جست‌وجوی استاد و پژوهشگر

• Quantum Thermodynamics
• Open Quantum Systems
• Quantum Fluctuations
• +۴ مورد دیگر

Prof. Dr. Patrick Potts is a faculty member at the Department of Physics, University of Basel , where he leads the Quantum Thermodynamics Group . He completed his BSc in Nanosciences (2010) and MSc in Physics (2012) at the University of Basel, followed by a PhD in Theoretical Mesoscopic Physics (2013-2016) at the University of Geneva under Markus Büttiker and Christian Flindt. He held postdoctoral positions at the University of Geneva (2016-2017) and Lund University (2018-2021), supported by a Marie Skłodowska-Curie Individual Fellowship and a Swedish Research Council Starting Grant. Research Interests: Patrick Potts focuses on open quantum systems out of equilibrium , particularly in quantum thermodynamics, fluctuations, sensing, and information theory. His work bridges quantum mechanics with thermodynamic principles, exploring how nano-scale quantum systems can surpass classical limitations. Key themes include feedback control, reservoir engineering, and the role of coherence in thermodynamic processes. Publications: His recent work spans quantum feedback protocols , stochastic dynamics , and non-classical fluctuations , with a strong emphasis on quantum thermodynamics and technologies. Notable contributions include deterministic models for feedback control, thermodynamic frameworks for driven systems, and methods like prodiabatic elimination. Honors & Awards: Award in General Physics (Swiss Physical Society, for PhD thesis) Eccellenza Professorial Fellowship Marie Skłodowska-Curie Individual Fellowship Grants & Collaborations: Funded by the Swedish Research Council Starting Grant and involved in projects like Practical bounds for quantum state preparation and Fluctuations, Sensing, and Information in Open Quantum Systems .

Manolis Zampetakisمشاهده پروفایل

استادیار

• Theoretical Machine Learning
• Statistics
• Optimization
• +۴ مورد دیگر

Manolis Zampetakis is an Assistant Professor of Computer Science at Yale University. Previously, he was a postdoctoral researcher in the EECS Department at UC Berkeley working with Michael Jordan. He earned his Ph.D. and M.S. from the EECS Department at MIT, where he was advised by Constantinos Daskalakis. His undergraduate education was completed at the National Technical University of Athens (NTUA). His research interests span Theoretical Machine Learning, Statistics, Optimization, Computational Complexity, Game Theory, Mechanism Design, and Sublinear Algorithms. His work bridges theoretical computer science with practical applications in machine learning and economics. His research consistently appears in top-tier conferences such as STOC, FOCS, COLT, NeurIPS, and EC, with a strong focus on the theoretical foundations of machine learning and game theory. Zampetakis has received numerous prestigious awards including the ACM SIGEcom Doctoral Dissertation Award and the Google Ph.D. Fellowship. His publications from 2023-2025 demonstrate a consistent output of high-quality research across multiple domains, with a notable emphasis on bridging statistical theory with algorithmic approaches. His work shows increasing integration of robust statistics with mechanism design and game-theoretic approaches. ACM SIGEcom Doctoral Dissertation Award Google Ph.D. Fellowship Special Issue at SAGT 2015 Special Issue at WINE 2013 Second Prize in IMC 2012 Top 5 in Panhellenic Physics Competition 2008 Zampetakis actively advises multiple graduate students including Anay Mehrotra, Jane Lee, Vikram Kher, Katerina Mamali, Shuchen Li, and Nikolaos Koumpis. He has served on program committees and organized workshops including the Workshop on Algorithms for Learning and Economics (WALE 2019, WALE 2022). His research has been supported by grants from Google and likely other major funding agencies given his publication record in top venues. His research group focuses on theoretical aspects of machine learning with connections to statistics and game theory. He collaborates extensively with researchers across institutions including MIT, UC Berkeley, and Yale colleagues.

• Mechanical Engineering
• Structural Engineering
• Materials Science
• +۷ مورد دیگر

Prof Zhen (Jeff) Luo is a Professor at the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS). Since 2012, he has led the Advanced Metamaterials & Metastructures (AMM) and Engineering Computation and Optimisation (ECO) research groups, focusing on multi-disciplinary engineering innovations. Expertise : Advanced materials design, topology optimization algorithms, additive manufacturing, and computational mechanics. Education : PhD in Mechanical Engineering from Huazhong University of Science and Technology (2005). His research bridges Mechanical, Structural, Aerospace, and Biomechanical Engineering , developing cutting-edge metamaterials and computational methods. Recent work includes two-scale lattice optimization , stochastic bandgap analysis , and machine learning-aided virtual modeling for structural reliability. Key contributions span 3D-printed heat sinks , frequency-selective surfaces , and hydrogen storage systems . As a World’s Top 2% Scientist (Stanford, 2019–present), he has secured over AUD $7 million in grants, including from the Australian Research Council (ARC) and National Intelligence Discovery Research Grants (NI220100074). Awards : IAAM Scientist Award, IAAM Fellow. Leadership : Editorial roles in Structural and Multidisciplinary Optimization , Frontiers in Bioengineering and Biotechnology , and organizing roles in 21 international conferences.

