جستجوی استاد راهنمای دانشگاهی

• 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.

Olivier David Zerbibمشاهده پروفایل

استادیار

• Sustainable Finance
• Environmental Finance
• Asset Pricing
• +۲ مورد دیگر

Olivier David Zerbib is an Assistant Professor of Sustainable Finance at CREST, ENSAE, Institut Polytechnique de Paris, and serves as an Associate Editor for the Review of Finance. His research bridges financial theory with environmental sustainability, focusing on market mechanisms for ecological challenges. His primary research areas include sustainable finance, environmental finance, and asset pricing, with specific expertise in green bonds, biodiversity valuation, and climate impact investing. He investigates how pro-environmental preferences influence pricing mechanisms, develops sustainable asset pricing models, and examines shareholder engagement strategies to curb greenwashing through quantitative methodologies. Analysis of his publications reveals a cohesive trajectory toward integrating environmental metrics into core financial frameworks, with significant contributions spanning green bond markets, sustainable CAPM development, and biodiversity economics. His work demonstrates methodological rigor through score-driven conditional betas and empirical validation across global markets. Dr. Zerbib's scientific recognition includes: #1 most cited article in the Journal of Banking and Finance over 3 and 5 years (2019) SUERF/UniCredit Research Prize (2018) Multiple Best Paper Awards at international finance conferences (2017-2020) European Investment Forum Research Prize Finalist (2021) He co-organizes the CMAP-CREST Quantitative Sustainable Economics and Finance seminar series with Patricia Crifo, Emmanuel Gobet, Peter Tankov, and Gauthier Vermandel, fostering collaboration between Ecole Polytechnique and ENSAE on quantitative approaches to sustainability challenges. His presentation record spans elite institutions including LSE, Yale, and the European Commission Joint Research Center.

• 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.

Dond Asha Kisanمشاهده پروفایل

استادیار

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۴ مورد دیگر

Dond Asha Kisan is an Assistant Professor at the School of Mathematics , Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM). His research focuses on numerical analysis and computational mathematics , particularly in finite element methods for partial differential equations. He can be contacted at ashadond@iisertvm.ac.in or via phone at +91 (0)471-2778247. PhD : Mathematics, Indian Institute of Technology Bombay M.Sc. : Mathematics, K.T.H.M. College, Nashik Kisan's research spans adaptive finite element methods , stabilized formulations for convection-diffusion problems , and optimal control governed by Stokes equations . His work includes convergence analysis, nonconforming discretizations, and hybrid numerical schemes. Recent publications (2023-2025) address stochastic modeling in liquid crystal physics, advanced WENO schemes, and adaptive algorithms for control problems. Scientific Awards : No explicit awards mentioned in the data, though he held prestigious postdoctoral fellowships including National Post-Doctoral Fellowship and NBHM Post-Doctoral Fellowship. Kisan has extensive teaching experience , including MATLAB workshops and undergraduate course assistantships. He has presented at major international conferences like ICIAM and Hyperbolic Problems, demonstrating global engagement in computational mathematics.

Aida Jebaliمشاهده پروفایل

استاد

• Supply Chain Management
• Operations Research
• Maritime Logistics
• +۴ مورد دیگر

Aida Jebali is a Professor of Operations and Supply Chain Management at SKEMA Business School's Digitalization Academy. She holds a PhD and Habilitation (HDR) from Grenoble Institute of Technology and has held faculty positions at ESIEE Paris, University of Sharjah, Masdar Institute, and Prince Sultan University. Her research focuses on supply chain resilience, pandemic planning, healthcare operations, and maritime logistics. Education: 2023: HDR in Industrial Engineering, Université Grenoble Alpes 2004: PhD in Industrial Engineering, Grenoble INP 2000: Master of Science in Industrial Engineering, Grenoble INP 1999: National Engineer Diploma, Ecole Nationale d'Ingénieurs de Tunis Research Interests: Pandemic resilience in supply chains Maritime logistics and quay crane optimization Emergency medical service design Healthcare operations and operating room scheduling Environmental sustainability in supply chains Recent Contributions: Her work addresses critical challenges such as pandemic-induced disruptions, ambulance relocation systems, and carbon-efficient supply chains. Recent studies include optimizing production under pandemic conditions and resilience strategies for food supply chains. Awards: High Level Scientific Stay (French Ministry of Foreign Affairs, 2009 & 2007) PhD Scholarship Award (French Ministry of Foreign Affairs, 2000) PhD Supervision: Co-director: G. Pinto, H. NOUIRA, R. BOUJEMAA Jury Member: L. WANG Research Affiliations: Senior Editor of IMA Journal of Management Mathematics and active reviewer for top journals such as International Journal of Production Economics .

