جست‌وجوی استاد و پژوهشگر

• statistical machine learning
• optimization
• sparse methods
• +۵ مورد دیگر

Francis Bach is a Professor and researcher at INRIA, leading the SIERRA project-team since 2011, which is part of the Computer Science Department at Ecole Normale Supérieure (ENS) within PSL Research University. His work bridges CNRS, ENS, and INRIA as a joint research effort. Elected to the French Academy of Sciences in 2020, he currently runs the ERC project SEQUOIA following his previous ERC project SIERRA (2009-2014). His research spans statistical machine learning with focus on optimization, sparse methods, kernel-based learning, neural networks, graphical models, and signal processing. Bach completed his Ph.D. in Computer Science at U.C. Berkeley under Professor Michael Jordan, followed by work at Ecole des Mines de Paris and the WILLOW project-team at INRIA/ENS/CNRS (2007-2010). His recent book "Learning Theory from First Principles" was published by MIT Press in December 2024. Bach's publication record shows consistent high-impact contributions across machine learning theory and applications, with recent work focusing on conformal prediction, diffusion models, optimization theory, and learning theory foundations. His research demonstrates strong connections between theoretical guarantees and practical algorithms, with applications spanning generative modeling, robust optimization, and statistical inference. Elected to French Academy of Sciences (2020) ERC project SIERRA (2009-2014) ERC project SEQUOIA (current) Author of "Learning Theory from First Principles" (MIT Press, 2024) Bach actively mentors numerous PhD students and postdocs, with many alumni now holding faculty positions at institutions like EPFL, Ecole Polytechnique, University of Washington, and University of Montreal. His teaching includes advanced courses on learning theory at ENS's Master's programs. He regularly presents tutorials at major conferences including COLT, NeurIPS, and ICML, demonstrating his leadership in the theoretical machine learning community.

• Risk Analysis
• Resilience Engineering
• Optimization Under Uncertainty
• +۷ مورد دیگر

Dr. Yi-Ping Fang is an Assistant Professor at the EDF Chair SSEC with a joint appointment at the Industrial Engineering Laboratory, CentraleSupélec, Université Paris-Saclay, France. His research focuses on computational methods for risk, vulnerability, and resilience analysis of critical infrastructures including smart grids, electrified transportation, and interdependent lifeline systems. Risk Analysis Resilience Engineering Optimization Under Uncertainty Game Theory Applications His work applies advanced techniques like distributionally robust optimization, POMDP modeling, and interdependency analysis to enhance infrastructure resilience against climate change, natural hazards, and intentional attacks. Publications demonstrate expertise in hybrid optimization algorithms, stochastic modeling, and network vulnerability assessment. Recent trends include: Smart grid resilience enhancement Uncertainty quantification in infrastructure systems Multi-stage decision modeling Game-theoretic approaches for interdependent networks Integration of deep learning for dynamic system prediction

Olivier Sigaudمشاهده پروفایل

استاد

• Reinforcement Learning
• Robotics
• Decision Modeling
• +۳ مورد دیگر

Olivier Sigaud is a Full Professor at Sorbonne University, affiliated with the ISIR (Intelligent Systems and Robotics Institute) and the Machine Learning and Intelligent Autonomous Systems (MLIA) team. He holds an engineering degree from ISEN and dual PhDs in Computer Science (University of Paris XI, 1996) and Philosophy (University of Paris I, 2004). Previously employed at Dassault Aviation (1995–2001), he transitioned to academia as a Lecturer and later a Professor at LIP6 and ISIR. His research focuses on reinforcement learning, robotics, computational neuroscience of decision-making in animals, and human-robot interaction. Key contributions include advances in goal-conditioned reinforcement learning, intrinsically motivated agents, and human-in-the-loop systems. He has co-authored over 100 publications in top-tier conferences (NeurIPS, ICML) and journals, with recent work exploring large language model grounding, open-ended learning frameworks, and motor skill acquisition through interactive curricula. Notable projects include the CURIOUS framework for modular multi-goal RL and the DREAM architecture for open-ended robotic learning. His work bridges theoretical AI with practical robotics applications, emphasizing interdisciplinary collaboration between computer science and neuroscience.

