جستجوی استاد راهنمای دانشگاهی

Michael Hintermüllerمشاهده پروفایل

استاد

• Applied Mathematics
• Optimization and Control
• Partial Differential Equations
• +۴ مورد دیگر

Prof. Michael Hintermüller is the Director of the Weierstrass Institute (WIAS) and holds a professorship in Applied Mathematics at Humboldt-Universität zu Berlin. He serves as Founding Coordinator of BR50, Spokesperson of the Mathematical Research Data Initiative (MaRDI), and Board Member of the MATH+ Cluster of Excellence. His research focuses on nonsmooth optimization, PDE-constrained control, mathematical image processing, and quasi-variational inequalities. Leadership Roles: Director of WIAS; Founding Coordinator of BR50; Spokesperson of MaRDI; Board Member of MATH+ Key Research Areas: Mathematical image processing, optimization under uncertainty, PDE-constrained optimization, shape/topology optimization, learning-informed constraints Recent Applications: Image deblurring/denoising/demodulation, energy network modeling, gas dynamics on pipeline networks, thermoforming simulations, strained photonic device design. His work combines analytical rigor with numerical methods for inverse problems, including adaptive regularization and physics-informed neural networks. Scientific Leadership: Active in mathematical modeling for biomedical imaging (e.g., quantitative MRI) and industrial applications (e.g., semiconductor design, gas flow optimization). Develops novel algorithms for nonsmooth PDE systems and contributes to the theoretical foundations of quasi-variational inequalities and generalized Nash equilibrium problems.

Daniel Peralta-Salasمشاهده پروفایل

استاد

• Dynamical Systems
• Partial Differential Equations
• Differential Geometry
• +۱۳ مورد دیگر

Daniel Peralta-Salas is a CSIC Research Professor and Chair of the Group 'Differential Geometry and Geometric Mechanics' at the Institute of Mathematical Sciences (ICMAT) in Madrid, Spain. ICMAT is a joint research institute of the Spanish National Research Council (CSIC) and several Madrid universities. He is an active researcher with leadership roles in the ICMAT board and multiple international advisory boards. PhD in Mathematics, Universidad Complutense de Madrid, 2006 His research lies at the intersection of dynamical systems , partial differential equations , and differential geometry , with applications in fluid mechanics, plasma physics, quantum mechanics, and mathematical physics. He has developed a unifying theory to study geometrically complex structures in physical models. His work includes proving long-standing conjectures by Arnold, Lord Kelvin, and Michael Berry, and constructing fluid flows that simulate universal Turing machines, thereby demonstrating undecidable dynamics in fluid particle paths. The most recent articles highlight his ongoing focus on steady Euler flows , topological obstructions in magnetic fields , optimal eigenvalue problems , and Turing universality in fluid dynamics . His publications span geometric analysis, spectral theory, and mathematical physics, often combining deep topological insight with PDE techniques. He has developed novel methods such as global approximation and inverse localization, which have broad implications across mathematical physics. His scientific awards include: ERC Starting Grant (2014–2019) Barcelona Dynamical Systems Prize (2015) Plenary Speaker, European Congress of Mathematics (2016) The Floer Lectures (2019) MINT Distinguished Lectures, Tel Aviv (2020) Plenary Speaker at major national and international mathematical societies (2021–2024) EMS Distinguished Speaker (2023) Peralta-Salas advises and collaborates extensively with researchers including Alberto Enciso, Robert Cardona, Eva Miranda, and others. His work has been supported by prestigious grants such as the ERC Starting Grant. He has published over 100 papers in top journals including Annals of Mathematics , Acta Mathematica , PNAS , and Physical Review Letters . He is a member of editorial boards for journals such as Revista Matemática Iberoamericana and Journal of Dynamics and Differential Equations , and serves on the scientific advisory board of IMTECH (UPC-Barcelona Tech). He leads the research group on Differential Geometry and Geometric Mechanics at ICMAT, which focuses on geometric structures in dynamical systems and PDEs. The group explores topics like Beltrami fields, vortex dynamics, and topological aspects of eigenfunctions. Their work is highly collaborative and interdisciplinary, bridging pure mathematics and theoretical physics.

• Computational Mechanics
• Numerical Simulation
• Shape Optimization
• +۱۲ مورد دیگر

Stefanie Elgeti is Associate Professor and Private Lecturer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. She previously held a professorship in lightweight design at TU Vienna starting in 2019. Her research integrates computational mechanics with manufacturing process optimization, focusing on plastics extrusion, injection molding, and high-pressure die casting. Diploma in Mechanical Engineering, majoring in 'Manufacturing Techniques for Microsystems' PhD (2011): 'Free-Surface Flows in Shape Optimization of Extrusion Dies' Habilitation (2016): 'CAD-Conforming Finite Element Methods in Engineering Design' Her research centers on solving inverse problems in manufacturing through numerical simulation. She employs advanced techniques such as free-surface flow modeling, non-Newtonian material models, spline-based finite elements, and PDE-constrained shape optimization. Her group simulates entire process chains from filling to solidification and warpage prediction, enabling design optimization of cavities and cooling systems. The recent publications (2022–2024) reveal a strong trend toward integrating artificial intelligence—particularly physics-informed neural networks and Bayesian optimization—into traditional simulation workflows. There is increasing emphasis on warpage compensation, shape optimization of extrusion dies, and modeling of biomedical and environmental systems, showcasing a broadening scope from industrial manufacturing to interdisciplinary applications. She is actively involved in academic service, having served as vice-spokesperson of GAMM-Juniors (2013–2014) and currently co-chairing the ECCOMAS Young Investigator Group. While no formal awards are listed, her leadership roles and editorial contributions reflect significant recognition in the computational mechanics community. Prof. Elgeti advises students and leads multiple research initiatives at CATS, including work groups focused on production engineering, fluid-structure interaction, and INTERESST. Her team develops model hierarchies and digital twins for industrial processes, aiming to bridge simulation and real-world manufacturing through intelligent, adaptive systems.