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Simulation-Based Optimization
• Supply Chain Management
• Operations Management
• +۴ مورد دیگر

Hamed Jalali is a Full Professor in Supply Chain Management at NEOMA Business School since September 2024, having joined the institution in September 2017. He holds a Master's degree in Statistics and a PhD in Business Economics from KU Leuven, Belgium. His research emphasizes simulation-based optimization, particularly Kriging-based algorithms, and explores intersections between marketing and operations management, as well as sustainable supply chain practices using game-theoretic models. His work has been published in journals like IIE Transactions , European Journal of Operational Research , and International Journal of Production Economics . Dr. Jalali has received numerous teaching awards at NEOMA Business School and collaborates with industries such as IMEC and EuroGijbels on projects like inventory management and rationing strategies. His research spans inventory dynamics, product portfolio management, blockchain applications in supply chains, and coopetition models in closed-loop systems. Education: PhD in Business Economics, KU Leuven, Belgium Master's degree in Statistics, KU Leuven, Belgium His recent articles highlight contributions to sustainable supply chain traceability, bullwhip effect mitigation through product portfolio adjustments, and multiobjective optimization algorithms. He actively reviews for top-tier journals and presents at international conferences. Key Awards: Multiple teaching excellence awards at NEOMA Business School. Industrial Projects: Collaboration with IMEC and EuroGijbels on inventory management and supply chain sustainability initiatives. His research integrates technical simulation methods with real-world supply chain challenges, emphasizing both operational efficiency and environmental sustainability.

Tijani CHAHEDمشاهده پروفایل

استاد

• Network Optimization
• Edge Computing
• Machine Learning in Networks
• +۴ مورد دیگر

Prof. Tijani CHAHED is a Professor at Telecom SudParis, part of Université Paris-Saclay, affiliated with the SAMOVAR laboratory and the NeSS research group. His work focuses on network optimization, edge computing, machine learning applications in telecommunications, and game-theoretical frameworks for distributed systems. He holds a position in the Department of Computer Science and Telecommunications. His research spans resource allocation in 5G/6G networks, energy efficiency strategies for mobile infrastructure, reinforcement learning for dynamic systems, and coalitional game theory for multi-agent systems. Key contributions include optimization of cache allocation in edge computing, latency-critical traffic management (URLLC), and strategic investment models for distributed computing infrastructures. Selected articles highlight advances in edge computing resource management, metaverse data transport over 5G, and energy-efficient sleep mode control for base stations. His work often intersects with industrial applications in green networks and smart grid integration for mobile infrastructure. Collaborations involve institutions like École Polytechnique, INRIA, and industry partners in telecommunications. Current projects include 6G network architectures, metaverse-enabled edge services, and decentralized resource allocation frameworks. Labs/Teams: SAMOVAR Lab (Signal and Media Access Networks, Optical and Radio Networks), NeSS Group (Networked Systems and Services).

Nadjib BRAHIMIمشاهده پروفایل

دانشیار

• Supply Chain Management
• Production Planning and Scheduling
• Operations Research
• +۴ مورد دیگر

