جستجوی استاد راهنمای دانشگاهی

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

Todd Millsteinمشاهده پروفایل

استاد

• Software Reliability
• Network Verification
• Programming Languages
• +۵ مورد دیگر

Todd Millstein is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA), and served as Department Chair from 2022–2025. He is also an Amazon Scholar and a co-founder and former Chief Scientist of Intentionet (now at AWS). His research focuses on making software systems more reliable, particularly through network verification and programming language techniques. He pioneered the Batfish network configuration analyzer, which is used by AWS, Oracle Cloud, and dozens of companies, and received the ACM SIGCOMM Networking Systems Award (2025) for this work. His recent publications span probabilistic programming, network reliability, and interactive program verification, including papers at PLDI 2024 (on bit blasting probabilistic programs), NSDI 2024 (on behavioral testing of BGP), and HotNets 2024 (on network layering). Todd has received prestigious awards such as an NSF CAREER Award , a Microsoft Research Outstanding Collaborator Award , and multiple best paper awards at PLDI, OOPSLA, and SIGCOMM. He has advised Ph.D. students like Ana Brendel and Poorva Garg , and teaches courses such as CS30 (Principles of Computing), CS231 (Types and Programming Languages), and CS239 (Current Topics in PL and Systems). His professional roles include Program Chair for OOPSLA 2014 and ECOOP 2018, and committee member for numerous conferences including PLDI , SPLASH , and LAFI .

• Quantum Computing
• Quantum Foundations
• ZX-calculus
• +۷ مورد دیگر

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute, University of Amsterdam, working with the QuSoft research center. He co-authored the open-access book Picturing Quantum Software and developed the PyZX quantum compiler. His research spans quantum computation and quantum foundations, focusing on diagrammatic methods like the ZX-calculus and ZH-calculus. Quantum circuit optimization and verification Quantum foundations via algebraic/compositional methods Co-creator of PyZX His recent publications explore multi-qutrit systems, completeness of graphical calculi, and quantum state representations. Supervises students in quantum computing, including Lia Yeh and Sarah Li. Directs the new Master's program in Quantum Computer Science at UvA. Actively contributes to open-source projects and international conferences. Notable collaborations include Aleks Kissinger, Neil J. Ross, and QuSoft researchers. Uses GitHub for DiZX development (qudit extension of PyZX). No explicit scientific awards mentioned.

Sören Laueمشاهده پروفایل

استاد

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +۳ مورد دیگر

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

• Financial Mathematics
• Actuarial Mathematics
• Stochastic Processes
• +۷ مورد دیگر

Thorsten Schmidt is Professor of Mathematical Stochastics at the University of Freiburg, succeeding Prof. Ernst Eberlein in the summer semester of 2015. He also serves as Senior Financial Engineer at MathFinance. Previously, he held professorships at Chemnitz University of Technology (2008-2015), Technical University Munich (2008), and University of Leipzig (2004 onwards). From 2017-2019, he was a Research Fellow at the Freiburg Institute for Advanced Studies (FRIAS) in a joint research group with the University of Strasbourg and USIAS on the topic of Linking Finance and Insurance. His research focuses primarily on financial and actuarial mathematics, stochastic processes, and statistics, with recent work on machine learning methods and their applications in financial mathematics and AI regulation. In Freiburg, his goal with his young team is to tackle complex challenges with improved mathematical models and apply these methodologies to various fields. Key Research Areas: Financial mathematics and credit risks Pricing and hedging of derivative financial products Statistics of stochastic processes Energy markets and nonlinear filter theory Machine learning applications in finance and insurance His recent publications show a strong trend toward integrating machine learning with traditional mathematical finance, particularly in risk management, insurance-finance arbitrage, and robust financial modeling. His work increasingly addresses ethical considerations in AI applications within finance, reflecting his broader interest in responsible AI development. Notable Awards: IDA Award Finance (2015) FRIAS-USIAS Research Fellow (2017/2018) IDA Award Machine Learning and AI (2020) MAPFRE Research Grant (2020) Luis Bachelier Fellow (2021) As Editor-in-Chief of Statistics and Risk Modeling and Associate Editor for Mathematical Finance and International Journal of Theoretical and Applied Finance, Schmidt plays a significant role in academic publishing. He leads the CRC 'Small Data' research center with Harald Binder, focusing on medical problems where disease progression must be estimated with few data points per patient. His LeanAI project, funded by the Vector Foundation, explores the connection between machine learning and theorem-proving software LEAN, aiming to develop AI that can translate between mathematics and formal proof systems. His laboratory work centers around the application of stochastic methods combined with machine learning to solve problems in finance and insurance where data is limited ('Small Data' initiative), with significant funding from DFG (€12 million for CRC Small Data) and the Carl Zeiss Foundation.