Marta Lazzarettiمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Inverse Problems in Imaging
• Optimization Algorithms in Banach Spaces
• +۳ مورد دیگر

Marta Lazzaretti is a Research Fellow at the Department of Mathematics (DIMA) of the University of Genoa. Her work focuses on inverse problems in imaging, numerical analysis, and optimization algorithms in non-standard functional spaces. Affiliation: Department of Mathematics, University of Genoa Academic Rank: Research Fellow Research Interests: Specializing in regularization techniques and numerical optimization, her research spans: Off-the-grid methods for Poisson inverse problems Banach space formulations for geophysical data inversion Stochastic gradient descent in variable exponent Lebesgue spaces Dual descent regularization algorithms Publication Trends: Recent work emphasizes non-Hilbertian optimization frameworks (2023-2025), combining stochastic methods with deterministic regularization for imaging and subsoil inversion applications. Collaborations include Claudio Estatico, Luca Calatroni, and Giuseppe Rodriguez.

Geordie Richardsمشاهده پروفایل

استادیار

• Nonlinear Partial Differential Equations
• Probability Theory
• Dynamical Systems
• +۶ مورد دیگر

Geordie Richards is an Assistant Professor in the Department of Mathematics and Statistics at the University of Guelph. He holds a PhD in Mathematics from the University of Toronto (2012) and has held academic positions at the University of Minnesota, University of Rochester, Utah State University, and the University of Toronto Mississauga. PhD in Mathematics, University of Toronto (2012) NSERC Graduate Scholarship (2006) Richards' research focuses on the analysis of deterministic and stochastic nonlinear partial differential equations (PDEs) arising in physics and engineering, particularly those modeling fluid dynamics and dispersive wave phenomena. His work integrates techniques from probability theory and dynamical systems to address questions of well-posedness, ergodicity, and uncertainty quantification in PDEs. Key themes include random data Cauchy theory, ergodicity of stochastic PDEs, singular stochastic dispersive equations, and applications to engineering uncertainty analysis. Recent publications highlight his contributions to stochastic hydrodynamic stability, surrogate modeling for nuclear energy systems, and ergodic theory for dispersive equations. His work bridges rigorous PDE analysis with practical applications in turbulence and nuclear reactor design. Mechanical & Aerospace Engineering Teacher of the Year, Utah State University (2019) NSERC Graduate Scholarship (2006) Richards has secured significant research funding including the NRC Faculty Development Grant (2019-2022) as Co-PI and NSF Conference Grant (2017). He teaches with active learning strategies, covering courses from first-year Calculus to advanced research topics.

• Differential Equations
• Nonlinear Dynamics
• Dynamical Systems
• +۷ مورد دیگر

Lev Kapitanski is a Professor in the Department of Mathematics at the University of Miami's College of Arts and Sciences. His research spans differential equations, nonlinear dynamics, Sobolev spaces, and mathematical biology. Key contributions to semiflow selection theorems and Markov selection Pioneering work on dynamics with discrete-to-continuous transitions Studies on homotopy invariants in low-regularity function spaces Mathematical modeling of vector-borne disease transmission in heterogeneous environments His publications demonstrate expertise in differential inclusions, Hardy inequalities, and attractor theory in both discrete and continuous frameworks. Current research focuses on deterministic switching systems, epidemiological modeling, and topological methods in nonlinear analysis. Collaborative work includes mathematical physics applications in the Faddeev model and spatial disease dynamics. Notable grants include National Institutes of Health (NIH) and National Science Foundation (NSF) funding for vector-borne disease research. Publications frequently appear in top journals like Nonlinearity and Communications in Contemporary Mathematics .

