جست‌وجوی استاد و پژوهشگر

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +۴ مورد دیگر

Jaume Martí Farré is a researcher at the Technical University of Catalonia (Universitat Politècnica de Catalunya) , affiliated with the School of Mathematics and Statistics (FME) and the Department of Mathematics . He is a member of the COMBGRAPH (Combinatorics, Graph Theory, and Applications) and OMGRAPH (Optimization Methods on Graphs) research groups. Education: PhD in Mathematics from Universitat de Barcelona , thesis on symbolic power filtrations and their algebraic properties. Research Interests: Martí Farré's work spans combinatorics, graph theory, and commutative algebra, focusing on structures like matroids, clutters, and dominating sets. His studies include network optimization, immune sets in infection dynamics, and symbolic power algebra. He applies graph theory to telecommunications and uses algebraic methods to analyze discrete structures. Article Trends: His recent publications (since 2023) emphasize graph decomposition, matroid theory, and network optimization. Earlier works (2015–2019) explore hypergraphs, uniform clutters, and their connections to dominating sets. Older research (1994–1999) delves into algebraic topics like symbolic powers and local cohomology. Scientific Awards: Recognition of Special Quality Teaching Merits (2023) Advising & Projects: He supervised the PhD thesis of José Luis Ruiz Muñoz (2015). Martí Farré participates in competitive R+D+I projects like AGRUPS 2024 and OMGRAPH , focusing on graph-based optimization and network analysis. Collaborations include researchers such as Josep Fàbrega and Marisa Mora .

Olivier Buffetمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Automated Planning and Scheduling
• Reinforcement Learning
• +۷ مورد دیگر

Olivier Buffet is a Researcher at INRIA, working at the INRIA Center at Université de Lorraine / LORIA since November 2007. He is affiliated with the LORIA laboratory (Lorraine Laboratory of Computer Science and its Applications), which focuses on computer science research. His work spans multiple institutions, having previously held positions at NICTA's Statistical Machine Learning program (2004-2006), RSISE at ANU (2004-2006), and LAAS at CNRS (2006-2007). Dr. Buffet received his engineering degree from Supélec and a DEA (Diplôme d'Etudes Approfondies) from Henri Poincaré University. He completed his PhD in computer science under the supervision of François Charpillet and Alain Dutech at LORIA / INRIA Nancy Grand-Est, defended on September 10, 2003. He later defended his habilitation to supervise research (HDR) on December 18, 2017. Dr. Buffet's research focuses on artificial intelligence, particularly in the areas of automated planning and scheduling, reinforcement learning, and decision-making under uncertainty. His work extensively explores Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and Decentralized POMDPs (Dec-POMDPs), with applications ranging from multi-agent systems to traffic management and adaptive conservation strategies. His research often bridges theoretical foundations with practical applications, developing algorithms that can handle complex decision problems in uncertain environments. His publication record demonstrates a consistent focus on advancing methods for planning and decision-making under uncertainty. Over the past decade, his work has increasingly addressed decentralized and multi-agent settings, developing novel approaches for coordination among multiple decision-makers with partial information. More recently, his research has explored interpretable solutions for adaptive management problems, particularly in environmental contexts, and advanced theoretical understanding of properties like Lipschitz continuity in POMDP value functions. Dr. Buffet has received recognition for his contributions to the field, including: Winner of the probabilistic track in the Fifth International Planning Competition (IPC-06) Best Paper award at AAMAS-14 for "Exploiting separability in multi-agent planning with continuous-state MDPs" Best Paper award at JFSMA-13 for "Synchronisation de véhicules autonomes aux croisements d'un réseau de routes" Best Paper award at CAp'11 for "Une extension des POMDP avec des récompenses dépendant de l'état de croyance" As an educator and mentor, Dr. Buffet has supervised numerous PhD students including Arnaud Glad, Mauricio Araya-Lòpez, Mohamed Tlig, Arsène Fansi, and Manel Tagorti. He has also guided many interns and research projects. His teaching experience includes tutored sessions on discrete and deterministic optimization, decision making under uncertainty, and computer science for industrial engineering at École des Mines de Nancy, as well as courses on Unix shell and C programming at Université Henri Poincaré. Dr. Buffet has been actively involved in the academic community, serving as Co-Conference Chair of the 30th International Conference on Automated Planning and Scheduling (ICAPS 2020) in Nancy. He has organized multiple meetings of the French workgroup JFPDA (formerly PDMIA) and chaired several workshops on planning and scheduling under uncertainty. He previously served on the editorial boards of Revue d'Intelligence Artificielle (RIA) and Journal of Artificial Intelligence Research (JAIR), and has been a reviewer for numerous prestigious journals and conferences in artificial intelligence.