Nadjib Brahimi is an Associate Professor at Rennes School of Business, specializing in Supply Chain Management and Operations Research. He holds a PhD in Production and Logistics Systems from Université de Nantes and has extensive academic experience, including roles as Academic Head of the Supply Chain Management & Information Systems department (2019–2020) and Programme Manager of the MSc in Data and Business Analytics. His expertise spans Production Planning, Supply Chain Optimization, and Reconfigurable Manufacturing Systems. Education: PhD in Production and Logistics Systems, Université de Nantes, 2004 M.Sc. in Automatic Control and Applied Computing, Ecole Centrale de Nantes, 2001 Engineering Degree in Electronics and Electrical Engineering, National Institute of Electronics and Electrical Engineering, Boumerdes, Algeria, 1999 Research Focus: His work emphasizes optimization of production systems, reconfigurable manufacturing, and sustainable supply chains. Key areas include task reassignment in assembly lines, biomass supply chain design, and disruption resilience strategies. Key Contributions: Over 40 peer-reviewed articles in journals like International Journal of Production Economics and European Journal of Operational Research . Developed robust optimization models for supplier selection and production routing. Advocated for circular economy principles in hybrid manufacturing systems. Labs/Teams: He collaborates with research groups on reconfigurable manufacturing systems and bioenergy supply chain design, contributing to projects in France, UAE, and Algeria.

Philippe Moireauمشاهده پروفایل

استاد

• Inverse Problems
• Data Assimilation
• Partial Differential Equations
• +۴ مورد دیگر

Philippe Moireau is a Full Professor in the Department of Applied Mathematics at École Polytechnique, where he is also affiliated with the Center for Applied Mathematics (CMAP). He serves as the head of the Inria Project-Team MΞDISIM (Mathematical and Mechanical Modeling with Data Interaction for Simulation in Medicine) and holds the distinguished position of Ingénieur Général of The Corps des Mines. His primary research focuses on inverse problems and data assimilation for partial differential equation models, with particular emphasis on: Observer-based methods from optimal control perspectives Stabilization approaches for evolution equations Numerical analysis of time-dependent control problems Digital twin applications in cardiovascular medicine Professor Moireau's publication portfolio demonstrates consistent focus on mathematical methods for physical systems, with recurring themes in: Data assimilation techniques for PDE-based models Numerical stabilization and discretization methods Cardiovascular biomechanics and hemodynamics Stochastic modeling of biological systems Epidemiological forecasting and control He leads the ANANKΞ project-team at Inria focused on Analysis And Numerics of physical-Knowledge-based Estimation. His educational contributions include lectures on data assimilation theory at CEMRACS and courses on mathematical modeling in cardiac biomechanics at Institut Polytechnique de Paris.

Yelva Roustanمشاهده پروفایل

مدرس

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +۳ مورد دیگر

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

• Cybersecurity
• Network Security
• IoT Security
• +۵ مورد دیگر

Prof. Joaquin GARCIA ALFARO is a Professor at Telecom SudParis, affiliated with the SCN department. His research focuses on cybersecurity, network security, quantum computing applications, and resilience engineering in cyber-physical systems. He has contributed to advancements in intrusion detection systems, blockchain integration in cellular networks, and privacy-preserving frameworks for IoT and healthcare. University: Telecom SudParis Key Research Areas: Cybersecurity, Quantum Computing, IoT Security, Resilience Engineering Labs: SAMOVAR laboratory His work emphasizes practical solutions for real-world challenges, including secure data provenance, digital twin implementations, and energy-efficient edge computing. Recent research explores quantum-resistant protocols and collaborative drone systems.

Samuel Herrmannمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Applied Mathematics
• +۴ مورد دیگر

Samuel Herrmann is a Professor of Applied Mathematics at the University of Burgundy, France. He is a member of the Statistics, Probability, Optimization and Control team and an external member of the TOSCA project team at INRIA. His research focuses on stochastic processes, particularly asymptotic analysis of non-linear stochastic processes, large deviations, and stochastic resonance phenomena, with applications in climatology, biology, and financial modeling. Education: PhD in Mathematics (2001) - University of Burgundy Habilitation (2009) - Asymptotic analysis related to stochastic processes Research Interests: Stochastic differential equations and their numerical simulation Large deviation phenomena in stochastic processes Self-stabilizing diffusions and stochastic resonance First-passage and exit time problems for diffusions Applications in climatology, biology, and finance Scientific Contributions: Professor Herrmann has published extensively on stochastic processes, with over 50 peer-reviewed articles and a monograph on stochastic resonance. His work includes exact simulation methods for diffusion processes, studies on self-stabilizing systems, and theoretical contributions to large deviations theory. He has collaborated with leading researchers such as Peter Imkeller and David Peithmann. Awards and Recognition: Contributed to the encyclopedia of mathematical physics Co-authored the book "Stochastic Resonance: A Mathematical Approach in the Small Noise Limit" (2014) Teaching and Supervision: He teaches courses on stochastic processes and their simulation at both undergraduate and master's levels, including the Master in Turin program. He has supervised numerous PhD and master's students in stochastic processes and related fields.

