جست‌وجوی استاد و پژوهشگر

• Discrete Optimization
• Convex Geometry
• Mathematical Optimization
• +۳ مورد دیگر

René Brandenberg is a Senior Lecturer at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on Discrete Optimization, Convex Geometry, and Mathematical Optimization, with notable contributions to geometric inequalities, containment problems, and algorithm design. He has extensively supervised theses addressing topics like energy system optimization, linear programming applications, and geometric algorithms. His teaching responsibilities include courses such as Linear Algebra for Engineering students and advanced seminars on topics like Convex Optimization. He has co-advised numerous theses, collaborating with researchers like Peter Gritzmann and Michael Ritter. His work bridges theoretical advancements with practical applications in logistics, energy systems, and computational geometry. Brandenberg’s research group participates in initiatives like the TUM-ICL Mathematical Sciences Hub and contributes to interdisciplinary projects, including optimization models for power markets and algorithmic solutions for geometric containment. His publications reflect a deep engagement with foundational problems in computational geometry and optimization theory.

• Optimal Transport
• Calculus of Variations
• Wasserstein Barycenters
• +۴ مورد دیگر

Camilla Brizzi is a Researcher at the Technical University of Munich (TUM), affiliated with the Chair of Global Analysis under Prof. Friesecke. She completed her PhD through a co-tutorship program between the University of Florence and the University of Paris Dauphine. Her research focuses on optimal transport theory, calculus of variations, and related areas such as Wasserstein barycenters, multi-marginal problems, and entropic methods. She has contributed to advancements in non-convex cost optimization and supremal variational problems. Her academic journey includes studies at the University of Florence, followed by doctoral research combining European institutions. She collaborates with leading figures in the field, including Gero Friesecke and Luigi De Pascale, and her work bridges theoretical analysis with computational methods. Her publications span high-impact journals like Nonlinear Analysis and Applied Mathematics and Optimization , addressing both foundational and applied aspects of optimal transport. Brizzi’s research interests also extend to the application of optimal transport in solving complex mathematical problems, such as those arising in physics and data science. She actively contributes to the academic community through seminars, conferences, and interdisciplinary projects, reflecting her commitment to advancing mathematical foundations and their real-world implications.

Christoph Pflaumمشاهده پروفایل

استاد

• simulation of airships
• computational optics
• high performance computing
• +۲ مورد دیگر

Prof. Christoph Pflaum holds the position of Professor of Computational Engineering at the University of Erlangen-Nürnberg's Department of Computer Science. His academic journey includes a PhD (1996) and Habilitation (1999) from Technical University Munich and University of Würzburg, respectively. His research focuses on computational optics, high-performance computing, numerical analysis, and multigrid methods, with applications in laser simulation and energy-efficient systems. Education : 1992: Diploma in Mathematics, Technical University Munich 1996: Dr. rer. nat., Technical University Munich 1999: Habilitation, University of Würzburg Research Interests : PDE solvers on sparse grids, laser dynamics (thermal lensing, solid-state amplifiers), computational fluid dynamics for airships, and optical system optimization. His work bridges theoretical numerical methods with industrial applications in photonics and renewable energy. Key Contributions : Developed simulation tools for high-power lasers and solar cells, including LASCAD and FDFD methods. Advances in multigrid algorithms and finite element analysis for complex optical systems. Teaching : Courses in theoretical computer science, scientific computing, and computational optics, emphasizing practical implementation using advanced numerical libraries and HPC. Grants & Projects : Led research on laser systems optimization (Innonet SOL), solar cell simulations (Photonics Europe), and high-performance computing initiatives (Par-EXPDE project).

Stefan Zimmerمشاهده پروفایل

مدرس ارشد

• Numerical Simulation
• Computational Science
• Sparse Grids
• +۴ مورد دیگر

Stefan Zimmer is a Senior Lecturer and Academic Councillor at the University of Stuttgart, affiliated with the Simulation of Large Systems department under the IPVS institute. His work focuses on numerical simulation, computational science, and algorithm development with applications in sparse grids, multigrid methods, and high-performance computing. Research interests include adaptive algorithms for large-scale systems, model reduction techniques, and interdisciplinary challenges in technical education. He has contributed to foundational works in sparse grid techniques and parallel computing architectures. Publications span from 1993 to 2015, emphasizing advancements in numerical methods, data mining, and computational fluid dynamics. Notable works include the 2015 multigrid method for adaptive sparse grids and a 2009 textbook on modeling and simulation methodologies. No awards or student advisees are explicitly listed in the provided text.

