جستجوی استاد راهنمای دانشگاهی

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.

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

Salim ROSTAMI is an Associate Professor at the IÉSEG School of Management in France, specializing in Operations Management. He holds a Ph.D. in Economics and Mathematics Sciences from KU Leuven (2019) and a Master’s in Engineering from KU Leuven (2013), alongside a Bachelor’s in Industrial Engineering from Ferdowsi University of Mashhad (2012). His research focuses on scheduling under uncertainty, project planning, combinatorial optimization, and healthcare logistics. Notable achievements include the 2016 2nd Best Conference Paper Award from the University of Valencia. Education: Ph.D., Economics and Mathematics Sciences, Operations Management, KU Leuven, Belgium (2019) Master, Engineering, Operations Research, KU Leuven, Belgium (2013) Bachelor, Engineering, Industrial Engineering, Ferdowsi University of Mashhad, Iran (2012) His work spans stochastic resource-constrained project scheduling, sequential testing of systems, and chemotherapy appointment scheduling. He has published widely in journals like the European Journal of Operational Research and Flexible Services and Manufacturing Journal. Teaching roles include courses on operations management and project management across undergraduate and graduate programs. Awards: 2016: 2nd Best Conference Paper Award, University of Valencia His research emphasizes practical applications in healthcare and project management, leveraging dynamic programming and metaheuristic algorithms. Collaborations include work with institutions like École des Mines de Saint-Étienne and KU Leuven.

• Operations Research
• Scheduling under Uncertainty
• Project Planning and Scheduling
• +۳ مورد دیگر

Andrzej Murawski is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Worcester College . His research focuses on the semantics of programming languages and software verification, with applications in automata theory, probabilistic computation, and concurrency. Current affiliations: University of Oxford, SIGLOG Vice-Chair, FoSSaCS Steering Committee Research interests: Game semantics, higher-order recursion, probabilistic systems, differential privacy Recent publications address probabilistic verification, equivalence checking, and game semantics for concurrent systems. He has chaired program committees for conferences such as ESOP and PERR, and his work has earned recognition like the POPL 2025 Distinguished Paper Award . Current students : Benedict Bunting, Haoxuan Yin Past students : Conrad Cotton-Barratt, David Hopkins, Guanyan Li, Dominik Wagner, Fabian Zaiser

• Semantics of Programming Languages
• Automata Theory
• Software Verification
• +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.

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +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.

• Stochastic Processes
• Probability Theory
• Applied Mathematics
• +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).

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

Andreas Honecker is a Professor at the Theoretical Physics and Modeling Laboratory (CNRS UMR 8089) of CY Cergy Paris Université, where he has been employed since September 2014. He currently serves as Adjoint Director of the Institut des Sciences et Techniques (since April 2023) and was previously Director of the Physics Department (October 2020-April 2023). He also co-directs the Master Program in Physics at CY Cergy Paris Université. Professor Honecker's research focuses on condensed matter physics, particularly strongly correlated electron systems, quantum magnetism, and magnetocaloric materials. His work bridges theoretical physics with practical applications in quantum information and low-temperature refrigeration. He has made significant contributions to understanding quantum phase transitions, frustrated spin systems, and the magnetocaloric effect, with a notable Nature publication in 2021 on critical points in SrCu 2 (BO 3 ) 2 . His research activities are characterized by a strong emphasis on numerical methods for many-body systems, including quantum Monte Carlo techniques, density matrix renormalization group approaches, and advanced diagonalization methods. He has organized numerous international workshops on quantum materials, magnetocaloric effects, and quantum information, reflecting the interdisciplinary nature of his work. Among his notable recognitions are the APS Outstanding Referee award (2019) and being named a distinguished referee of The European Physical Journal (2015). He previously held a prestigious Heisenberg fellowship from the Deutsche Forschungsgemeinschaft (2007-2011). Honecker has extensive experience in academic service, including membership in the Conseil National des Universités (section 29, 2017-2023) and the Commission de la formation et de la vie universitaire at Université de Cergy-Pontoise (2016-2019). His collaborative work spans institutions across Europe, including previous positions at Göttingen University, ETH Zürich, and TU Braunschweig.