• Quantum Information Theory
• Quantum Many-Body Theory
• Quantum Optics
• +۷ مورد دیگر

Prof. Dr. Jens Eisert is a Professor at the Free University of Berlin, where he leads the Quantum Many-Body Theory, Quantum Information Theory, and Quantum Optics research group (Eisert AG) within the Institute of Theoretical Physics at the Dahlem Center for Complex Quantum Systems. His office is located at Arnimallee 14, Room 1.3.06 in Berlin-Dahlem. His research focuses on the intersection of quantum information theory and condensed matter physics, specifically exploring what information processing tasks are possible using individual quantum systems as information carriers. His group develops mathematical-theoretical foundations of quantum information, particularly in entanglement theory and tomography, while also investigating quantum optical implementations using light modes or cold atoms in optical lattices. A major emphasis of their work is on quantum many-body systems, including static properties, efficient numerical simulation methods like tensor networks, and non-equilibrium quantum dynamics. Recent publications highlight significant contributions in thermalization of quantum systems (Communications Physics 2025), quantum thermodynamics (Nature Physics 2025), and quantum error correction (PRX Quantum 2025). The group's work is characterized by combining the rigor of mathematical physics with physically motivated applicability, frequently leading to direct collaborations with experimental groups. Quantum Information Theory Quantum Many-Body Theory Quantum Optics Entanglement Theory Tensor Networks Quantum Error Correction Prof. Eisert maintains active supervision of numerous PhD students and postdoctoral researchers, with research positions regularly available in areas including quantum error correction, quantum information theory, tensor networks, and quantum simulation. His group has published extensively in top journals including Nature Physics, PRX Quantum, and Physical Review series.

• Financial Mathematics
• Insurance Mathematics
• Stochastic Processes
• +۳ مورد دیگر

Prof. Francesca Biagini is a Full Professor of Applied Mathematics at the University of Munich (LMU), leading the Department of Mathematics within the Faculty of Mathematics, Computer Science, and Statistics. She holds additional roles as Vice President for International Affairs and Diversity at LMU since 2019, and served as President of the Bachelier Finance Society (2022–2023). Her academic career includes professorships at LMU (since 2009) and prior roles at the University of Bologna and Leibniz University Hannover. She specializes in financial and insurance mathematics, focusing on asset pricing, systemic risk, and model uncertainty. Education: PhD in Mathematical Finance (Scuola Normale Superiore, 2001), Laurea in Mathematics (University of Pisa, 1997). She has advised over 14 PhD students and 180+ master/bachelor students, collaborating with institutions like Allianz, MunichRe, and SwissRe. Research: Biagini’s work bridges financial and actuarial mathematics, including stochastic processes, systemic risk modeling, and insurance frameworks. Notable contributions include modeling asset bubbles, xVA calculations, and liquidity-based frameworks. She has published extensively in journals like *Finance and Stochastics* and *Mathematical Finance*. Awards and Activities: Recipient of the Prinzessin Therese von Bayern Preis (2019) and Zonta Clubpreis (2015). She organizes international conferences, serves on editorial boards (e.g., *Mathematical Finance*), and chairs the Munich Risk and Insurance Center. Her research is funded by grants from BayernLB and LMU Excellence programs.