Hubertus Th. Jongenمشاهده پروفایل

استاد

• Nonlinear Optimization
• Parametric Optimization
• Semi-Infinite Programming
• +۱۰ مورد دیگر

Hubertus Th. Jongen is a full Professor of Mathematics at RWTH Aachen University and has held an ALMA Professorship at Maastricht University since 2001. He is affiliated with the Department of Mathematics within the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen. His academic career includes professorships at the University of Hamburg and visiting positions at institutions such as the Humboldt University of Berlin, University of Rome, Tokyo Institute of Technology, and ICSI Berkeley. Full Professor, RWTH Aachen University (since 1987) ALMA Professor, Maastricht University (since 2001) Professor, University of Hamburg (1980–1982) Special Professor, University of Hamburg (from 1983) Guest Professor, Humboldt University of Berlin (1985) His primary research interests lie in nonlinear optimization, parametric optimization, semi-infinite and generalized semi-infinite programming, bilevel optimization, and global optimization. His work integrates deep theoretical analysis with applications in operations research and mathematical programming. He has made foundational contributions to the structural analysis of optimization problems, stability of feasible sets, and critical point theory. The most recent articles show a consistent focus on advanced topics in optimization, particularly in generalized semi-infinite programming, bilevel problems, and equilibrium models. His publications emphasize structural analysis, stability, smoothing techniques, and computational aspects of nonconvex and constrained optimization. There is a strong trend toward understanding the geometry and topology of feasible sets and the behavior of solution concepts under perturbations. Doctor honoris causa, Shanxi University (2003) Honorary Foreign Member, Mongolian Mathematical Society Miembro Honorario, Sociedad Colombiana de Investigación de Operaciones Visitante Distinguido de la Ciudad Puebla, Mexico Special Issue in Optimization journal (2008) honoring his 60th birthday Professor Jongen has advised numerous PhD students and collaborators, including Oliver Stein, Jan-J. Rückmann, and Vladimir Shikhman. He has been deeply involved in research grants and editorial work, serving as co-editor of major journals such as Journal of Global Optimization , Optimization , and Mathematical Methods of Operations Research . He co-founded the SIGOPT interest group and has organized multiple international conferences. He has also authored influential books on nonlinear optimization and optimization theory. He has led significant academic initiatives, including serving as Chairman of the Department of Mathematics at RWTH Aachen (1992–1994, 1996–1998) and as a Senator of the university (1998–2000). His research group and collaborations form a well-established network in parametric and global optimization.

Felix Voigtlaenderمشاهده پروفایل

استاد

• Mathematics of Machine Learning
• Neural Networks
• Approximation Theory
• +۱۱ مورد دیگر

Felix Voigtlaender is a Professor for Mathematics with a focus on Reliable Machine Learning at the Mathematical Institute for Machine Learning and Data Science, KU Eichstätt-Ingolstadt. He has held previous academic positions at TU Munich as an Emmy Noether Group Leader, at the University of Vienna as a Senior Scientist, and at KU Eichstätt and TU Berlin in research and postdoctoral roles. PhD: RWTH Aachen University, 2015 Master: RWTH Aachen University, 2013 Bachelor: RWTH Aachen University His research lies at the intersection of mathematics and machine learning, with a strong emphasis on the theoretical underpinnings of neural networks. He investigates approximation properties, expressiveness, and the existence of adversarial examples. His work also extends into harmonic analysis, functional analysis, and multiscale systems such as wavelets and shearlets. He is deeply interested in the mathematical foundations of data science, including sampling theory and information-based complexity. The recent publications highlight a consistent focus on the mathematical analysis of neural networks, particularly approximation capabilities and universal approximation theorems in both real and complex domains. His work often provides sharp theoretical bounds and deep insights into the behavior of deep learning models in high-dimensional settings. Friedrich-Wilhelm-Award 2016 for his PhD thesis Felix Voigtlaender has supervised students and is actively involved in teaching and academic development, notably contributing to the launch of a new BSc program in Data Science at KU Eichstätt-Ingolstadt. He has collaborated with prominent researchers such as Götz Pfander, Gitta Kutyniok, and Hartmut Führ, and has been funded through competitive grants like the Emmy Noether program. He leads a research group focused on rigorous mathematical approaches to machine learning challenges. He is affiliated with the Mathematical Institute for Machine Learning and Data Science, a newly founded institute at KU Eichstätt-Ingolstadt, which will be based in Ingolstadt. His team is involved in both theoretical exploration and practical applications of data science methods.