• Risk Analysis
• Resilience Engineering
• Reliability Engineering
• +۵ مورد دیگر

Dr. Yi-Ping Fang serves as an Assistant Professor at the EDF Chair SSEC with a joint appointment at the Industrial Engineering Laboratory, CentraleSupélec, Université Paris-Saclay, France. His research focuses on developing computational methodologies for risk, vulnerability, and resilience analysis of critical infrastructure systems including smart grids, electrified transportation networks, and interdependent lifeline systems. His core research interests encompass: Risk Analysis Resilience Engineering Reliability Engineering Optimization under Uncertainty Decision Making under Uncertainty Critical Infrastructure Systems Smart Grids Interdependent Systems Dr. Fang's recent publications reveal a concentrated research trajectory applying distributionally robust optimization, stochastic programming, and game theory to infrastructure resilience challenges. His work demonstrates particular expertise in microgrid hardening, distribution network restoration, and maintenance optimization under uncertainty, with significant contributions to modeling supply-demand fluctuations, random contingencies, and climate change impacts. Key application domains include energy systems (microgrids, wind farms), transportation networks (electric vehicle integration), and communication infrastructures. Scientific Awards: No scientific awards documented in the provided materials Dr. Fang actively advises students in risk/resilience analysis and optimization methodologies, though specific advisee names are not listed. His research program involves substantial collaboration with industry partners like EDF and academic colleagues including Anne Barros and Henry Uncle, focusing on practical implementations of resilience frameworks for critical infrastructure protection. Current projects emphasize prescriptive analytics for infrastructure networks, maintenance optimization under imperfect monitoring, and game-theoretic approaches to interdependent system vulnerabilities. He operates within the Industrial Engineering Laboratory at CentraleSupélec, which serves as the primary research hub for his work on computational methods in industrial engineering contexts, particularly for complex infrastructure systems requiring advanced decision-making frameworks under uncertainty.

• Stochastic Optimization
• Chance-Constrained Optimization
• Distributionally Robust Optimization
• +۶ مورد دیگر

Abdel Lisser is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on stochastic optimization, distributionally robust optimization, and game theory with applications in control systems, networks, and autonomous systems. He has made significant contributions to chance-constrained programming, Markov decision processes, and neurodynamic optimization. His work bridges mathematical foundations with practical applications in energy management, telecommunications, and machine learning. Notable recent research includes the development of physics-informed neural networks for solving nonlinear optimization problems and stochastic trajectory planning for autonomous vehicles. Lisser collaborates internationally, with publications in top journals such as *Journal of Optimization Theory and Applications* and *IEEE Transactions on Neural Networks and Learning Systems*. He advises on projects involving robust control and decision-making under uncertainty. His laboratory, L2S, is a leading center for systems analysis and optimization at Paris-Saclay.

Erwan LE PENNECمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Statistics
• +۵ مورد دیگر

Erwan LE PENNEC is a Professor in the Department of Applied Mathematics at École Polytechnique, France, where he has been since 2013. He is affiliated with the Center for Applied Mathematics (CMAP) and the SIMPAS team, focusing on data science, artificial intelligence, statistics, and signal processing. His research spans health applications (notably women’s health) and reinforcement learning, often collaborating with interdisciplinary teams. He leads programs like the MScT Data Science and AI for Business, and contributes to executive education initiatives at Polytechnique. Education: Habilitation à Diriger des Recherches (HdR), Université Paris Sud, 2013 PhD in Applied Mathematics, École Polytechnique, 2002 Master in Mathematics and Artificial Intelligence, ENS Cachan, 1997 Research interests emphasize health and AI-driven solutions, with contributions to medical diagnostics (e.g., neonatal acidemia detection via DeepCTG®), image processing, and robust reinforcement learning. His work bridges theoretical foundations and practical applications, including collaborations with industry partners like Air Liquide and Sonio. Teaching includes courses on data science, statistics, and Python/R tools. He has advised over 20 PhD/PostDoc students, focusing on topics like reinforcement learning, medical imaging, and statistical modeling.