Amr Rizkمشاهده پروفایل

استاد

• Networking
• Communication Systems
• Network Calculus
• +۵ مورد دیگر

Professor Amr Rizk is the Director of the Networks and Communication Systems (NCS) Lab at the University of Duisburg-Essen, where he has been serving as Professor since April 2021. Previously, he was Assistant Professor at Ulm University (2019-2021) and completed his habilitation at TU Darmstadt in 2019. His academic journey includes research positions at prestigious institutions including University of Massachusetts Amherst, University of Warwick, and TU Darmstadt where he was an Athene Young Investigator. Professor Rizk's research spans multiple aspects of networking and communication systems with a particular focus on network performance analysis, stochastic modeling, and practical implementations. His work bridges theoretical foundations with real-world applications, especially in content delivery, video streaming, and network protocols. He has made significant contributions to network calculus, quality of experience optimization, and novel approaches to congestion control and caching mechanisms. His publication record demonstrates consistent high-impact contributions across top networking conferences and journals. Recent work shows a growing emphasis on programmable data planes, AI/ML applications in networking, and advanced techniques for network measurement and performance prediction. His research group at Duisburg-Essen maintains strong connections with both academic and industrial partners in the networking ecosystem. Best Paper Award at ACM MMSys Conference (2023) Distinguished TPC Member for IEEE INFOCOM (2020, 2022) Best Paper Award at ACM/USENIX Middleware Conference (2017) Athene Young Investigator Award, TU Darmstadt (2017) Professor Rizk serves as Associate Editor for Elsevier Computer Communications and has extensive experience with research funding bodies as a reviewer. His leadership extends to conference organization, including roles as PC Co-Chair for IEEE MIPR (2023) and Steering Committee member for Workshop on Network Calculus (2022). He maintains active participation in numerous top networking conferences as Technical Program Committee member, reflecting his standing within the international networking research community.

• Geometric Analysis
• Partial Differential Equations
• Differential Geometry
• +۱ مورد دیگر

Prof. Dr. Andreas Gastel is a Professor in the Faculty of Mathematics at the University of Duisburg-Essen. His research specializes in geometric analysis, partial differential equations, and differential geometry. He leads a research group working on mathematical modeling and optimization problems, particularly in gas network applications. His recent publications focus on nonlinear material models, conservation laws in polyharmonic systems, and gauge theory applications. He maintains active collaborations with researchers across mathematical physics and applied mathematics disciplines.

Steffen Bondorfمشاهده پروفایل

استاد

• Network Calculus
• Deterministic Networking
• Performance Modeling
• +۴ مورد دیگر

Steffen Bondorf is a Professor of Distributed and Networked Systems in the Faculty of Computer Science at Ruhr University Bochum, Germany. His research focuses on performance modeling and analysis of deterministic networking, particularly using network calculus to provide rigorous performance guarantees for networked systems. Dr. Bondorf received his B.Sc., M.Sc., and Dr.-Ing. (Ph.D.) in Computer Science from TU Kaiserslautern (TUK), where he was part of the distributed computer systems lab. He was the first student to enroll in a B.Sc. program at TUK before finishing secondary education. After graduation, he held positions as postdoctoral researcher, lecturer and Carl-Zeiss Fellow at TUK, research fellow at the National University of Singapore, and visiting researcher at the University of Toronto. From fall 2018 to fall 2019, he was funded by an ERCIM Alain-Bensoussan fellowship at NTNU Trondheim, Norway, and spent time at INRIA Paris. His research interests include network calculus, deterministic networking, performance modeling, computer networks, distributed systems, and P2P overlays. Dr. Bondorf's work has significantly advanced the field of network calculus, particularly in developing methods for analyzing FIFO feedforward networks, multicast flows, and P2P overlay structures. His recent research has integrated machine learning techniques with network calculus to improve analysis accuracy and efficiency, creating hybrid approaches that leverage the strengths of both methodologies. Dr. Bondorf was appointed as assistant professor at Ruhr University Bochum on October 1, 2019, becoming the university's first hire in the joint tenure-track program for the promotion of young scientists. Since December 1, 2021, he has been a tenured full professor at RUB. Carl-Zeiss Fellow ERCIM Alain-Bensoussan fellowship At RUB, Dr. Bondorf teaches Advanced Topics in Networking, Distributed Systems, Deterministic Network Calculus, and Seminar Distributed and Networked Systems. His research group develops tools and methodologies for deterministic network analysis with applications in industrial networking, real-time systems, and time-sensitive communications. His work bridges theoretical networking principles with practical implementations, contributing to the advancement of deterministic networking standards and protocols.