• Condensed Matter Physics
• Strongly Correlated Electron Systems
• Magnetocaloric Materials
• +۴ مورد دیگر

Alberto Santini is an Associate Professor of Operational Research and a Ramon y Cajal fellow at Universitat Pompeu Fabra in Barcelona, Spain. He is also an affiliate professor at the Barcelona Graduate School of Mathematics and the Data Science Centre at the Barcelona School of Economics. During 2025-2027, he coordinates the Transportation group of the Spanish O.R. Society. His research focuses on optimization methods applied to transportation, logistics, and sustainability, including scheduling, vehicle routing, and heuristic algorithms. He has contributed to solving complex problems like last-mile delivery integration with public transport, airline flight scheduling, and energy-efficient vertical farming. His work often employs advanced techniques like column generation and decomposition strategies. Notable contributions include decomposition strategies for vehicle routing heuristics and the application of metaheuristics such as Adaptive Large Neighbourhood Search (ALNS). He is the founder of EUROYoung and AIROYoung, youth branches within prominent operational research societies. His GitHub repositories, such as cvrp-decomposition , provide open-source implementations of his algorithms. Santini’s research addresses real-world challenges like epidemic resource allocation and sustainable logistics, reflecting his commitment to both theoretical and applied operational research. Awards: Ramon y Cajal Fellow Labs/Teams: Leads Transportation group (Spanish O.R. Society), Founded EUROYoung/AIROYoung.

• Operational Research
• Transportation Systems
• Logistics Optimization
• +۵ مورد دیگر

Yohan PETETIN is an Associate Professor at Telecom SudParis (Institut polytechnique de Paris) in the CITI Department. His research focuses on Bayesian filtering, Monte Carlo methods, hidden Markov models, and multi-object tracking. He has authored over 20 peer-reviewed articles since 2011, with notable contributions in IEEE Transactions on Signal Processing and other top venues. His work bridges statistical signal processing with machine learning applications. PhD: Algorithmes de restauration bayésienne mono- et multi-objets dans des modèles Markoviens (2013, Telecom SudParis) HDR: Generative models for time series data (2023, Institut polytechnique de Paris) Research interests emphasize sequential Monte Carlo algorithms, particle filtering optimizations, and deep learning integration for time-series analysis. Recent work explores expressivity comparisons between recurrent neural networks and hidden Markov models. Teaching includes courses on probabilistic graphical models, Bayesian filtering, and deep learning across undergraduate and graduate programs at Telecom SudParis and affiliated institutions.

• Monte Carlo methods
• Bayesian filtering
• Hidden Markov models
• +۵ مورد دیگر

Imene ZAIDI is a Teacher-researcher at CESI engineering school in France, affiliated with the Engineering and Digital Tools research team. Her work bridges computer science and operational research with practical applications in sustainable transportation systems. Her academic background includes: Doctorate in Optimized management of electric vehicle fleet charging from University of Haute-Alsace (2022) Engineering Diploma in Computer Systems from ex.INI Algiers (2018) ZAIDI's research centers on optimization challenges in electric mobility, particularly electric vehicle charging scheduling. She develops computational approaches including heuristics, metaheuristics, and exact algorithms to address grid capacity constraints, demand satisfaction, and energy maximization in unbalanced power systems. Her work integrates operational research with real-world transportation and energy infrastructure challenges. Analysis of her 2020-2024 publications reveals consistent focus on preemptive scheduling, computational complexity, and multi-objective optimization in electric vehicle ecosystems. The research demonstrates increasing sophistication from foundational scheduling models to grid-integrated solutions addressing three-phase power imbalances and capacity limitations. She teaches computer science courses including Object-oriented programming, Algorithms, Databases, Embedded systems, Web development, and Information systems to engineering and bachelor's students across multiple academic levels. The Engineering and Digital Tools research team at CESI provides the collaborative framework for her investigations into decision support systems for sustainable mobility and energy-efficient transportation solutions.

