جست‌وجوی استاد و پژوهشگر

• Static Analysis
• Abstract Interpretation
• Fixpoint Algorithms
• +۱ مورد دیگر

Sarah Tilscher, M.Sc., is a researcher at the Chair for Languages and Structures in Computer Science at the Technical University of Munich (TUM). She contributes to teaching tutorials for courses including Advanced Concepts of Programming Languages and Compiler Construction , and has supervised seminars on static analysis and programming paradigms. Her research focuses on: Incremental and interactive static analysis using abstract interpretation Verification of fixpoint algorithms in Isabelle theorem prover Development of the Goblint static analyzer for concurrent C programs Formal methods for program correctness and verification Her publications (11 recent works) predominantly explore static analysis techniques, abstract interpretation frameworks, and verification tools. Key themes include top-down solvers, weak relational domains, parser generators, correctness witnesses, and concurrency modeling, primarily applied to programming languages and compiler design. She actively supervises student theses on topics including incremental static analysis, visualization of analysis results, and verification of fixpoint algorithms. Current opportunities include master's projects on verified top-down solvers and bachelor's projects on solver code generation. As a core member of the Goblint project team, she develops static analysis tools for memory safety and termination verification, collaborating with TUM researchers and external partners.

• Human Computer Interaction
• Multi-modal Interaction
• Robotics
• +۱ مورد دیگر

Julian Wolter is a Doctoral Researcher at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany, affiliated with the Ubiquitous Media Technology Lab (UMTL) at the Saarland Informatics Campus. His work spans Human-Computer Interaction, Multi-modal Interaction, Robotics, and Chatbots, with emphasis on practical applications in human-robot collaboration and physiological monitoring systems. Wolter's research centers on developing accessible technologies using minimal resources, such as off-the-shelf cameras for real-time exercise feedback in rehabilitation settings. He has led projects including Green-AI Hub and aquaChat, and currently contributes to KIgÜBA, FedWell, RZzKI, Intel RESUMAIS, and stepDP. His methodology focuses on bridging theoretical AI with deployable solutions under resource constraints. With two recent publications in human-robot interaction (2023) and speech-based robot control (2020), Wolter demonstrates consistent scholarly output in practical robotics applications. His research trajectory shows increasing focus on responsible AI systems and sustainable computing, aligning with current European AI ethics frameworks. Wolter actively supervises students through Master's practical assignments and thesis projects, requiring technical proficiency in visual computing or machine learning. He has secured continuous project funding, including ongoing grants for five major initiatives, reflecting strong research support and institutional confidence. As a core member of UMTL, Wolter collaborates within a 30+ researcher team at DFKI, contributing to the lab's reputation in adaptive interfaces and human-centered AI. The lab's integration within Saarland Informatics Campus provides access to world-class computing infrastructure and interdisciplinary research networks.

Alex Rees-Jonesمشاهده پروفایل

دانشیار

• Behavioral Public Finance
• Market Design
• Reference Dependence
• +۴ مورد دیگر

Alex Rees-Jones is an Associate Professor in the Business Economics and Public Policy Department at the Wharton School, University of Pennsylvania. He is affiliated with the National Bureau of Economic Research (NBER) as a Research Associate and the Leonard Davis Institute as a Senior Fellow. His research bridges behavioral economics with public finance and market design, focusing on how psychological biases affect policy analysis and market outcomes. Ph.D. in Economics, Cornell University (2013) Postdoctoral Fellowship, NBER (2014) His work examines public finance through the lens of imperfect tax responses and behavioral biases , and explores market design in contexts like school assignment and residency matching. He has contributed to journals such as the American Economic Review, Review of Economic Studies, and Management Science. Recent publications highlight his focus on loss aversion , reference dependence , and cognitive constraints in policy evaluation. His studies often combine theoretical modeling with empirical or experimental approaches. Scientific Awards and Affiliations Research Associate, National Bureau of Economic Research Senior Fellow, Leonard Davis Institute Board of Editors, American Economic Review Associate Editor, Management Science Alex teaches undergraduate and MBA courses on Public Finance and Policy, as well as Ph.D.-level courses on public economics and behavioral economics applications.

Thomas Vogelمشاهده پروفایل

پژوهشگر

• Software Engineering
• Self-Adaptive Systems
• Autonomous Systems
• +۲ مورد دیگر

Thomas Vogel is a postdoctoral researcher at the Software Engineering Group within the Institute of Computer Science at Humboldt-Universität zu Berlin . From October 2021 to September 2022, he served as a stand-in professor for Empirical Software Engineering at Paderborn University. He earned his Ph.D. summa cum laude in 2018 from the University of Potsdam under the Hasso Plattner Institute, specializing in model-driven engineering of self-adaptive systems. He graduated with distinction in Information Systems from the University of Bamberg .