Didier Raboissonمشاهده پروفایل

استاد

• Animal Health Economics
• Ruminant Population Medicine
• Bioeconomic Modeling
• +۴ مورد دیگر

Didier Raboisson is a Full Professor in Ruminant Population Medicine and Animal Health Economics at the National Veterinary School of Toulouse (ENVT), France. Holding a DVM, MSc, PhD, and Diplomate status in the European College of Bovine Health Management (ECBHM), his career spans from clinical practice (2004-2006) to academic leadership at ENVT since 2006, progressing from contractual assistant to full professor in 2019. His educational credentials include a Doctor in Veterinary Medicine (2003), ruminant clinical internship (2005), MSc in Agricultural Socio-economics (2007), PhD in Institutional Economics (2011), and HDR accreditation (2017). His research pioneered the DairyHealthSimulator® (DHS®), a stochastic dynamic bioeconomic model optimizing dairy herd management under constraints like antimicrobial use. Key contributions address veterinary workforce shortages, disease cost analytics, and veterinary business models through innovative frameworks like the DODforD herd health approach. Raboisson's publication portfolio exceeds 75 international peer-reviewed papers, with recent work focusing on antimicrobial reduction strategies, lameness economics, and dairy cooperative development. His research demonstrates consistent interdisciplinary integration of veterinary science, economics, and stochastic modeling, particularly evident in the shift from descriptive disease cost analysis to utility-optimized decision frameworks. Scientific recognition includes: ECBHM Diplomate status (2011) HDR accreditation (2017) DHS® and App Qost® patents Academic editor roles at PlosOne and Frontiers in Veterinary Sciences As supervisor of 8 PhD students and 6 post-doctorates, Raboisson leads the VetEconomics research group while directing France's continuous training program in bovine population medicine. His grant portfolio exceeds €1.4 million from INRAE, Ministry for Agricultural Sovereignty, ANR, and EU programs. International collaborations span Cornell, Liverpool, and Indian institutions through CEFIPRA and Prezode initiatives, with policy impact evidenced by DGAL working group contributions on veterinary shortages and biosecurity.

Elena Panteleyمشاهده پروفایل

پژوهشگر

• Nonlinear Control Systems
• Networked Systems
• Multi-Agent Systems
• +۵ مورد دیگر

Elena Panteley is a Research Director at the CNRS and a member of the Laboratoire des Signaux et Systèmes (L2S) at CentraleSupélec. She holds a PhD in Applied Mathematics from Saint Petersburg State University (1997) and has extensive experience in research, including roles at the Institute of Problems in Mechanical Engineering, Russian Academy of Sciences (1986–1998). She co-chairs the International Graduate School of Control at the European Embedded Control Institute (EECI-IGSC) and serves as a Book-reviews Editor for Automatica and Associate Editor for IEEE Control Systems Letters . Education: PhD in Applied Mathematics, Saint Petersburg State University, 1997 MSc/BSc in relevant fields (implied by career progression) Research Interests: Focuses on stability and control of nonlinear dynamical systems, networked systems, multi-agent systems, and their applications. Her work emphasizes robust control, consensus algorithms, synchronization, and distributed control strategies for autonomous systems. She explores theoretical frameworks like Lyapunov methods and adaptive control, with practical applications in robotics and communication networks. Publications: Over 100 peer-reviewed articles in top journals like IEEE Transactions on Automatic Control and Automatica . Recent trends include advancements in consensus algorithms for multi-agent systems, synchronization of nonlinear oscillators, and control under communication delays. Awards: No specific awards mentioned, but contributions to control theory and networked systems are widely recognized. Grants/Advising: Leads research on adaptive control, network synchronization, and robotics. Collaborates internationally and advises on projects involving autonomous vehicles and distributed systems. Labs/Teams: Active in L2S and affiliated with CNRS, contributing to teams like MODESTY, COMEDY, and SYCOMORE. Engaged in transverse research on energy, industry automation, and healthy systems.