Orna Kupfermanمشاهده پروفایل

استاد

• Formal Verification
• Synthesis of Computer Systems
• Automata Theory
• +۲ مورد دیگر

Orna Kupferman is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. She received her PhD in 1995 from the Technion and has established herself as a leading researcher in theoretical computer science with a focus on formal methods. Her research centers on the theoretical foundations of formal verification and synthesis of computer systems, with special emphasis on automata theory and game theory applications. Kupferman's work bridges abstract theoretical concepts with practical verification challenges, developing frameworks for analyzing and constructing reliable computer systems. Her research has consistently explored the intersection of logic, computation, and strategic behavior in system design. Analysis of her recent publications reveals a strong focus on game-theoretic approaches to system verification and synthesis, with increasing attention to privacy concerns, weighted objectives, and uncertainty modeling. Her work spans multiple prestigious venues including ATVA, CONCUR, TACAS, and ICALP, demonstrating both depth and breadth in theoretical computer science. Professor Kupferman actively contributes to the academic community through teaching courses such as Formal Verification of Reactive Systems and maintaining a robust publication record that continues to grow with significant contributions through 2025. She is also engaged in broader societal issues, as evidenced by her participation in Israel's democracy movement.

Alistair Sinclairمشاهده پروفایل

استاد

• Randomized Algorithms
• Markov Chain Monte Carlo
• Statistical Physics
• +۳ مورد دیگر

Alistair Sinclair is the Kikuo Ogawa and Kaoru Ogawa Professor of Computer Science in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his BA in Mathematics from the University of Cambridge (1982) and PhD in Computer Science from the University of Edinburgh (1988). After briefly serving on faculty at Edinburgh, he joined UC Berkeley in 1994. Sinclair has held visiting positions at DIMACS, Princeton University, Rutgers University, Microsoft Research, École Polytechnique, University of Paris-Orsay, and University of Rome III. His research explores: Randomized algorithms and Markov chain Monte Carlo methods Phase transitions in statistical physics Algorithmic applications of stochastic processes Nonlinear dynamical systems Combinatorial optimization Analysis of Sinclair's recent publications (2017-2025) reveals strong emphasis on statistical physics models (especially Ising and random-cluster systems), Markov chain dynamics, phase transitions, and algorithmic solutions for combinatorial problems. Key methodologies include spatial mixing analysis, entropy decay measurements, and deterministic approximation techniques. Scientific Awards: 1996 ACM-EATCS Gödel Prize 2006 Fulkerson Prize 2017 SIGACT Distinguished Service Prize Sinclair has advised 17+ PhD students including notable researchers in theoretical computer science and mathematics. He served as Founding Associate Director (2012-2017) of the Simons Institute for the Theory of Computing, receiving recognition for developing its research programs on probability, geometry, and computational complexity.

• Industrial Engineering
• Applied Optimization
• Simulation Models
• +۹ مورد دیگر

Miguel Vieira is an Assistant Professor at the Faculty of Engineering , Lusófona University, Lisbon, and a Researcher at RCM2+. He also holds Visiting Researcher positions at Instituto Superior Técnico and Universidade de Coimbra. PhD in Leaders for Technical Industries (2017) from Instituto Superior Técnico (part of MIT Portugal Program) Focus on industrial engineering challenges, including optimization methods, simulation models, and machine learning for supply chain and production systems Developed mathematical programming models (LP, IP, MILP, MINLP) and decomposition methods for complex manufacturing problems His research spans deterministic and stochastic scenarios, with a focus on Industry 4.0, human-robot interaction, and decision-support systems. He has published 8 journal articles, 11 book chapters, and contributed to 7 events, including organizing 7 conferences. Recent publications emphasize data-driven optimization , risk assessment , and sustainable strategies in biomass and fuel distribution supply chains, alongside machine learning integration in manufacturing. His work includes a Q-Learning algorithm for assembly lines and stochastic programming applications. Awards & Honors : 6 scientific awards (specifics not listed) Miguel has supervised 3 MSc dissertations and collaborated with 53 researchers. His projects include risk assessment in process design, dynamic scheduling for Industry 4.0, and enhancing industrial optimization via deep learning.

Bruno Gaujalمشاهده پروفایل

استاد پژوهشی

• Performance Evaluation
• Optimization
• Discrete Event Dynamic Systems
• +۵ مورد دیگر