Mirjam Meijerمشاهده پروفایل

استادیار

• Inventory Control
• Supply Chain Optimization
• Supply Chain Coordination
• +۴ مورد دیگر

Mirjam Meijer is Assistant Professor of Operations Management at Kühne Logistics University in Hamburg, Germany since June 2022, following a Visiting Scholar position at the same institution in 2021. Her academic foundation includes a PhD from Eindhoven University of Technology and master's/bachelor's degrees in Econometrics and Operations Research from Erasmus University Rotterdam. Educational background: PhD in Operations Management and Logistics, Eindhoven University of Technology (2017-2022) Master of Science in Econometrics and Management Science, Erasmus University Rotterdam (2016-2017) Bachelor of Science in Econometrics and Operations Research, Erasmus University Rotterdam (2013-2016) Semester abroad, Norwegian University of Science and Technology (2015) Campus Scholarship Program, Fulbright Center (2012-2013) Her research focuses on inventory control and supply chain optimization for high-tech manufacturers, utilizing stochastic optimization , game theory , and queueing theory to solve real-world coordination challenges. She specializes in single-sourcing dynamics and multi-generation product development cycles unique to semiconductor and electronics industries. Recent publications reveal a methodological evolution toward applying extreme-value theory to large-scale assembly systems and developing innovative contract structures like contingent renewal mechanisms. These works bridge theoretical operations research with practical logistics constraints, particularly in optimizing capacity investments and mitigating component shortage risks. Scientific recognition includes: Fulbright Scholarship (awarded for UCLA Anderson School of Management collaboration, cancelled due to COVID-19 pandemic) Meijer has supervised graduation projects in Industrial Engineering at Eindhoven University of Technology and teaches stochastic operations management. Her conference presentations at INFORMS, MSOM, and International Symposium on Inventory Research demonstrate active engagement with the global operations research community. While specific grant details are unreported, her Fulbright award supported intended cross-institutional research. Though lab affiliations aren't specified, her extensive collaborations with scholars like Willem van Jaarsveld, Ton de Kok, and Christopher S. Tang indicate participation in major supply chain research networks focused on high-tech manufacturing challenges.

Ira Neitzelمشاهده پروفایل

استاد

• PDE-Constrained Optimization
• Numerical Analysis
• Optimal Control Theory
• +۵ مورد دیگر

Professor Ira Neitzel leads the Research Group for Optimization with Partial Differential Equations at the Institute for Numerical Simulation, Faculty of Mathematics and Natural Sciences, University of Bonn. Her research focuses on PDE-constrained optimal control problems, numerical analysis, and phase-field fracture modeling. She maintains active collaborations with institutions including TU Darmstadt and Leibniz University Hannover. Her primary research interests include: PDE-Constrained Optimization Numerical Analysis of Optimal Control Problems Regularization Approaches for Nonsmooth Control Fracture Propagation Modeling using Phase-Field Methods Recent work emphasizes quasilinear parabolic PDEs, state constraints, and sparse control applications in fracture mechanics. Analysis of her 15 most recent publications (2019-2025) reveals consistent focus on: Mathematical foundations of phase-field fracture control Numerical methods for state-constrained problems Convergence analysis of SQP algorithms Error estimation in finite element discretization Key journals include SIAM Journal on Control and Optimization, ESAIM: COCV, and Numerische Mathematik. Her scientific contributions include leadership in DFG Priority Program SPP 1962 and organization of the "Women in Optimization" workshop series. Notable projects include "Optimizing Fracture Propagation Using Phase-Field Approach" (SPP 1962) and "Sparse Controls in Optimization of Quasilinear PDEs" (SFB 1060). Professor Neitzel advises doctoral researchers including Andreas Hehl and Konstantinos Dimitriou. She teaches advanced courses in Numerical Algorithms, Scientific Computing, and Graduate Seminars on Numerical Simulation. Her group maintains active participation in the International GAMM Activity Group on Optimization with PDEs.

