Find Academic Supervisors

• Reinforcement Learning
• Bandit Algorithms
• Learning Theory
• +4 more

Claire Vernade is a Group Leader at the University of Tübingen within the Cluster of Excellence Machine Learning for Science. She holds an Emmy Noether award (2022) and an ERC Starting Grant (2024) for her projects FoLiReL and ConSequentIAL , focusing on theoretical reinforcement learning and non-stationary environments. Education: PhD from Telecom ParisTech (2017) Post-doctoral researcher at University of Magdeburg (2018) Her research bridges sequential decision making, bandit problems, and reinforcement learning theory. Recent work explores lifelong learning, distributional RL, and game-theoretic approaches to PCA, emphasizing mathematical rigor and algorithmic innovation. Recent publications highlight trends in non-stationary RL , continual learning , and bandit algorithms with complex feedback structures. Key subfields include meta-learning, adaptive control, and theoretical guarantees in dynamic programming. Scientific Awards: Emmy Noether Award (2022) ERC Starting Grant (2024) Outstanding Paper Award & Oral Presentation, ICLR (2021) She mentors PhD and master's students in theoretical machine learning, with current advisees including Nicolas Nguyen, Onno Eberhard, and Ziyad Sheebaelhamd. Her lab actively recruits candidates in bandit algorithms and RL theory through the IMPRS-IS and ELLIS doctoral programs. Claire co-leads diversity initiatives like Tübingen Women in Machine Learning and Women in Learning Theory, advocating for inclusivity in AI research. She has organized workshops at ICML and EWRL, and contributed to union activism in the tech industry.

Volker MehrmannView profile

Professor

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +4 more

Volker Mehrmann is a full professor at the Technical University of Berlin in the Institute of Mathematics , Faculty II - Mathematics and Natural Sciences. He has held academic positions at Chemnitz University of Technology and RWTH Aachen University . His roles include leadership in research centers: Spokesperson for the DFG Research Center Matheon (2008-2016), President of the European Mathematical Society (2017-2022), and committee member of the Cluster of Excellence MATH+. PhD: Bielefeld University (1982) Habilitation: Bielefeld University (1987) His research interests span Numerical Linear Algebra , Differential-Algebraic Equations (DAEs) , Control Theory , and Industrial Mathematics . Recent work focuses on port-Hamiltonian systems and model order reduction for multi-physics applications. Key scientific contributions include: ERC Advanced Grant (2011-2016) on multi-physics systems Hans Schneider Prize (2019) SIAM Fellow (2011) and AMS Fellow (2022) He serves as editor-in-chief of Linear Algebra and Its Applications and contributes to numerous editorial boards. His leadership roles include presidency in the European Mathematical Society and GAMM .

Mira SchedensackView profile

Assistant Professor

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

JProf. Dr. Mira Schedensack is a faculty member at the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science , University of Münster. Her expertise lies in Numerical Analysis, Machine Learning, and Scientific Computing , with a focus on finite element methods and numerical solutions for partial differential equations. Research Interests: Numerical methods for PDEs, mixed finite element formulations, adaptive algorithms, and thermo-optical interactions in computational physics. Teaching: Offers courses in Numerical Partial Differential Equations and Adaptive Finite Element Methods. Students: Mentors doctoral student Jonas Ketteler . Publications: Key contributions to non-conforming FEM, Stokes equations, and gradient elasticity.

Joscha GedickeView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +3 more

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 .

Andrea WaltherView profile

Professor

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +3 more

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Ngoc Tien Tran is a Junior Professor for Numerical Simulation at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences and Technology, University of Augsburg since 2023. Previously, he was a Research Associate at Friedrich Schiller University Jena (2021-2023) and Humboldt University of Berlin (2018-2021). Dr. Tran's educational background includes: Doctorate in Mathematics, Humboldt University of Berlin (2021) Master of Mathematics, Humboldt University of Berlin (2018) Bachelor of Mathematics, Humboldt University of Berlin (2016) Dr. Tran's research focuses on advanced numerical methods for complex mathematical problems. His primary areas of interest include numerical methods for completely non-linear second-order PDEs , the convergence behavior of adaptive methods , nonstandard discretizations and hybridizable methods , and convex minimization and eigenvalue problems . His work bridges theoretical mathematics with practical computational techniques, developing innovative approaches to solve challenging problems in numerical analysis. Dr. Tran's publication record demonstrates a consistent focus on hybrid high-order methods and their applications to various mathematical problems. His recent work shows a progression from foundational methods to specialized applications in areas like the Monge-Ampère equation, eigenvalue problems, and convex minimization. A notable trend is his emphasis on guaranteed error control and stability analysis, reflecting a commitment to robust and reliable numerical methods. Dr. Tran is involved in research supported by the ERC Consolidator Grant and participates in the GAMM Workshop on Numerical Analysis and the One World Numerical Analysis Seminar. As a Junior Professor at the University of Augsburg, Dr. Tran advises students and contributes to the Numerical Mathematics research team. His teaching includes courses such as 'Finite elements in the calculus of variations' and a 'Seminar on Numerics' for Summer semester 2025. He is part of a vibrant research community that includes colleagues like Daniel Peterseim and Tatjana Stykel, as well as numerous research associates.