• Dynamical Systems
• Complex Analysis
• Metric Geometry
• +۷ مورد دیگر

Sergiy Merenkov is a Professor in the Department of Mathematics at The City College of New York (CCNY) and a Doctoral Faculty member in the Mathematics Program at the CUNY Graduate Center. He holds offices in MR 316 at CCNY and 4213 at the Graduate Center, with phone numbers 212-650-5147 (CCNY) and 212-817-8561 (GC). His research focuses on dynamical systems, complex analysis, metric geometry, and fractal geometry. Key themes include quasisymmetric maps, conformal dynamics, hyperbolic groups, Sierpiński carpets, and Kleinian group actions. His work often bridges geometric topology, complex variables, and algebraic structures. Recent articles explore topics such as Carathéodory convergence, wandering domains in dynamical systems, and rigidity properties of fractal spaces. His research is supported by grants from the National Science Foundation (DMS-0400636, DMS-2247364, etc.). Merenkov has collaborated extensively with mathematicians like Mario Bonk, Misha Lyubich, and Oded Schramm on projects involving fractal geometry, conformal welding, and geometric group theory. His PhD thesis (Purdue University, 2003) addressed manifold classification via algebraic methods.

Vicenç Torraمشاهده پروفایل

استاد

• Data Privacy
• Approximate Reasoning
• Decision Making
• +۷ مورد دیگر

Vicenç Torra is a Professor at the Department of Computing Science, Umeå University, Sweden. He leads the NAUSICA: PrivAcy-AWare traNSparent deCIsions research group, focusing on artificial intelligence, data privacy, and approximate reasoning. His work bridges computer science and applied mathematics. Research Interests: Data Privacy (differential privacy, k-anonymity, secure multiparty computation) Approximate Reasoning (fuzzy sets, non-additive measures, Choquet integrals) Decision Making and Aggregation Operators Information Fusion and Clustering Recent Article Trends: His publications emphasize data privacy in machine learning, non-additive measure theory applications, and integrating privacy guarantees with mathematical rigor. 2024 works focus on Frechet manifold privacy and LLM knowledge distillation. Scientific Awards: EurAI Fellow (2010) ISI Elected Member (2013) IEEE Fellow (2017) Additional Contributions: Founder of Transactions on Data Privacy and MDAI conference series. Active in editorial boards of Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems, and Information Sciences. Collaborates extensively with institutions in Japan and Spain.

• Theoretical Computer Science
• Algorithms
• Parameterized Complexity
• +۵ مورد دیگر

Yang Liu is a computer scientist specializing in theoretical computer science, particularly in the design and analysis of parameterized and exact algorithms. He has been affiliated with institutions such as Texas A&M University and has collaborated extensively with researchers like Jianer Chen and Songjian Lu. His work primarily focuses on NP-hard graph problems, including feedback vertex set, multiway cut, matching, and packing, where he has contributed improved fixed-parameter tractable algorithms and kernelization techniques. His research lies at the intersection of algorithms, complexity theory, and combinatorics. Key areas include: Fixed-parameter tractability (FPT) Kernelization and measure-and-conquer methods Graph partitioning and structural graph theory Randomized and deterministic exact algorithms Algebraic methods in polynomial testing His publications in top journals such as Journal of the ACM , Algorithmica , and Theoretical Computer Science demonstrate a consistent contribution to foundational algorithmic research. The article trends show a strong focus on solving hard combinatorial problems through novel algorithmic frameworks, often improving time complexity or kernel bounds. Notable scientific contributions include: A landmark 2008 JACM paper proving that the Directed Feedback Vertex Set problem is fixed-parameter tractable. Improvements in kernel sizes for feedback vertex and packing problems. Applications of color-coding and iterative expansion in 3D-matching. While no explicit information about advisees or grants is available, his long-term collaboration network suggests a role in mentoring and team-based research. He has not been associated with any lab or research center in the provided data.