• Quantum Algorithms for Optimization Problems
• Quantum Software-Hardware Co-Design
• Quantum Error Correction
• +۲ مورد دیگر

Lilly Palackal is a Researcher and External PhD candidate at the Technical University of Munich (TUM) School of CIT, affiliated with the Department of Computer Science. She collaborates closely with Infineon Technologies AG on quantum computing research. Her work focuses on quantum algorithms for optimization problems, co-design of quantum software/hardware, and quantum error correction/mitigation. Education: BSc (2018) and MSc (2021) in Mathematics from TUM, with thesis topics in quantum error correction and interactive quantum communication. Current PhD research bridges academic and industrial quantum computing challenges. Research interests include developing quantum solutions for logistics (e.g., vehicle routing), optimization (e.g., knapsack problems), and hardware-aware algorithm design. She supervises multiple student projects across mathematics, physics, and engineering disciplines at TUM and partner institutions. Recent publications explore hybrid quantum-classical methods, QAOA applications, and efficient algorithm encodings for quantum systems. Her work emphasizes practical implementations of quantum technologies in real-world scenarios.

• Quantum Computing
• Quantum Walks
• Quantum Simulation
• +۴ مورد دیگر

Jingbo Wang is Professor of Physics at the University of Western Australia, where she heads the Physics department and directs the QUISA Research Hub for Quantum Information, Simulation, and Algorithms. Her research pioneers quantum walk applications in complex systems analysis and quantum compiler development. After completing her PhD at the University of Adelaide, she established a leading quantum computation group at UWA. Her work bridges quantum dynamics, algorithm design, and complex network theory. Recent publications demonstrate strong focus on quantum machine learning, optimization algorithms, and experimental quantum computing implementations. Her team consistently develops efficient quantum circuits for practical problem-solving in finance, biology, and material science. Fellow of Australian Institute of Physics (2021) Vice-Chancellor’s Award in Research Mentorship (2022) School Research Mentorship Award (2022)

Nikos Gianniotisمشاهده پروفایل

پژوهشگر

• probabilistic machine learning
• dimensionality reduction
• Bayesian inference
• +۱ مورد دیگر

Nikos Gianniotis serves as a Staff Scientist in the Astroinformatics group at the Heidelberg Institute of Theoretical Studies (HITS), leveraging his computer science expertise to develop advanced machine learning solutions for astronomical data analysis. His role focuses on probabilistic modeling and algorithmic innovation within a dedicated research environment. His academic credentials include: PhD in Computer Science from the University of Birmingham (2007) MSc in Natural Computation (with Distinction) from the University of Birmingham (2003) BSc in Computing Science (First class) from the University of Aberdeen (2002) Dr. Gianniotis specializes in probabilistic machine learning , Bayesian inference , and dimensionality reduction techniques applied to astronomical challenges. His research bridges computational statistics and astrophysics, with emphasis on interpretable models for complex observational data. Recent work demonstrates sophisticated integration of Gaussian processes with time-series analysis for cosmic phenomena. Analysis of his 15 most recent publications (2016-2024) reveals consistent innovation in machine learning for astronomy, particularly in time-delay estimation for active galactic nuclei, spectral analysis, and time-series dimensionality reduction. His methodology frequently combines Gaussian processes, variational inference, and neural architectures like autoencoders to address astronomical data challenges. No scientific awards were documented in the provided materials. While no student advisement or grant details were specified, his publications indicate active collaboration within the astroinformatics community. The Astroinformatics group at HITS provides his primary research environment, focusing on scalable computational frameworks for next-generation astronomical datasets from missions like Kepler.

• Stochastic Analysis
• Partial Differential Equations
• Nonlinear PDEs
• +۵ مورد دیگر

Benjamin Gess is a Professor of Mathematics at Technische Universität Berlin and leads the 'Stochastic analysis in the Sciences' research group at the Max Planck Institute for Mathematics in the Sciences (MIS) in Leipzig. He holds an ERC Consolidator Grant (2023) for his project 'FluCo', focusing on fluctuations in continuum systems and conservative SPDEs. His research spans stochastic partial differential equations (SPDEs), nonlinear PDEs, stochastic dynamics, interacting particle systems, machine learning, and fluid dynamics, with an emphasis on conservative SPDEs and their applications. Education: PhD (summa cum laude) from Universität Bielefeld (2011), MSc from University of Warwick (2009). Postdoctoral positions included University of Chicago (2013–2015) and Technische Universität Berlin (2012). He has held academic roles at Universität Bielefeld (W3 Professor from 2019–2024) and the Max Planck Institute since 2015. Key research interests include: regularization by noise, large deviations, stochastic flows, and the interplay between stochasticity and complex systems. His work combines stochastic analysis, nonlinear PDE theory, and kinetic theory to address challenges in singular SPDEs and supercritical phenomena. Recent grants include the ERC Consolidator Grant, and his research has led to advancements in understanding fluctuating hydrodynamics, machine learning dynamics via stochastic gradient descent, and self-organized criticality models. He supervises a research group and collaborates internationally with institutions like the University of Chicago and MPI MIS.