Lambert TheisenView profile

Researcher

• Partial Differential Equations (PDEs)
• Numerical Analysis
• Computational Fluid Dynamics
• +5 more

Lambert Theisen is a PhD student and Researcher at RWTH Aachen University's Applied and Computational Mathematics (ACoM) department. He holds a BSc (2014–2018) and MSc (2018–2019) in Computational Engineering Science from RWTH Aachen. His research focuses on PDE eigenvalue problems, asymptotic analysis, directional homogenization, and numerical methods for rarefied gas dynamics. He has held research roles at RWTH Aachen, the University of Stuttgart's NMH/IANS, and ABB Corporate Research. His research spans computational fluid dynamics, domain decomposition methods, and preconditioning techniques for eigenvalue algorithms. Notable contributions include finite element solvers for electrolyte models and FEniCS-based simulations of rarefied gas flows. His work often integrates advanced mathematical frameworks with high-performance computing. Teaching roles include assistantships for courses like 'Mathematical Aspects in Computational Chemistry' and 'Higher Mathematics for Engineers.' His publications are tracked via ORCid and emphasize scalable eigensolvers, mesh generation, and tensorial methods. He maintains a personal website at thsn.dev for research updates.

Gert LubeView profile

Professor

• Numerical Methods for Partial Differential Equations
• Computational Fluid Dynamics
• Turbulence Modeling
• +2 more

Prof. Dr. Gert Lube is a faculty member at the Institute for Numerical and Applied Mathematics (NAM) within the Faculty of Mathematics and Computer Science at Georg-August-University Göttingen. His research focuses on numerical methods for partial differential equations , with emphasis on stabilized finite element methods , turbulence modeling , and magnetohydrodynamics (MHD) . Workshops Organized : Calibration of Viscosity Models for Turbulent Flows (2010), Variational Multiscale Methods (2008), Local Projection Stabilization (2008), BAIL Conferences. His academic contributions include 15+ publications since 2010 on topics like Navier-Stokes simulations , LES/VMS methods , stabilized FEM , and FEM-BEM coupling . Collaborations span institutions like TU Graz, Saarbruecken University, and DLR Göttingen. Key Research Areas : Finite Element Methods, Turbulence Modeling, MHD, Incompressible Flows, Singularly Perturbed Problems, Parallel Computing. Advisees include PhD candidates working on topics such as non-isothermal flows , mass conservation , hybrid RANS/LES , and domain decomposition .

Ming ZhouView profile

Senior Lecturer

• Numerical Linear Algebra
• Eigenvalue Problems
• Adaptive Finite Elements

Ming Zhou is a Senior Lecturer at the Institute of Mathematics, University of Rostock, Germany. His research focuses on numerical linear algebra, eigenvalue problems, and adaptive finite elements. He is known for contributions to convergence theory of preconditioned eigensolvers and iterative methods. Zhou co-developed the AMP Eigensolver, a software tool for solving elliptic partial differential operator eigenvalue problems in 2D domains. His work emphasizes robust bounds for Ritz values, block preconditioned gradient methods, and restarted Krylov subspace iterations. Collaborations with researchers like K. Neymeyr and A.V. Knyazev have produced influential studies in numerical analysis. Zhou’s recent publications (2023–2024) address cluster-robust estimates and angle-free Ritz value bounds, advancing eigensolver algorithms. He is based at the Institute of Mathematics in Rostock, with office 332. His email is ming.zhou@uni-rostock.de . Office hours are by appointment.

Giuseppe SavaréView profile

Professor

• Mathematical Analysis
• Optimal Transport
• Gradient Flows
• +4 more

Giuseppe Savaré is a Full Professor of Mathematical Analysis at the Department of Mathematics, University of Pavia, and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS) since 2019. His research focuses on variational methods for evolution problems, including gradient flows, rate-independent models, nonlinear PDEs, and optimal transport theory in metric spaces. Education: PhD in Mathematics (1988) from University of Pavia. His work bridges optimal transport, metric measure spaces, and analysis in non-Euclidean geometries, with applications to PDEs, curvature bounds, and generalized gradient flows. Key contributions include foundational studies on Wasserstein gradient flows, entropy-transport problems, and abstract action spaces in dynamical systems. Scientific awards include the prestigious Ennio De Giorgi Prize (2011) and the Gioachino Japichino Prize (1994), alongside an invitation as a speaker at the 7th European Congress of Mathematics (2016). Collaborations with researchers like Massimo Fornasier and Alexander Mielke have advanced consensus-based optimization, multi-agent control, and quantum drift-diffusion models.