• Scheduling
• Multi-criteria Optimization
• Operational Research
• +۱ مورد دیگر

Dr. Vincent T'KINDT is a Researcher in the Computer Science Department at the Polytechnic School of the University of Tours. He serves as Head of the "Scheduling and Management" axis at the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT, UR 6300) and as Head of Corporate Relations for his department. His research focuses on Scheduling , Multi-criteria Optimization , and Operational Research , contributing to theoretical and applied advancements in these fields. Recent publications highlight his work on Multi-objective scheduling for parallel machines Heuristic algorithms for dynamic scheduling Just-in-Time scheduling models Rescheduling with setup times His research combines exact and approximate methods to solve complex industrial scheduling challenges. He has authored key works in journals like the Journal of Scheduling , Computers & Operations Research , and European Journal of Operational Research , with notable contributions including the book Multicriteria Scheduling: Theory, Models and Algorithms (Springer, 2006). Dr. T'KINDT collaborates with the LIFAT laboratory (UR 6300) and has participated in international conferences such as PMS'08, MISTA'07, and MAPSP2007. His methodological approaches include Integer Programming, Beam Search algorithms, and Pareto optima enumeration.

Charlie Garrodمشاهده پروفایل

عضو هیئت علمی

• distributed database systems
• computer science education
• teaching assistant mentorship
• +۶ مورد دیگر

Charlie Garrod is an Associate Teaching Professor in the Software and Societal Systems Department within Carnegie Mellon University's School of Computer Science. His academic career focuses primarily on undergraduate computer science education and related areas such as teaching assistant and faculty mentorship. Institution: Carnegie Mellon University School: School of Computer Science Department: Software and Societal Systems Department Position: Associate Teaching Professor Dr. Garrod received his B.S. in Computer Science from Harvey Mudd College in 2001 and completed both his M.S. and Ph.D. in Computer Science at Carnegie Mellon University in 2006 and 2008, respectively. His educational background laid the foundation for his expertise in distributed database systems and software engineering education. His research interests center around computer data management systems, with particular emphasis on the performance, scalability, and power management of highly scalable distributed systems. As a teaching professor, his research program is intentionally focused on undergraduate and Master's student research projects rather than large-scale independent research. His work bridges theoretical computer science with practical applications in transportation information systems, mobile location services, and database scalability. Dr. Garrod's publication history reveals a strong trajectory in distributed systems and database technologies, with recent work shifting toward educational topics such as mentoring teaching assistants. His publications span prestigious venues including ACM Transactions on Algorithms, VLDB, ICDE, and SIGCHI, with his work on the Tiramisu transportation system earning a CHI 2011 Honorable Mention. His research demonstrates consistent contributions to database caching, query optimization, and consistency management for web applications. CHI 2011 Honorable Mention for 'Field Trial of Tiramisu: Crowd-Sourcing Bus Arrival Times to Spur Co-Design' As an educator, Dr. Garrod has mentored numerous students through formal courses and undergraduate research projects. He has developed and taught a wide range of courses including software design and engineering, algorithms and data structures, theoretical computer science, distributed systems, and databases. His teaching philosophy emphasizes practical application of theoretical concepts, with a focus on preparing students for real-world software engineering challenges. He has been actively involved in developing pedagogical approaches for computer science education, particularly in the mentorship of teaching assistants. Dr. Garrod maintains an active teaching schedule, currently instructing courses such as 15-210 Parallel and Sequential Data Structures and Algorithms, 17-480/17-780 API Design, and 07-070 Teaching Techniques for Computer Science. His extensive teaching portfolio demonstrates deep commitment to computer science education across multiple institutions and course levels.

Günter Langمشاهده پروفایل

دانشیار

• Empirical Economics
• Environmental and Resource Economics
• Economic Policy
• +۴ مورد دیگر