Ali Hashemiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Ali Hashemi is a Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and a Postdoctoral Researcher at Technical University of Berlin. He holds a PhD (Dr. rer. nat.) with distinction from TU Berlin (2022) and an MSc in Electrical Engineering from Sharif University of Technology (2013). His research focuses on machine learning, Bayesian methods, and neuroimaging applications, particularly in inverse problems and explainable AI. He has contributed to advancements in structured noise modeling, robust source localization, and 3D neural networks for brain imaging. His work bridges signal processing and AI to improve biomedical data analysis. Education: PhD in Machine Learning, Technical University of Berlin (2022) MSc in Electrical Engineering, Sharif University of Technology (2013) Research Interests: Machine Learning & Optimization Bayesian Inference Neuroimaging (M/EEG, fMRI) Explainable AI & Diffusion Models Signal Processing for Biomedical Applications Key Achievements: Developed the 'Champagne Algorithm' for noise estimation in brain imaging Published widely in top venues like NeurIPS and NeuroImage Recipient of PhD distinction award for outstanding dissertation Labs & Affiliations: Active in TU Berlin's Uncertainty & Inverse Modeling group and Charité’s Brain and Data Science lab. Collaborates on open-source tools for M/EEG analysis (e.g., contributions to MNE-Python).

Rolf H. Möhringمشاهده پروفایل

استاد

• Combinatorial Optimization
• Graph and Network Algorithms
• Operations Research
• +۶ مورد دیگر

Prof. Rolf H. Möhring is a Professor at the Department of Mathematics, Faculty II – Mathematics and Natural Sciences, Technical University of Berlin. He specializes in Combinatorial Optimization, Graph and Network Algorithms, and Operations Research with applications in industrial and transportation systems. His research bridges theoretical foundations and practical implementations, addressing challenges in scheduling, distributed computing, and algorithmic game theory. Key research interests include graph algorithms, project scheduling optimization, and robust optimization methodologies. Notable contributions span network flow analysis, traffic equilibrium design, and logistics optimization in container terminals and shipping routes. Awards: 2025 Research.com Mathematics in Germany Leader Award, 2010 EURO Gold Medal. Publications: Over 127 papers with 16,279 citations in Computer Science and 113 papers with 15,733 citations in Mathematics. His work frequently explores combinatorial structures, invariant theory, and practical applications in distributed systems. His advising focuses on PhD students in combinatorial optimization and algorithmic theory, with contributions to the Mathematics Genealogy Project. Collaborations include industrial projects like Kiel Canal ship traffic optimization and AGV routing in container terminals, emphasizing real-world problem-solving through mathematical frameworks.

• Theoretical Physics
• Quantum Many-Body Systems
• Quantum Phase Transitions
• +۵ مورد دیگر

Prof. Markus Heyl is a Professor of Theoretical Physics III at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. Prior to this role, he held positions as a Leibniz Group Leader at the Max-Planck Institute for the Physics of Complex Systems and a postdoctoral fellow at institutions such as TU Munich and IQOQI Innsbruck. His research focuses on quantum many-body systems, dynamical quantum phase transitions, and the application of machine learning techniques to quantum physics. Heyl's academic journey includes a PhD from Ludwig-Maximilians-Universität München under Prof. Stefan Kehrein, followed by postdoctoral work exploring quantum dynamics, localization phenomena, and quantum simulation. He currently leads a vibrant research group comprising postdoctoral researchers and PhD students investigating topics like neural quantum states, adaptive quantum algorithms, and non-equilibrium quantum systems. Teaching responsibilities include courses on 'Machine Learning in Physics' and advanced seminars on quantum many-body theory. His recent workshops, such as 'Connecting Physics and Computer Science' (2024) and 'Bridging the Divide' (2023), reflect his interdisciplinary approach to advancing quantum research. Key research themes include dynamical criticality, Trotterization optimization for NISQ devices, and the interplay between quantum chaos and integrability. His work bridges theoretical insights with experimental validations, leveraging tools like neural networks and classical networks to model complex quantum systems.