Bruno Gaujal is a Research Professor at Inria Grenoble-Rhône-Alpes, affiliated with Université Grenoble Alpes. He obtained his PhD from the University of Nice in 1994 under François Baccelli's supervision and has held positions at AT&T Bell Labs, INRIA, and École Normale Supérieure de Lyon. He previously led the MESCAL (now POLARIS) research group focused on large-scale computing until 2015. His research interests center on performance evaluation, optimization, and control of discrete event dynamic systems with stochastic inputs. Specific areas include: Markov Chains and Markov Decision Processes Reinforcement Learning and stochastic optimization Queueing theory and scheduling algorithms Energy-efficient computing in distributed systems Game-theoretic approaches in network optimization Gaujal's recent publications show strong emphasis on reinforcement learning applications in queueing networks, energy optimization for real-time systems, and scalable algorithms for Markov Decision Processes. His work bridges theoretical frameworks like Whittle indices with practical implementations in cloud computing and distributed systems. He has supervised numerous PhD students including Nicolas Gast (now Inria researcher), Anne Bouillard (Huawei researcher), and Emmanuel Hyon (Paris Nanterre professor). Current students include Hélène Arvis and Romain Cravic. Gaujal co-founded RTaW, a startup specializing in real-time network design tools. At Inria, he leads research in the POLARIS group, focusing on optimization methods for large-scale distributed computing infrastructures. His work involves collaborations with 85+ co-authors across institutions globally.

• Global Optimization
• Blockchain Technologies
• Quantum Computing
• +۲ مورد دیگر

Prof. Dr. Remigijus Paulavičius is a Senior Researcher, Professor, and Group Leader at the Blockchain and Quantum Technologies Group of Vilnius University. He earned his Doctor of Science degree in Computer Science in 2010 from Vilnius University's Institute of Mathematics and Informatics, focusing on global optimization with simplex subdomains under the supervision of Dr. J. Žilinskas. Research Interests : His work bridges global optimization methods with blockchain and quantum computing. Key areas include non-convex bilevel programming, derivative-free optimization algorithms (e.g., DIRECT), and applications in blockchain scalability, consensus protocols, and quantum circuit design. Scientific Awards : Best Paper Award, Journal of Global Optimization (2014) Funding from Lithuanian State Science and Studies Foundation for doctoral students (2007–2009) Member, Young Academy of the Lithuanian Academy of Sciences (since 2019) Projects & Leadership : Leads research on blockchain simulators, quantum machine learning frameworks, and optimization toolboxes (e.g., DGO, DIRECTGO). Involved in international collaborations and conference committees, including AIChE and EUROPT. Publications : Over 70 works on global optimization algorithms, blockchain applications, and quantum computing, with recent focus on generative AI, energy consumption in blockchains, and reproducibility in machine learning.

• Modal analysis
• Structural damage diagnosis
• Damping systems
• +۵ مورد دیگر

Stefano Marchesiello is a Full Professor of Applied Mechanics at the Polytechnic University of Turin, Department of Mechanical and Aerospace Engineering (DIMEAS), a position he has held since 2019. His academic work spans theoretical studies, numerical applications, and experimental tests within the field of Applied Mechanics. He maintains active roles in doctoral education, serving on mechanical engineering doctoral colleges from 2013/2014 through 2024/2025, and teaches courses including Dynamics and Identification of Nonlinear Systems, Dynamics of Mechanical Systems, Vibration Mechanics, and Machine Mechanics for Aerospace Engineering. Marchesiello's research focuses on modal analysis and identification, damage diagnosis in structures and construction materials, damping systems, mechanical vibrations, and nonlinear dynamics. His primary research lines include vehicle-bridge dynamic interaction, dynamic identification techniques in linear and nonlinear fields, damage identification, vibrations of continuous systems with non-proportional damping, innovative vibration damping devices, diagnostics and monitoring of rotating systems, and pantograph-catenary dynamic interaction. His work bridges theoretical mechanics with practical engineering applications, particularly in transportation infrastructure and mechanical systems. His recent publications demonstrate a strong focus on nonlinear system identification, structural health monitoring, and vibration analysis across various mechanical and aerospace applications. Marchesiello's research shows increasing integration of machine learning techniques with traditional mechanical engineering approaches, particularly in system identification and damage detection. His work spans from fundamental nonlinear dynamics to practical applications in railway systems, rotating machinery, and structural components. Certificate of reviewing awarded by Journal of Sound and Vibration - Elsevier, Netherlands (2013) Certificate of Excellence in Reviewing - Mechanical Systems and Signal Processing 2013 awarded by Elsevier, Netherlands (2013) Marchesiello serves as Scientific Director for multiple commercial research contracts, particularly with Officina Fratelli Bertolotti SpA, focusing on vibration damping systems for railway catenaries and rotor dynamics modeling. He has led research projects from 2008 through 2023, demonstrating sustained research leadership and industry collaboration. His editorial work includes membership on the Editorial Board of SHOCK AND VIBRATION since 2018, and he has served on program committees for the International Conference on Damage Assessment of Structures (DAMAS) across multiple years. He is actively involved with the Dynamics of Mechanical Systems and Identification research group (DIMEAS), which focuses on developing advanced methods for analyzing and identifying mechanical systems with both linear and nonlinear behaviors. His research integrates computational modeling, experimental validation, and practical applications across multiple engineering domains.