Prof. Dr. Günter Lang serves as Associate Professor for Applied Economics at Kühne Logistics University (KLU) in Hamburg, where he has been teaching since 2014. His academic journey includes professorial positions at the German University in Cairo, University of Augsburg, University of Klagenfurt, and Technical University Ilmenau, alongside teaching appointments at Warsaw School of Economics and University of Bratislava. With over two decades of academic experience, he has established himself as a prominent researcher in empirical economics. His educational credentials include: Habilitation and venia legendi in Economics from University of Augsburg (1998) Dr. rer. pol. (summa cum laude) from University of Augsburg (1993) Diploma in Economics and Management (M.Sc. equivalent) from University of Augsburg (1989) Prof. Lang's research centers on empirical economic analysis using sophisticated econometric methods to investigate firm technology, management inefficiency, environmental issues, technological change, and economic policy. His work spans environmental economics (particularly climate change impacts on agriculture), labor economics (focusing on immigrant earnings), banking efficiency, and increasingly, digital transformation. According to Google Scholar metrics, his publications have accumulated approximately 1,500 citations, reflecting substantial scholarly impact. His publication trajectory reveals an evolution from traditional economic topics toward contemporary digital economy issues while maintaining his environmental economics foundation. Early work focused on German banking, media markets, and agricultural impacts of climate change, while recent publications examine digitalization's effects on productivity, entrepreneurship, and ICT adoption in developing countries. Methodologically, he consistently employs advanced techniques including stochastic frontier analysis, panel data econometrics, and cost function estimation across diverse geographical contexts from Germany to Egypt and the broader EU. Prof. Lang teaches across KLU's academic programs including Microeconomics and Macroeconomics (Bachelor), Statistics and Econometrics, International Economics, and Economics of Business Strategy (Master), plus Business in a Global Economy and Strategic Decision Making: Game Theory in Business (MBA). His teaching philosophy emphasizes equipping students with analytical tools necessary for addressing complex economic challenges in both academic and professional settings.

Detta Dickinsonمشاهده پروفایل

دانشیار

• Metric Diophantine approximation
• Distribution of conjugate algebraic points
• Simultaneous Diophantine approximation on manifolds
• +۲ مورد دیگر

Detta Dickinson is an Associate Professor in the Department of Mathematics and Statistics at Maynooth University's Faculty of Science & Engineering. Her research focuses on metric Diophantine approximation, particularly the distribution of conjugate algebraic points in spatial regions and simultaneous Diophantine approximation on manifolds. She has published extensively on topics including polynomial discriminants, p-adic metrics, and algebraic number theory. Email: detta.dickinson@mu.ie Recent Publications Trends: Her work spans 2000-2012 with emphasis on: Metric theory of limsup sets Simultaneous approximation in mixed real/p-adic spaces Applications to polynomial curves and surfaces Hausdorff dimension analysis Integer polynomial distribution patterns

• Combinatorial optimization
• Graph theory
• Discrete mathematics
• +۳ مورد دیگر

Cédric Picouleau is a Professor affiliated with the CEDRIC Laboratory at the Conservatoire National des Arts et Métiers (CNAM) in Paris. His research focuses on combinatorial optimization, graph theory, discrete tomography and scheduling, with sustained publication activity from 1994 through 2025. Education: Specific degrees and graduation years are not listed in the available sources. Research interests: Professor Picouleau’s work spans theoretical and applied aspects of graph theory (dominating sets, matchings, graph partitioning, Hamiltonian cycles), discrete tomography (reconstruction of binary matrices and polyominoes), combinatorial optimization (blockers & transversals, degree-constrained problems), and transportation modelling (railway delay prediction). He investigates complexity classifications, designs exact and parameterized algorithms, and studies minimal or extremal graph structures. Publication trends: Recent articles (2018-2025) concentrate on matching-cut and dominating-set complexity, diameter-constrained graph modifications, minimal graph constructions for Hamiltonian & 2-factor extensions, and statistical modelling of train delays. His output appears in journals such as Journal of Graph Theory , Theoretical Computer Science , Discrete Applied Mathematics , and Annals of Operations Research . Scientific awards: No awards, fellowships or honours are mentioned in the supplied material. Students & grants: No list of supervised PhD/Master’s students or specific grant details are provided in the sources. Labs & teams: He is a member of the CEDRIC Laboratory within CNAM, participating in the “Combinatorial Optimization” research axis and regularly collaborates with co-authors from CNAM and other French and international institutions.