Markus Weimarمشاهده پروفایل

مدرس

• Regularity Theory for Operator Equations
• Besov Regularity
• Nonlinear Partial Differential Equations
• +۱۲ مورد دیگر

Markus Weimar is a Lecturer at the Chair of Mathematics IX (Scientific Computing) at the University of Würzburg. He previously held academic positions at Ruhr University Bochum (Interim Professor, Assistant Professor), University of Siegen (Senior Lecturer, Interim Professor), and Philipps-University Marburg (Postdoctoral Researcher). Education: Dipl.-Math. (2009) and Ph.D. (2013) from Friedrich-Schiller-University Jena. Honors: Summa cum laude Ph.D. thesis, German National Academic Foundation support, and election to the Early Career Researchers Board at RUB. Research Interests include regularity theory for operator equations, partial differential equations, boundary integral equations, function spaces, numerical analysis, adaptive methods, and high-dimensional problems. His work focuses on overcoming the curse of dimensionality through techniques like wavelets, quasi-Monte Carlo methods, and discrepancy theory. Recent Publications highlight breakthroughs in Besov regularity for p-Poisson equations, adaptive wavelet boundary element methods, and tractability of high-dimensional permutation-invariant integration. His 2024 papers on Sobolev spaces with mixed weights and oscillation analysis in Morrey-type spaces indicate ongoing innovation in theoretical numerical analysis. Scientific Leadership: Senior member of the GAMM Junior Research Group at RUB, active in organizing workshops, and participant in international conferences like FoCM and MCQMC.

Alexandros Stamatakisمشاهده پروفایل

استاد

• Algorithms
• Parallel Computing
• Parallel Architectures
• +۳ مورد دیگر

Prof. Alexandros Stamatakis is a Professor of High Performance Computing at Karlsruhe Institute of Technology (KIT) and Group Leader of the 'Scientific Computing' group at the Heidelberg Institute for Theoretical Studies (HITS). He holds an adjunct professorship at the University of Arizona. His research focuses on algorithms, parallel computing architectures, and evolutionary bioinformatics. Stamatakis earned his PhD in Computer Science from TU Munich (2004) and has held roles including DFG Emmy-Noether Junior Research Group Leader (2008-2010). He is a member of multiple scientific advisory boards, including Elixir Greece and the Computational Biology Institute in Montpellier. His teaching awards (2015, 2016) highlight his contributions to bioinformatics education. His work spans computational methods for phylogenetic analysis, energy-efficient computing, and genomic data processing, with notable tools like RAxML-NG and EPA-ng. Research interests include parallel computing optimizations, phylogenetic inference algorithms, and applications in evolutionary biology. His recent work addresses challenges in large-scale genomic data analysis, including error detection in DNA barcoding systems and accelerating phylogenetic computations through algorithmic innovations. He leads collaborative projects on avian genome complexity and ancient DNA analysis, contributing to both computational methods and biological discoveries. Stamatakis collaborates internationally, with affiliations spanning institutions in Europe and the U.S. His lab at HITS emphasizes interdisciplinary approaches, merging computer science with biological problems. Key contributions include developing carbon-aware energy management frameworks and predictive models for phylogenetic inference stability.

• Machine Learning
• Data Mining
• Big Data
• +۶ مورد دیگر

Mehrdad Almasi is affiliated with the University of Luxembourg and Tarbiat Modares University, Tehran, Iran. His research focuses on Machine Learning , Data Mining , and Big Data applications. Key Research Areas : Associative classification, sentiment analysis, and scalable data processing. Technical Domains : Pattern recognition, distributed computing, and Android malware detection. Published Work : Collaborated on graphical models for database joins, large-scale classification systems, and multi-objective evolutionary algorithms.

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +۳ مورد دیگر

Prof. Tatjana Stykel is a Professor of Computational Mathematics at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. She holds positions in the Institute of Mathematics and leads the Computational Mathematics research group. Her academic journey includes a PhD from Technische Universität Berlin (2002) and a Habilitation (2009), both in numerical mathematics. Prior to her current role, she led a junior research group at TU Berlin’s MATHEON Research Center and held visiting professorships at TU Berlin and Humboldt-Universität zu Berlin. Stykel’s research focuses on Numerical Linear Algebra (e.g., generalized eigenvalue problems, matrix equations), Differential-Algebraic Equations (numerical methods and stability), and Control Theory (descriptor systems, model reduction). She has pioneered energy-adaptive optimization methods and Riemannian geometry techniques for complex systems, including applications in quantum chemistry and electromagnetism. Her awards include the Richard von Mises Prize (2007), Leslie Fox Prize (2003), and multiple fellowships from the Pacific Institute for the Mathematical Sciences and Soros programs. She has led major projects like the EU-funded EU-MORNET (2014–2018) and contributed to BMBF networks in model reduction for coupled systems. Stykel teaches advanced numerical methods, including courses on differential equations, optimization, and model reduction. Her work emphasizes interdisciplinary collaboration, with partnerships in robotics, semiconductor technology, and fluid dynamics. She advises several students, including alumni who have become prominent in applied mathematics and engineering.