• Optimal Control Theory
• Differential Games
• Mean Field Games
• +۵ مورد دیگر

Pierre Cardaliaguet is a Professor at Ceremade (UMR CNRS 7534), Paris-Dauphine University, where he conducts research in mathematical analysis with a focus on game theory and control systems. His work bridges theoretical mathematics with practical applications in economics, traffic modeling, and financial markets. Research Interests: Optimal Control Theory - Developing theoretical frameworks for optimal decision-making in dynamic systems Differential Games - Analyzing strategic interactions between multiple decision-makers over time Mean Field Games - Studying large population games where individual behavior influences and is influenced by aggregate behavior Homogenization Theory - Investigating the behavior of materials with complex microstructures Hamilton-Jacobi Equations - Exploring the mathematical properties of these fundamental PDEs that arise in control and game theory Recent Publication Trends: Cardaliaguet's recent work demonstrates a strong focus on Mean Field Games theory, with particular attention to the master equation, convergence problems, and connections to physical phenomena like aggregation and flocking. His research combines deep theoretical analysis with practical applications in traffic flow modeling and financial markets. The publications reveal sophisticated mathematical techniques involving viscosity solutions, weak KAM theory, and stochastic homogenization applied to increasingly complex game-theoretic settings. Teaching Activities: Course on Stochastic Control Course on Mean Field Games Traffic models on a Junction: From Micro to Macro Optimization and Dynamic Programming Functional Analysis and PDEs

Mickaël LAURENTمشاهده پروفایل

پژوهشگر

• Dynamic Programming Languages
• Type Systems
• Set-Theoretic Types
• +۹ مورد دیگر

Mickaël LAURENT is a French researcher in computer science currently working at Charles University with Jan Vitek. He completed his PhD in 2024 at Université Paris-Cité under Giuseppe Castagna and Kim Nguyen, focusing on polymorphic type inference for dynamic languages. His research combines parametric polymorphism, ad-hoc polymorphism, and subtyping in set-theoretic type systems. Affiliation: Charles University (Prague, Czech Republic) Previous Affiliation: Université Paris-Cité (IRIF & LMF) Research Interests span dynamic language type systems, occurrence typing, set-theoretic types, and traceability analysis. His work integrates union elimination, intersection type introduction, and type narrowing techniques to create sound and terminating type reconstruction algorithms. Scientific Awards : 1st Place, 2025 Thesis Award (GDR GPL) Teaching Experience includes courses on network programming (TCP/UDP sockets), binary machine principles (IEEE-754, cryptography), object-oriented programming in Java, web development (HTML/CSS/PHP), and Python programming for mathematics students. He also contributes to outreach programs like Fête de la Science. Projects include a type checker prototype for dynamic languages, the Modular Set-Theoretic Types Library (SSTT), Arbitrary Code Execution tools for Pokémon games, and a level editor for Kuru Kuru Kururin. His work bridges academic research with practical reverse engineering challenges.

David Monniauxمشاهده پروفایل

پژوهشگر

• program verification
• abstract interpretation
• static analysis
• +۸ مورد دیگر

David Monniaux is a senior researcher (directeur de recherche) at CNRS and an adjunct professor at École polytechnique. He works at VERIMAG, a computer science laboratory jointly operated by CNRS and the University of Grenoble. Dr. Monniaux obtained his PhD in 2001 from Université Paris Dauphine under Professor Patrick Cousot, with a dissertation on the static analysis of probabilistic programs by abstract interpretation. He later earned his habilitation in computer science in 2009 from Université Joseph Fourier, Grenoble, and also holds an agrégation in mathematics. Monniaux's research focuses on program verification, with particular emphasis on proving software correctness. His work spans theoretical foundations in computability theory and practical applications in safety-critical systems. He has made significant contributions to abstract interpretation, static analysis, and the verification of numerical properties in programs. His research bridges computer science theory with practical engineering challenges, particularly in the context of critical embedded systems where software failures can have severe consequences. His work connects to diverse fields including game theory, algebra, and convex optimization. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques. Key themes include polyhedral approximation, program analysis with local policy iteration, abstraction of arrays and maps, synthesis of ranking functions, and computing worst-case execution times. These works collectively advance the field of program verification by addressing challenges in handling nonlinear constraints, branching, and complex data structures while maintaining computational feasibility. Monniaux has supervised several students including Julien Henry, Alexis Fouilhé, George (Egor) Karpenkov, and Alexandre Maréchal (now at LIP6), with current students Hang Yu and Valentin Touzeau. He has led significant research projects including VERASCO (2012-2015), which aimed at integrating a static analyzer into the CompCert certified compiler, and STATOR (2012-2017), an ERC starting investigator grant exploring advanced techniques for automatic inference of program invariants. At VERIMAG, Monniaux is part of a vibrant research community focused on critical systems. His work connects with broader efforts in formal methods, with applications in aviation, automotive systems, and other safety-critical domains where software reliability is paramount.