Florian Schweigerمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Mathematical Analysis
• Stochastic Processes
• +۴ مورد دیگر

Florian Schweiger is a Postdoctoral Scholar in the Department of Mathematics at the University of Geneva, hosted by Professor Hugo Duminil-Copin. Previously, he was a postdoc at the Weizmann Institute of Science under Ofer Zeitouni. His research centers on probability theory and analysis, with a primary emphasis on stochastic models for interfaces. His educational background includes: BSc in Mathematics from the University of Bonn (2011-2014) MSc in Mathematics from the University of Toronto (2014-2015) MSc in Mathematics from the University of Bonn (2015-2016) PhD in Mathematics from the University of Bonn (2016-2020), supervised by Stefan Müller Dr. Schweiger's research interests encompass probability theory and mathematical analysis. He investigates stochastic interface models, log-correlated fields and their extremes, correlated percolation models, and regularity theory for discrete elliptic operators. His work often addresses the behavior of random surfaces and fields in various regimes. His recent publications demonstrate a strong focus on interface models and log-correlated Gaussian fields. Key contributions include the study of the maximum of log-correlated fields in random environments, pinning phenomena in membrane models, and finite range decompositions of Gaussian fields. These works highlight the interplay between probability and analysis in understanding complex random systems. He is an active member of Hugo Duminil-Copin's research group at the University of Geneva, contributing to the group's efforts in probability theory and statistical mechanics.

Shay Solomonمشاهده پروفایل

دانشیار

• Design and analysis of algorithms
• Graph compression problems
• Dynamic graph algorithms
• +۴ مورد دیگر

Shay Solomon is an Associate Professor in the School of Electrical & Computer Engineering at Tel Aviv University. His research focuses on theoretical computer science, particularly in the design and analysis of algorithms for graph problems, distributed systems, and computational geometry. He coordinates the EE-Systems Department Colloquium and has received funding from prestigious organizations including the European Research Council (ERC), the Israel Science Foundation (ISF), and the US-Israel Binational Science Foundation (NSF-BSF). Professor Solomon's research interests span several key areas in theoretical computer science. He has made significant contributions to dynamic graph algorithms, developing novel techniques for problems like set cover and graph coloring. His work on Euclidean spanners and metric embeddings has advanced our understanding of geometric data structures. In distributed computing, he has explored locality-sensitive approaches to network representation. His research on sublinear algorithms addresses fundamental questions about what can be computed efficiently with limited resources. Across all these areas, his work is characterized by mathematical rigor and practical significance for algorithm design. Analysis of Professor Solomon's recent publications (2023-2025) reveals a strong focus on advancing the state-of-the-art in graph algorithms. His work frequently appears in top theoretical computer science venues including STOC, FOCS, and SODA. A notable trend is his exploration of dynamic algorithms that can efficiently update solutions as input changes, with applications to set cover and graph coloring problems. He has also made significant contributions to the theory of graph spanners in Euclidean and metric spaces, establishing new bounds and constructions. His collaborative work with researchers like Hung Le and Sayan Bhattacharya demonstrates the interdisciplinary nature of modern theoretical computer science research. Professor Solomon acknowledges support from several prestigious funding sources: European Research Council (ERC) Israel Science Foundation (ISF) US-Israel Binational Science Foundation (NSF-BSF) As the coordinator of the EE-Systems Department Colloquium, Professor Solomon plays an active role in academic community building at Tel Aviv University. While specific information about his advisees isn't provided in the source material, his research group likely includes graduate students working on theoretical computer science problems. His extensive publication record suggests an active research program with numerous collaborations across the global theoretical computer science community.

• Autonomous Systems
• Artificial Intelligence Safety
• System Identification
• +۵ مورد دیگر

Laura Lützow is a Researcher at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Cyber Physical Systems group. She earned her bachelor's degree in Mechatronics from TU Ilmenau (2020) and a master's degree in Robotics, Cognition, and Intelligence from TUM (2022). Her work focuses on enhancing the safety and reliability of autonomous systems through set-based system identification, uncertainty quantification, and conformance checking. B.Sc. in Mechatronics, TU Ilmenau (2020) M.Sc. in Robotics, Cognition, and Intelligence, TUM (2022) Laura's research emphasizes formal methods for analyzing and improving the safety of autonomous systems. Her work includes developing mathematical frameworks for reachability analysis and uncertainty quantification, particularly in dynamic environments with complex constraints. Laura's publications highlight advancements in reachability analysis, system identification, and uncertainty quantification. Key themes include conformal prediction for autonomous systems, zonotope reduction techniques, and motion planning under dynamic obstacles. Her work bridges theoretical control systems research with practical applications in robotics and AI safety. She has taught courses on verification, controller synthesis, formal methods, and fundamentals of artificial intelligence at TUM. Additionally, she was a visiting researcher at Stanford University's Intelligent Systems Laboratory (January-July 2025).