• Algebraic Complexity Theory
• Computational Algebra
• Geometric Complexity Theory
• +4 more

Prof. Dr. Peter Bürgisser is a Professor at the Technical University of Berlin, affiliated with the Institute of Mathematics and the Algorithmic Algebra research group within Faculty II - Mathematics and Natural Sciences. His research focuses on algebraic complexity theory, computational algebra, and geometric methods in computer science. Bürgisser has made significant contributions to topics including invariant theory, numerical analysis of algorithms, and the computational complexity of algebraic problems. He has authored influential books such as *Algebraic Complexity Theory* and has published extensively in top-tier journals like the Journal of the ACM and SIAM Journal on Computing. His work includes developing polynomial-time algorithms for problems in invariant theory, analyzing the condition numbers of algebraic varieties, and studying the computational aspects of semialgebraic sets. Bürgisser's research also intersects with probability theory, particularly in understanding the statistical properties of zeros of random polynomials and the geometry of random algebraic varieties. Recent trends in his publications emphasize geometric complexity theory, non-commutative optimization, and the application of numerical methods to algebraic problems. He has collaborated with researchers such as Felipe Cucker, Michael Walter, and Avi Wigderson on foundational topics in computational mathematics and theoretical computer science. Bürgisser's office is located in room EB 116, and his contact information includes the email pbuerg@math.tu-berlin.de. His research has been supported through grants and collaborations, though specific grant details are not explicitly mentioned in the provided texts.

Roland SpeicherView profile

Professor

• Free Probability Theory
• Random Matrices
• Operator Algebras
• +3 more

Roland Speicher is a Professor at the Department of Mathematics, Saarland University. His research focuses on Free Probability Theory , Random Matrices , and Operator Algebras , with applications in quantum physics, machine learning, and statistical mechanics. University: Saarland University Department: Mathematics Academic Rank: Professor Email: speicher@math.uni-sb.de Roland Speicher's research explores the interplay between free probability and random matrices, particularly through the lens of quantum mechanics and computational mathematics. He has contributed to understanding the effects of non-linear transformations on random matrices and their eigenvalue distributions, extending free probability tools to problems in machine learning and quantum information theory. His recent publications address: Non-linear functions on orthogonally invariant matrices Fuglede-Kadison determinants in operator-valued free probability Structured random matrices and cyclic cumulants Free probability applications in quantum gravity and black hole entropy Connections to the replica trick and quantum de Finetti theorems Key scientific recognitions include the ERC Advanced Grant (2014-2019) for non-commutative distributions. He leads a research group at Saarland University, collaborating with Dr. Johannes Hoffmann, Dr. Tobias Mai, and M.Sc. Alexander Wendel. His teaching includes advanced courses on random matrices, with lecture notes published by EMS Press.

• Quantum Chemistry
• Theoretical Chemistry
• Atomic Physics
• +5 more

Ruiz Ruiz serves as an Associate Professor in the Department of Chemistry and Pharmacy within the Faculty of Natural Sciences at Friedrich Alexander University Erlangen-Nuremberg (FAU). Affiliated with the Chair of Theoretical Chemistry (led by Prof. Dr. Görling), Ruiz Ruiz maintains an active research profile in quantum chemistry with numerous publications spanning several decades. As a PD Dr. (Privatdozent Doctor), Ruiz Ruiz has completed the German habilitation process, signifying advanced scholarly achievement beyond the doctoral degree. Ruiz Ruiz's research focuses on developing and applying advanced computational methods for quantum mechanical calculations of atomic systems. Primary research areas include Hylleraas-Configuration Interaction (Hy-CI) methods, Slater orbital integrals, and wave function theory for atomic and molecular systems. The research demonstrates particular expertise in high-precision calculations of atomic energy levels, electron correlations, and excited states. Recent work continues to refine computational algorithms for increasingly complex atomic systems while maintaining high accuracy in quantum mechanical predictions. Analysis of recent publications reveals a consistent trajectory in advancing computational quantum chemistry methods. Ruiz Ruiz has made significant contributions to the theoretical framework for calculating atomic properties with exceptional precision, particularly for systems involving multiple electrons. The research shows strong continuity in methodological development, with recent work building upon decades of prior contributions to Hy-CI methods and related computational approaches. Key application areas include helium-like systems, lithium ions, boron atoms, and other light elements where precise quantum mechanical treatment is essential. Ruiz Ruiz's scholarly output demonstrates sustained contribution to the field of theoretical quantum chemistry, with publications appearing consistently from the 1990s through 2025. The research program shows particular strength in developing analytical methods for quantum mechanical integrals and advancing computational techniques for atomic structure calculations. While specific grants and advising activities aren't detailed in the available information, the extensive publication record suggests active supervision of research students and potential grant-funded research activities within the theoretical chemistry group at FAU.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Haim AvronView profile

Professor

• Numerical Computing
• High Performance Computing
• Machine Learning
• +3 more

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.