Öncü HAZIRمشاهده پروفایل

استاد

• Project Management
• Inventory Management
• Machine Scheduling
• +۴ مورد دیگر

Öncü HAZIR is a Full Professor of Supply Chain Management at Rennes School of Business (since December 2024), previously holding the rank of Associate Professor there from 2017–2024. He also served as Programme Manager for the MSc in Supply Chain Management (2017–2021) and held academic roles at TED University (2012–2017) and Cankaya University (1999–2010). His academic journey includes a PhD in Operations Management from Bilkent University (2008), an MBA (2001), and a B.S. in Industrial Engineering (1999) from Middle East Technical University. He completed post-doctoral research in France at École Nationale Supérieure des Mines de Saint-Étienne (2010–2012) and Université Pierre et Marie Curie (2008–2010). Research Focus: His work spans project management, inventory control, machine scheduling, and assembly line balancing with a focus on robust optimization under uncertainty. Key contributions include risk-averse project scheduling models, machine learning-enhanced cost estimation, and workload smoothing algorithms for assembly lines. Publications: Over 30 peer-reviewed articles in top journals like European Journal of Operational Research , International Journal of Production Research , and IEEE Transactions on Engineering Management . Notable works address payment delay risks, perishable inventory policies, and big data adoption in project management. Education Contributions: Co-authored the textbook An Introduction to Project Modeling and Planning (Springer, 2021) and developed pedagogical materials on project management methodologies. Professional Engagement: Presented at major conferences (EUROMA, MISTA, PMS) and contributed to research centers like the Center for Unframed Thinking (CUT) and the AI-Driven Business research group at Rennes SB.

Pascal Quachمشاهده پروفایل

پژوهشگر

• Robust Optimization
• Resilience Engineering
• Energy Systems
• +۳ مورد دیگر

Pascal Quach is a researcher specializing in robust optimization and resilience engineering, with a focus on energy systems and critical infrastructure. His work intersects operations research, stochastic optimization, and climate adaptation. Research Focus : Distributionally robust optimization, microgrid design, infrastructure resilience Methodologies : Benders decomposition, column-and-constraint decomposition, reinforcement learning Recent trends in his publications highlight applications of advanced optimization techniques to enhance climate resilience in infrastructure networks and improve energy system reliability under uncertainty. He actively explores causal representation learning in human motion modeling, demonstrating interdisciplinary interests. He is affiliated with the Industrial Engineering Laboratory, where he collaborates with Yiping Fang and Anne Barros on projects addressing supply-demand uncertainty, distributed energy resources, and network scalability challenges.

Anne Barrosمشاهده پروفایل

پژوهشگر

• Resilience Engineering
• Optimization
• Critical Infrastructure Networks
• +۴ مورد دیگر

Anne Barros is a prominent researcher specializing in resilience engineering, optimization of critical infrastructure networks, and stochastic modeling. Her work focuses on enhancing the reliability and resilience of power systems, telecommunications networks, and interdependent infrastructures under uncertainty. She has contributed significantly to the development of maintenance strategies, condition-based monitoring systems, and decision-making frameworks under stochastic environments. Key research areas: Resilience quantification, stochastic optimization, infrastructure interdependencies, 5G communication reliability, and climate change impact modeling. Co-authored over 50 publications including articles in top journals and conference proceedings. Her recent work emphasizes the integration of reinforcement learning techniques with maintenance optimization for industrial systems. Collaborations include projects on pandemic control strategies, smart grid resilience, and disaster recovery modeling. Barros' research has been applied to real-world systems such as offshore wind farms, railway-power networks, and critical infrastructure coordination frameworks. She actively participates in international conferences and publishes across interdisciplinary domains including reliability engineering, operations research, and systems engineering.