Annie Liuمشاهده پروفایل

استاد

• Programming Languages
• Algorithms
• Systematic Design and Optimization
• +۶ مورد دیگر

Annie Liu serves as Professor of Computer Science at Stony Brook University, holding a PhD from Cornell University alongside prior degrees from Peking and Tsinghua Universities. Her research centers on systematic methods for language and algorithm design, with emphasis on optimization frameworks applicable across critical computing domains. Her educational credentials include: BS in Computer Science from Peking University MEng in Computer Science from Tsinghua University PhD in Computer Science from Cornell University Liu’s research program investigates systematic analysis and transformation techniques for design optimization, particularly through incrementalization—the discrete counterpart of calculus differentiation. Her work spans optimizing compilers , interactive environments , database systems , distributed computing , and security , with projects addressing modeling, verification, code generation, and testing for real-time systems, semantic Web, and big data applications. The Design and Analysis Research Laboratory she directs bridges theoretical foundations with practical implementations. Analysis of her 2012-2025 publications reveals an evolution from foundational distributed algorithm design toward unifying frameworks for solver semantics and assurance. Key trends include increasing integration of incremental computation principles across compiler optimizations, database queries, and distributed system reliability. Her scientific recognition includes: State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities Professor Liu has delivered over 100 international talks and maintains active conference leadership, serving on program committees for POPL, PEPM, and SPLASH through 2026. While specific grant details aren’t provided, her sustained involvement in high-impact venues like PADL and LPOP demonstrates significant research influence. She directs the Design and Analysis Research Laboratory where interdisciplinary teams advance optimization methodologies for emerging computational challenges.

Erika Ábrahámمشاهده پروفایل

استاد

• Computer Science
• Satisfiability Checking
• SMT Solving
• +۷ مورد دیگر

Erika Ábrahám is a Full Professor at RWTH Aachen University , Germany, leading the Theory of Hybrid Systems research group. Her academic journey includes positions as a Junior Professor (2008-2013) and postdoctoral researcher at institutions like Jülich Research Centre and Albert-Ludwigs-University Freiburg. Her research interests span formal methods, SMT solving, hybrid systems verification, and probabilistic systems. She has contributed to symbolic computation, railway timetables, and probabilistic hyperproperties, with recent work focusing on cylindrical algebraic decomposition, conflict-driven search algorithms, and stochastic modeling. Scientific contributions include 15+ articles (2021-2025) on topics like probabilistic hyperproperties , hybrid automata , and symbolic arithmetic , often published in LNCS, Springer, and Elsevier venues. She has collaborated on tools like HyPro and SMT-RAT, and edited proceedings for conferences including NASA Formal Methods and QEST.

Clark Barrettمشاهده پروفایل

استاد

• Formal Methods
• Automated Reasoning
• Verification
• +۷ مورد دیگر

Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.

Nikolaj Bjørnerمشاهده پروفایل

پژوهشگر

• Formal Methods
• Automated Reasoning
• SMT Solving
• +۵ مورد دیگر

Nikolaj Bjørner is a Principal Researcher at Microsoft Research , renowned for his foundational contributions to automated reasoning and formal verification. He is the co-creator and lead architect of the award-winning Z3 SMT solver , one of the most widely used tools in formal methods and program analysis. His research interests lie at the intersection of formal methods , programming languages , and automated reasoning . Specific areas include: SMT solving – theory solvers, quantifiers, and solver architectures Program verification – symbolic execution, model checking, and scalable analysis Constraint solving – linear arithmetic, strings, bit-vectors, and custom theories Network and cloud verification – configuration synthesis and policy checking Bjørner's recent work emphasizes scalable verification techniques for complex systems including cloud configurations, parameterized protocols, and sparse code optimizations. His publications span foundational theory, tool design, and real-world applications. He has served on the program committees of premier conferences such as POPL , PLDI , VMCAI , ASE , and SPLASH , shaping the direction of the field. He is a frequent invited speaker and tutorial presenter, including at PADL 2020 and POPL 2023 TutorialFest .