• Optimization
• Operational Research
• Electric Mobility
• +۳ مورد دیگر

Maxime Ferreira Da Costa is an Associate Professor at CentraleSupélec, Université Paris-Saclay, affiliated with the Laboratory of Signals and Systems (L2S). His research focuses on theoretical and algorithmic foundations of data science, particularly in inverse problems, structured signal processing, and physical layer security. Key research areas include super-resolution, system calibration, and privacy-enhancing communication schemes. He holds a Ph.D. from Imperial College London (2018), and previously worked at USC and Carnegie Mellon University. Notable achievements include an ANR Young Researcher Grant (2023) for projects on data science and physical layer security. He actively contributes to conferences and workshops, with recent presentations at Institut Henri Poincaré and Université Paris-Saclay. Education: Ph.D. in Electrical Engineering, Imperial College London (2018) M.Sc. Electrical Engineering, Imperial College London (2012) Engineer Diploma, CentraleSupélec (2012) Research interests span continuous inverse problems, off-the-grid methods, wireless security, and sensing systems. Current projects explore goal-oriented resource allocation, fake path injection for privacy, and preconditioned optimization techniques. His work bridges signal processing theory with practical applications in imaging, telecommunications, and sensing.

• Signal Processing
• Data Science
• Inverse Problems
• +۶ مورد دیگر

Ioan Todinca is a Professor of Computer Science at the University of Orléans, France, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) research laboratory. His academic career spans over two decades, with significant contributions to theoretical computer science, particularly in graph algorithms and distributed computing. Faculty of Science, University of Orléans LIFO Research Laboratory Member of Institut thématique pluridisciplinaire Modélisation, Systèmes, Langages (since 2014) Former director of MIPTIS doctoral school (2012-2014) Former head of Computer Science degree program (2007-2011) Former leader of LIFO Graphs, Algorithms and Computational Models team (2008-2012) Todinca's research focuses primarily on graph algorithms, with expertise in exact algorithms (moderately exponential), parameterized algorithms, and algorithms for specific graph classes. He has made significant contributions to techniques involving tree decompositions, treewidth, minimal separators, and potential maximal cliques. More recently, his work has expanded into distributed algorithms, especially in communication-constrained models like the broadcast congested clique. His research bridges theoretical foundations with practical algorithmic approaches for NP-hard problems. The analysis of his recent publications reveals a strong trend toward distributed computing problems, particularly in congested network models. His work spans from fundamental graph theory problems (cycle detection, graph modification) to applications in quantum computing and model checking. The consistent focus on communication complexity, verification, and efficient algorithms across different computational models demonstrates his ability to adapt theoretical computer science principles to emerging computational paradigms. Todinca has supervised numerous PhD students and has been actively involved in the theoretical computer science community through conference organization and editorial work. His publications appear consistently in top-tier venues including SIAM Journal on Computing, Algorithmica, and proceedings of major conferences like STACS, WG, and DISC. Teaching responsibilities include algorithms, graph theory, and discrete structures for undergraduate and graduate students, with previous experience teaching databases, programming, and software engineering. His educational materials are hosted on the university's Celene platform.

• Graph Algorithms
• Theoretical Computer Science
• Exact Algorithms
• +۶ مورد دیگر

Cristina Bazgan is a University Professor at Université Paris-Dauphine, affiliated with LAMSADE (Laboratoire d'Analyse et Modélisation de Systèmes pour l'Aide à la Décision) within PSL University. Her office is located at P 409 with contact number 01 44 05 40 90. She maintains an active research profile with numerous publications spanning graph theory, combinatorial optimization, and multi-objective optimization. Professor Bazgan's research primarily focuses on graph theory and combinatorial optimization , with significant contributions to domination theory, network analysis, approximation algorithms, and multi-objective optimization. Her work bridges theoretical computer science and operations research, addressing fundamental problems in computational complexity while developing practical algorithmic solutions. She has made notable contributions to understanding graph partitions, community detection in networks, and the complexity of various optimization problems. Analysis of her recent publications reveals a strong trend in multi-objective optimization and parameterized complexity . Her work often explores the interface between theoretical computer science and operations research, with applications to network analysis and decision support systems. A significant portion of her research addresses the complexity and approximability of graph-theoretic problems, particularly those related to domination, community structure, and anonymization in networks. Professor Bazgan has co-authored the book Combinatorial Algorithms (2022) with H. Fernau and contributed chapters to authoritative works on combinatorial optimization. While specific scientific awards aren't mentioned in the available information, her extensive publication record in top-tier journals demonstrates significant recognition in her field. She maintains an active collaboration network, frequently working with researchers such as Vanderpooten D., Tuza Z., Chlebíková J., and Herzel A. Her research has practical applications in network security, social network analysis, and decision support systems. The LAMSADE laboratory, where she is based, focuses on decision support systems and operations research, providing an interdisciplinary environment for her theoretical and applied work.