Christoph Schweigertمشاهده پروفایل

استاد

• Tensor categories
• Hopf algebras
• Topological field theories
• +۵ مورد دیگر

Christoph Schweigert is full Professor (W3) of Mathematics at the University of Hamburg , based in the Department of Mathematics within the Faculty of Mathematics, Informatics and Natural Sciences (MIN-Fakultät). Since 2003 he has held this permanent chair, and he currently serves as a Principal Investigator and Area Coordinator for Quantum Theories in the Cluster of Excellence “Quantum Universe” . In addition he is a member of the Centre for Mathematical Physics , the DFG Collaborative Research Centre SFB 1624 and the Research Training Group 1670 “Mathematics inspired by string theory and quantum field theory” . Education & Career Path: 1987–1992: Degree in Physics, Universität Heidelberg 1995: PhD in Mathematics, University of Amsterdam (supervisor: Robbert Dijkgraaf) 1995–1996: Postdoc, IHÉS, Bures-sur-Yvette 1997–1998: Fellow, CERN, Geneva 1999–2002: Lecturer (tenured), LPTHE, Université Paris 6; Habilitation 2000 2002–2003: Professor (C3) for Physics, RWTH Aachen Since 2003: Professor (W3) for Mathematics, Universität Hamburg Research Interests: Schweigert’s work lies at the intersection of algebra, category theory, topology and mathematical physics . He focuses on tensor categories , Hopf algebras and quantum groups , topological and conformal field theories , string-net models , and modular functors . These structures find applications in quantum topology, knot theory, 3-manifold invariants, quantum codes and quantum information theory . Editorial & Service Roles: Editor, Communications in Mathematical Physics (since 2017) Editor, Letters in Mathematical Physics (since 2009) Editor, Journal of Mathematical Physics (since 2006) Editor, Springer book series Algebra and Applications (since 2005) Spokesperson/Deputy spokesperson, DFG-RTG 1670 Member, steering committee, DFG Priority Program “Representation theory” Henriette-Herz scout for the Humboldt Foundation (since 2020) Teaching & Supervision: Schweigert regularly teaches advanced courses in linear algebra, Hopf algebras, quantum groups and topological field theory at bachelor, master and graduate levels. He organises the research seminar Algebra and Mathematical Physics and the joint seminar Quantum Physics and Geometry . Office hours are by appointment via email. Laboratory & Research Group: He leads an active research group based in the Geomatikum building (Room 313), collaborating closely with PhD students, postdocs and visiting researchers on projects in tensor categories, TQFT and related areas.

• Financial Mathematics
• Stochastic Processes
• Risk Management
• +۳ مورد دیگر

Prof. Dr. Francesca Biagini is a full Professor at the Department of Mathematics, University of Munich (LMU Munich) , leading the Stochastics and Financial Mathematics working group. She serves as Vice President for International Affairs and Diversity at LMU Munich since October 1, 2019, and as President of the Bachelier Finance Society (2022–2023). She is also a Correspondent of the Deutsche Aktuarvereinigung (DAV) and a member of the Executive Board of the Munich Risk and Insurance Center (MRIC) since 2017. Her research focuses on stochastic processes in financial markets , particularly asset price bubbles , default risk modeling , and robust hedging under model uncertainty. Recent work includes deep learning applications to bubble detection and non-linear affine processes for market dynamics. She actively contributes to academic leadership through teaching and publications, including 15+ recent articles on topics like liquidity-induced bubbles, machine learning calibration, and systemic risk transfer equilibrium. Her workgroup collaborates on quantLab initiatives and DAV certificate programs .