Luitpold Babelمشاهده پروفایل

استاد

• Discrete Mathematics
• Structural and Algorithmic Graph Theory
• Combinatorial Optimization
• +۶ مورد دیگر

Luitpold Babel is a Professor of Mathematics and Computer Science at the Faculty of Business Administration, University of the Federal Armed Forces Munich. He has been serving in this position since 2007 and continues to be actively involved in teaching and research as evidenced by his Spring Term 2025 course offerings including Fundamentals of Computer Science, Scientific Computing with Matlab, and Engineering Mathematics tutorials. His educational background includes: 1982-1987: Studied mathematics at the Technical University of Munich 1987: Diploma (with distinction) 1990: Doctorate (with distinction) 1997: Habilitation Professor Babel's research has evolved significantly over his career, beginning with theoretical work in discrete mathematics and graph theory before transitioning to applied defense technology research. His current focus spans operations research applications in military technology, particularly UAV route planning, missile guidance systems, and logistics optimization. He has developed expertise in translating complex mathematical concepts into practical engineering solutions for defense applications, with particular emphasis on kinematic constraints in path planning and risk assessment in navigation. An analysis of his publication trends reveals a clear shift from pure graph theory (1990-2005) toward increasingly applied research in aerospace engineering and defense technology (2010-present). His recent work demonstrates sophisticated integration of mathematical optimization with real-world constraints in military applications, with growing emphasis on cooperative systems, real-time replanning, and decentralized decision-making for missile and UAV fleets. His contributions have been recognized with: Teaching Award of the University of the Bundeswehr Munich (2015), awarded by the Student Convention Study Award of the German Society for Defense Technology eV Professor Babel has supervised over 30 master's and bachelor's theses, primarily focusing on defense-related applications of mathematics and computer science. His research has been supported by substantial external funding from the Federal Ministry of Defense and through multiple industry partnerships with major defense contractors including MBDA Deutschland GmbH and RAM-System GmbH. Current projects include Decentralized Flight Path Planning (2023-2025) and Studies on Technical-Logistical Tasks (2021-2024), demonstrating his continued active engagement in cutting-edge defense research.

Kai Zhouمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Heavy Ion Physics
• +۴ مورد دیگر

Dr. Kai Zhou is a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS) in the Theoretical Sciences division, where he leads the 'Deepthinkers' research group. Born in China in 1987, he completed his B.Sc. in Physics from Xi'an Jiaotong University in 2009 and earned his PhD with 'Wu You Xun' Honors from Tsinghua University in 2014. After postdoctoral research at Goethe University Frankfurt's Institute for Theoretical Physics, he joined FIAS in August 2017 as a Research Fellow focusing on Deep Learning applications in physics. Frankfurt Institute for Advanced Studies (FIAS), Theoretical Sciences (2017-present) Goethe University Frankfurt, Institute for Theoretical Physics (Postdoc) Tsinghua University, Physics (PhD, 2014) Xi'an Jiaotong University, Physics (B.Sc., 2009) Dr. Zhou's research bridges artificial intelligence and theoretical physics, with a focus on applying machine learning techniques to complex physical systems. His work spans heavy-ion collisions, lattice quantum field theory, seismology, and renewable energy systems. He has developed innovative deep learning approaches to extract physical insights from complex data, including constructing an Equation-Of-State meter for heavy ion collisions. His research demonstrates how physics can inform AI development while AI enhances our understanding of physical phenomena. Analysis of Dr. Zhou's recent publications reveals a strong trend toward integrating physics principles with machine learning architectures. His work increasingly focuses on Bayesian inference methods applied to QCD phase transitions, physics-informed neural networks for solving inverse problems in nuclear physics, and developing specialized architectures that preserve physical symmetries. The interdisciplinary nature of his research is evident in applications spanning from heavy-ion collisions to neutron star physics and industrial process optimization. Wu You Xun Honors (PhD) Third party funding through Samson AG: AI for science BMBF funding within ErUM data program: Deep Learning for CBM computing DAAD exchange program Xidian-FIAS International Joint Research Center: AI for science BMWI: AI for energy Nvidia: GPU Grant Dr. Zhou actively mentors doctoral and master's students, currently advising seven graduate students across multiple institutions. His research group 'Deepthinkers' has secured significant third-party funding from diverse sources including industrial partners (Samson AG), government agencies (BMBF, BMWI), and international collaborations (DAAD, Xidian University). His approach combines theoretical physics with cutting-edge AI techniques to address complex problems in both fundamental science and industrial applications. The 'Deepthinkers' research group operates at the intersection of AI and physics, developing novel methodologies that leverage physical principles to enhance machine learning and vice versa. Their work on applying deep learning to heavy-ion collisions represents a significant advancement in extracting meaningful physical insights from complex collision data. The group maintains strong international collaborations, particularly with Chinese institutions through the Xidian-FIAS International Joint Research Center.