• Graph Theory
• Combinatorial Optimization
• Operations Research
• +۴ مورد دیگر

Pierre-Alexandre Mattei is a research scientist at Inria, affiliated with the Maasai team in Sophia Antipolis and the J.A. Dieudonné Laboratory at Université Côte d'Azur. He holds a chair at the 3IA Côte d'Azur institute and has a strong academic background in applied mathematics, having earned his Ph.D. from Université Paris Descartes (now Université Paris Cité) under Charles Bouveyron and Pierre Latouche, followed by a postdoc at the IT University of Copenhagen with Jes Frellsen. Ph.D. in Applied Mathematics, Université Paris Descartes (2017) Postdoctoral Researcher, IT University of Copenhagen His research lies at the intersection of statistical machine learning, generative modeling, and uncertainty quantification, with a focus on hidden variables, missing data, and model interpretability. He has co-organized major workshops such as Artemiss, GenU, SophI.A Summit, and Statlearn, and teaches at the Generative Modeling Summer School (GeMSS). His recent work spans energy-based models, clustering, semi-supervised learning, and medical AI applications. The 15 most recent publications reflect a consistent trend in developing statistically principled methods for generative modeling, with emphasis on likelihood-based inference, missing data, and information-theoretic approaches to clustering and representation learning. His work frequently appears in top venues like NeurIPS, ICML, ICLR, and AISTATS, as well as in journals such as Statistics and Computing and JACC: Advances. Co-organizer, Generative Modeling Summer School (GeMSS) Co-organizer, Workshop on Generative Models and Uncertainty Quantification (GenU) Co-organizer, SophI.A Summit Co-organizer, Statlearn Mattei has advised several PhD students and postdocs, including Raphaël Razafindralambo, Hugo Senetaire, Louis Ohl, Federico Bergamin, and Hugo Schmutz, many of whom have gone on to research positions in Copenhagen, Grenoble, Linköping, and Marseille. He collaborates extensively with researchers across France and Denmark, particularly with Jes Frellsen, Frédéric Precioso, and Charles Bouveyron. He is actively involved in the development of open-source tools and libraries such as the GemClus Python library for discriminative clustering. He is a key member of the Maasai team at Inria Sophia Antipolis, which focuses on models and algorithms for artificial intelligence, and contributes to the broader 3IA Côte d'Azur initiative aimed at advancing AI research through interdisciplinary collaboration.

• Statistical Machine Learning
• Generative Modeling
• Model Uncertainty
• +۷ مورد دیگر

Dr. Mohamed Djemai is a Full Professor at École Nationale Supérieure de l'Électronique et de ses Applications (ENSEA), Cergy, and INSA Hauts-de-France. He is affiliated with the Quartz Laboratory (EA 7393) and LAMIH UMR CNRS 8201 at University Polytechnic Hauts-de-France. His research focuses on nonlinear control systems theory, with emphasis on hybrid and variable structure systems, sliding mode approaches, fault detection, and applications to power systems, robotics, and vehicle dynamics. IEEE Senior Member Associate Editor for Nonlinear Analysis: Hybrid Systems Co-Facilitator of National Working Group GT-SDH (2014–present) Member of IFAC-TC-1.3 (Discrete Event and Hybrid Systems) since 2001 Member of IFAC-TC-2.1 (Control Design) since 2005 His recent publications address fractional-order control of multiagent systems, stability analysis on time scales, fault-tolerant satellite attitude control, and robust consensus algorithms for nonlinear systems. Key methodologies include sliding mode control, event-triggered control, and observer-based fault detection. Current teaching activities encompass diagnostics, linear systems, signal processing, and sensor conditioning. The trend in Dr. Djemai's research since 2022 involves advanced control strategies for cyber-physical systems, distributed fault detection mechanisms, and time scale theory applications to intermittent communication problems. Notable collaborations include work with Michael Defoort, Stefano Di Gennaro, and international institutions like Kyungpook National University and University of Reims. Scientific contributions include: IEEE Senior Member recognition Development of robust exact filtering differentiators Innovations in fixed-time consensus protocols Leadership in IFAC technical committees Editorial role in hybrid systems analysis His laboratory work at Quartz and LAMIH supports applications in aerospace systems, renewable energy conversion, and industrial risk management architectures.

• Control Theory
• Hybrid Systems
• Multi-Agent Systems
• +۹ مورد دیگر