• Reliability of Mechanical Systems
• Structural Dynamics
• Uncertainty Quantification
• +۳ مورد دیگر

David Moens is Full Professor and Chair of the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology. He leads the Mecha(tro)nic System Dynamics (LMSD) research group at De Nayer Campus, where his research focuses on reliability engineering, structural dynamics, and computational mechanics. Moens serves in multiple leadership roles including membership in the university's Research Policy Council and heads Subdivision 10 of the De Nayer Campus. His research integrates computational mechanics with uncertainty quantification methods to address challenges in mechanical system reliability. Primary research domains include: Non-deterministic numerical analysis techniques for structural systems Fatigue and lifetime prediction methodologies Spatial uncertainty quantification in composite materials Physics-informed machine learning for engineering applications Robust design optimization under uncertainty Moens' recent publications demonstrate strong emphasis on computational uncertainty frameworks with applications spanning composite pressure vessels, manufacturing process twins, and neural network uncertainty estimation. Research consistently intersects finite element analysis, experimental validation, and emerging machine learning techniques to solve reliability challenges in mechanical systems. He leads significant research initiatives including RISQ (Random and Interval Spatial Uncertainty Quantification, 2025-2027) and projects on hydrogen storage vessel reliability (2024-2028). As primary supervisor for multiple PhD candidates, Moens actively contributes to academic training through courses including Reliability of Mechanical Systems, Dynamic Behavior of Machines, and Design for Safety and Reliability.

Arthur G Richardsمشاهده پروفایل

استاد

• Trajectory Optimization
• Unmanned Aerial Vehicles (UAVs)
• Model Predictive Control
• +۴ مورد دیگر

Arthur G Richards serves as Professor of Robotics and Control within the Dynamics and Control department at the University of Bristol's School of Engineering Mathematics and Technology. His research specializes in trajectory optimization for aerospace applications, focusing on UAV autonomy, spacecraft rendezvous, and air traffic management through advanced optimization techniques. His educational foundation includes an M.Eng. from the University of Cambridge and S.M./Ph.D. degrees from MIT. Research interests center on solving complex aerospace challenges through: Non-convex optimization for obstacle avoidance in cluttered environments Robust model predictive control for real-time disturbance compensation Distributed optimization enabling large-scale vehicle cooperation Scalable algorithms for high-traffic scenarios with minimal fuel consumption Analysis of his 132 research outputs reveals evolving emphasis on reliability-aware UAV path planning, interpretable reinforcement learning for aircraft control, and swarm robotics with real-world validation. Recent work increasingly integrates machine learning with traditional control theory while addressing practical constraints like sensor noise and system failures. Professor Richards has supervised 22 research students and secured funding for 11 projects, including the active Aerial Robotics for Search and Rescue (2022-2026) and PORTAL (2022-2024) initiatives. His industry collaborations with Thales and focus on technology transfer demonstrate strong academic-industrial integration. He actively contributes to the Smart Networks for Sustainable Futures and Robotics research groups, developing frameworks for multi-robot systems that balance theoretical rigor with practical deployment requirements in conservation, inspection, and exploration scenarios.

• Plant Safety
• Risk Analysis
• Fire Protection Engineering
• +۷ مورد دیگر

Professor Ulrich Krause serves as Chair of Plant Engineering and Plant Safety at Otto von Guericke University Magdeburg's Faculty of Process and Systems Engineering since 2011. His research establishes scientific foundations for plant safety through advanced mathematical and experimental methods, addressing critical gaps in technology risk assessment. 1980-1985: Dipl.-Ing. in Fluid Mechanics and Technical Thermodynamics, TU Dresden 1989: Dr.-Ing. on non-equilibrium two-phase flow, TU Dresden 2001: Habilitation on heat/mass transfer in dust explosion safety, TU Dresden 2001: Habilitation in Safety Engineering, TU Berlin His work integrates deterministic and probabilistic safety analysis for industrial plants, focusing on fire/explosion prevention in chemical facilities, recycling plants, and bulk material storage. Research spans combustion dynamics of solids/liquids, self-ignition mechanisms, silo/hazard simulations, and explosion protection system validation through experimental and computational fluid dynamics approaches. Professor Krause leads research groups in Renewable Energies and Fluidized Bed Technology, collaborating with Berlin Fire Department, Siemens AG, BAM Federal Institute, TU Dresden, Dräger Safety, and ZVEI electronics association to translate theoretical safety concepts into industrial applications.