probabilistic verification, and game theory. She develops efficient model-based formal methods to verify and enforce functional and non-functional properties of dependable distributed systems that include quantitative aspects such as time, cost, or probabilities. Her recent publications demonstrate a strong trend toward parameterized verification of distributed algorithms, probabilistic systems, and game-theoretic approaches to verification problems. Her research has evolved from foundational work on timed automata and probabilistic systems to increasingly complex problems involving distributed algorithms with arbitrary numbers of participants, blockchain consensus, and randomized fault-tolerant systems. Dr. Bertrand serves on the editorial boards of JLAMP and TCS, and has been a program committee member for numerous prestigious conferences including STACS, TACAS, Concur, QEST, LICS, FoSSaCS, ICALP, MFCS, and many others. She has co-chaired program committees for Formats'20, QEST'17, QAPL'14, and QAPL'15. As an advisor, she has supervised numerous PhD students including Nicolas Waldburger, Bastien Thomas, Suman Sadhukhan, Anirban Majumdar, Engel Lefaucheux, Paulin Fournier, and Amélie Stainer. She currently supervises postdoc Gaëtan Staquet and PhD candidates Pranav Ghorpade, Luca Paparazzo, and Luc Lapointe. She leads the DEVINE project-team (created January 2024), which focuses on developing efficient model-based formal methods to verify and enforce properties of dependable distributed systems with quantitative aspects. Previously, she was part of the SUMO team. Her current projects include ANR MAVeriQ (2021-2025), ANR BISouS (2022-2025), and ANR PaVeDyS (2024-2028).

Fabien Duveauمشاهده پروفایل

پژوهشگر

• Evolutionary Biology
• Genetics
• Molecular Biology
• +۵ مورد دیگر

Fabien Duveau is a CNRS Researcher at the Laboratory of Biology and Modelling of the Cell (LBMC) at École Normale Supérieure de Lyon. He leads the eGRIDE project, funded by an ERC Consolidator Grant, investigating evolutionary mechanisms of gene regulation in dynamic environments. His work focuses on understanding how gene expression plasticity and mutational effects shape organismal adaptation. Duveau holds a PhD from Université Paris 7/CNRS (2011), followed by postdoctoral research at the University of Michigan and Université Paris 7. He joined LBMC in 2019 as part of Gaël Yvert’s team, supported by an ANR Young Researchers grant. His research integrates experimental and computational approaches, including high-throughput RNA sequencing and genetic mapping in yeast ( Saccharomyces cerevisiae ), to study how regulatory evolution balances mutational randomness and environmental selection pressures. Key areas include gene expression noise, cis/trans regulatory variation, and fitness outcomes under environmental stress. Recognitions include the prestigious ERC Consolidator Grant (2024), awarded for innovative projects advancing fundamental biological understanding. Duveau collaborates across disciplines, combining evolutionary genetics with systems biology to predict regulatory evolution in natural and pathological contexts.

Jamal Atifمشاهده پروفایل

استاد

• Artificial Intelligence
• Responsible AI
• Privacy Preservation in Machine Learning
• +۴ مورد دیگر

Jamal Atif is a Professor at Paris-Dauphine University and holds multiple significant leadership positions including Project Manager for 'Data Science and Artificial Intelligence' at the Institute of Information Sciences and their Interactions (INS2I) of the CNRS, Deputy Scientific Director of 3IA PRAIRIE, Head of the MILES team/project at LAMSADE (UMR CNRS-Université Paris-Dauphine), Co-leader of the Transverse Artificial Intelligence Program at PSL University, and Director of the Dauphine Numérique program. Professor Atif's primary research focuses on the foundations of responsible artificial intelligence, with specific expertise in privacy preservation in machine learning, robustness of deep learning algorithms to malicious attacks, causality, and explainability. His work bridges theoretical foundations with practical applications in security and reliability of AI systems. He has developed innovative approaches to address adversarial vulnerabilities in machine learning models and has made significant contributions to privacy-preserving techniques in data analysis. His publication record demonstrates a consistent focus on robust and trustworthy AI systems, with recent work exploring differential privacy in clustering, adversarial robustness, and explainable AI. The research spans theoretical foundations in logic and knowledge representation to practical applications in finance, healthcare, and computer vision. His publications appear in top-tier venues including Machine Learning journal, Neural Information Processing Systems, and International Joint Conferences on Artificial Intelligence. Scientific Awards: Recipient of two awards from the North American Society of Radiology for his thesis work Professor Atif has co-supervised or is currently supervising around fifteen doctoral students, demonstrating his commitment to mentoring the next generation of AI researchers. His leadership extends to directing major institutional programs including Dauphine Numérique and the Transverse Artificial Intelligence Program at PSL University, where he shapes strategic research directions in AI. He leads the MILES team/project at LAMSADE, which focuses on foundational aspects of machine learning and artificial intelligence. The team's research spans theoretical aspects of learning algorithms to practical applications requiring robust and reliable AI systems, with particular emphasis on security and privacy considerations in modern machine learning deployments.

Aws Albarghouthiمشاهده پروفایل

پژوهشگر

• Program Synthesis
• Formal Verification
• Quantum Computing
• +۵ مورد دیگر

Aws Albarghouthi is affiliated with the University of Wisconsin-Madison, USA. He is an active researcher with significant contributions to program synthesis, formal verification, and machine learning. Key roles: Author, Session Chair, Committee Member in conferences like PLDI, POPL, VMCAI, SPLASH, and ICFP. Research spans quantum computing, differential privacy, and static analysis. Research Trends include: Quantum Circuit Compilation and Optimization Probabilistic Verification of Fairness and Privacy Synthesis of Datalog and MapReduce Programs Neural-Augmented Static Analysis Bias Detection in Data Security Robustness in Machine Learning

Petr Kuznetsovمشاهده پروفایل

استاد

• Distributed Systems
• Algorithms
• Synchronization
• +۳ مورد دیگر

Petr Kuznetsov is a Professor at Telecom Paris (Institut Polytechnique de Paris), affiliated with the Department of Computer Science and Networks (INFRES). He leads the Autonomous Critical Embedded Systems (ACES) research team at the Information Processing and Communication Laboratory (LTCI). His work bridges theoretical foundations and practical applications in distributed systems. Research Focus: Kuznetsov specializes in distributed algorithms, synchronization protocols, failure detection mechanisms, and the application of algebraic topology to distributed computing. His recent work explores Byzantine fault tolerance, blockchain consensus, and concurrency in networking infrastructures. Recent Publication Trends (2015-2025): His articles predominantly focus on scalability and resilience in distributed systems, with emerging themes in blockchain technologies, Byzantine fault tolerance, and concurrency optimization. Theoretical contributions include computability theorems and complexity bounds, while applied work targets payment systems and distributed ledgers. Awards & Honors: Best Paper Award at DISC 2019 for Scalable Byzantine Reliable Broadcast Best Student Paper Award at PODC 2018 for An Asynchronous Computability Theorem for Fair Adversaries Projects & Advising: He directs the TrustShare Innovation Chair (large-scale data synchronization) and DISCMAT (mathematical foundations of distributed computing). Actively seeks PhD candidates for projects on distributed algorithms and concurrency. Laboratory & Teams: Heads the ACES team at LTCI, focusing on critical embedded systems and fault-tolerant distributed architectures. Collaborates internationally through workshops like SPTDC.