Joscha Gedickeمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +۳ مورد دیگر

Joscha Gedicke is a Professor at the Institute for Numerical Simulation (University of Bonn), specializing in Numerical Analysis , Finite Element Methods , and Scientific Computing . His research focuses on adaptive algorithms, error estimation, and computational methods for partial differential equations (PDEs) and optimal control problems. Contact: gedicke@ins.uni-bonn.de | +49 228 73-69835 Teaching: Lectures on Hybrid High-Order Methods (V5E1), Adaptive Finite Element Methods (S4E1), and Discontinuous Galerkin Methods (V5E5). Research Trends: Gedicke's work spans Numerical Methods for PDEs , Adaptive Finite Element Analysis , Mixed and Discontinuous Galerkin Formulations , and Error Estimation . His recent publications emphasize Virtual Element Methods and Robust Discretizations for magnetostatic and optimal control problems. Collaborative Networks: He collaborates with researchers in computational mathematics, including institutions like TU Munich, University of Milano-Bicocca, and the University of Bonn's research seminar on Mathematics of Computation .

Wolfgang Stammerمشاهده پروفایل

پژوهشگر

• Explainable AI
• Neuro-Symbolic Architectures
• Concept Learning
• +۳ مورد دیگر

Wolfgang Stammer is a PostDoc researcher in the Machine Learning Group at TU Darmstadt's Computer Science Department. His work focuses on making AI models more interpretable and interactive, particularly in explainable AI (XAI), neuro-symbolic architectures, and systematic compositionality challenges in neural networks. He completed his Ph.D. in Machine Learning at TU Darmstadt (2019–2025), an M.Sc. in Computer Science at Goethe University Frankfurt (2016–2018), and a B.Sc. in Cognitive Science at the University of Osnabrück (2011–2015). Research Interests : Stammer's research bridges gaps between human understanding and AI capabilities. Key areas include: Explainable AI (XAI) and interactive machine learning (XIL) Neuro-symbolic integration for logical reasoning and visual concepts Mitigating shortcut learning and confounding factors in datasets Concept discovery and program synthesis for interpretable models Publications : His work spans foundational contributions to AI benchmarks (e.g., V-LoL, SLR-Bench) and frameworks (Neural Concept Binder, Revision Transformers). Recent studies highlight AI's limitations in systematic generalization and propose solutions for aligning reinforcement learning agents with human values. Grants & Labs : He contributes to the Machine Learning Lab at TU Darmstadt and co-organized workshops like the Interactive Machine Learning Workshop @ AAAI 2022. His research bridges theoretical advances with practical applications in healthcare and ethical AI systems.

Felix Seifertمشاهده پروفایل

پژوهشگر

• Calculus of Variations
• Nonlocal Analysis
• Abstract Harmonic Analysis
• +۱ مورد دیگر

Felix Seifert is a Researcher at the Chair of Analysis within the Department of Mathematics, Faculty of Mathematics and Geography at the Catholic University of Eichstätt-Ingolstadt (KU). Based in room KGB-102 of KG Building B, he can be contacted via telephone +49 8421-93-21501 or email felix.seifert@ku.de. His research focuses on calculus of variations with nonlocal gradients, abstract harmonic analysis, and graph theory. This interdisciplinary work bridges discrete and continuous mathematical structures, particularly examining geometric properties of graphs through nonlocal analytical frameworks. His investigations into intrinsic metrics on graphs demonstrate innovative approaches to metric space theory in discrete settings. Seifert's publication record centers on graph-theoretic geometry, with his 2024 Mathematische Nachrichten paper establishing foundational connections between intrinsic metrics and nonlocal operators. This work reflects broader trends in applying variational methods to discrete structures, contributing to emerging fields at the intersection of combinatorics and analysis. He actively collaborates within the Chair of Analysis research team at KU, notably with D. Lenz and M. Schmidt on graph theory projects. His current work extends to developing analytical tools for nonlocal phenomena on discrete spaces, with potential applications in network theory and geometric modeling.