Qi Shuمشاهده پروفایل

استاد

• Thermal Engineering
• Fluid Dynamics
• Materials Science
• +۴ مورد دیگر

Qi Shu is a Professor in the Compound Semiconductor Technology department at RWTH Aachen University . His research focuses on thermal engineering, fluid dynamics, and computational physics, particularly in advanced semiconductor technology applications. Current research interests include ferrofluid-based cooling systems, particle suspension mechanics, and heat transfer optimization in complex flows. His work combines experimental studies and numerical simulations to analyze thermal convection in particle-laden systems, non-spherical particle behavior, and shear flow effects on conductivity. Recent publications highlight trends in multiphysics modeling (LBM-DEM-FEM coupling), electromagnetic cooling systems using ferrofluids, and detailed investigations of particle rotation dynamics in nanofluids. These studies span disciplines including materials science, computational fluid dynamics, and applied thermal engineering. Based at the Central Laboratory for Micro- and Nanotechnology (ZMNT), his lab investigates semiconductor technology with a focus on thermal management solutions for high-performance electronic systems and nanoscale fluid dynamics.

Gerhard Starkeمشاهده پروفایل

استاد

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
• +۷ مورد دیگر

Gerhard Starke is a Professor of Numerical Mathematics at the Faculty of Mathematics, University of Duisburg-Essen. He leads the Numerical Mathematics workgroup, focusing on the development and analysis of finite element methods for solving complex mathematical and mechanical problems. His research spans multiple areas of computational mathematics with applications in solid mechanics and optimization. Professor Starke's research interests center around numerical analysis of finite element methods , reconstruction-based a posteriori error estimators , and mixed finite element methods . His work has significant applications in shape optimization , solid mechanics , hyperelasticity , plasticity , and frictional contact problems . His research combines theoretical mathematical analysis with practical computational approaches to solve challenging engineering problems. Analysis of Professor Starke's recent publications (2017-2025) reveals a strong focus on stress-based methods, shape optimization, and advanced finite element formulations. His work demonstrates a consistent progression from fundamental numerical analysis to increasingly complex applications in solid mechanics. Key trends include the development of constrained first-order system least mean approximation for shape optimization, superconvergent DPG approximations in linear elasticity, and stress-based methods for variational inequalities in contact mechanics. Professor Starke has secured significant third-party funding for his research, including current projects funded by the German Research Foundation (DFG) within Priority Programs 1748 and 1962. These projects focus on stress approximation in hyperelastic material models and stress-based methods for variational inequalities in solid mechanics. His research collaborations span multiple institutions including Humboldt University Berlin, University of Duisburg-Essen's Institute of Mechanics, and international partners in Switzerland. Head of Numerical Mathematics workgroup at University of Duisburg-Essen Principal investigator for multiple DFG-funded projects since 1999 Supervisor for numerous doctoral students including Laura Hetzel, Henrik Schneider, and Kemal Suntay Extensive international collaborations with researchers across Europe The Numerical Mathematics workgroup led by Professor Starke maintains active collaborations with mechanics researchers at the University of Duisburg-Essen and international institutions. The group has participated in significant research initiatives including the DFG Research Training Group 615 and multiple Priority Programs. Current work focuses on applying advanced numerical methods to challenging problems in computational solid mechanics and shape optimization.

• Physical Simulations
• Geometry Processing
• Fluid Dynamics
• +۷ مورد دیگر

Dr. Marcel Padilla is a Postdoctoral Researcher at the Interactive Geometry Lab within the Department of Computer Science at ETH Zürich, supported by the Feodor-Lynen Fellowship from the Alexander von Humboldt Foundation. Previously, he completed his PhD at TU Berlin under Professors Ulrich Pinkall and Peter Schröder, focusing on solar corona modeling and geometry processing. Education: PhD in Computer Science, TU Berlin (2023) MSc in Mathematics, TU Berlin (2018) BSc in Mathematics, TU Berlin (2016) Research Interests: Marcel’s work spans physical simulations, geometry processing, fluid dynamics, and plasma modeling. He explores applications in computer graphics, such as modeling solar corona dynamics and vortex filament behavior. His methods often leverage discrete exterior calculus and variational principles to achieve computational efficiency and accuracy. Key Contributions: Recent work includes Exact 3D Green’s Function Integrations on Triangles (2025), Going with the Flow (2024), and Filament Based Plasma (2022). These studies address challenges in fluid-structure interaction, plasma visualization, and efficient numerical integration. Awards: Feodor-Lynen Fellowship (Alexander von Humboldt Foundation) Teaching & Outreach: Marcel has taught courses on Discrete Differential Geometry at ETH Zürich and Geometry Processing at TU Berlin. He also develops practical resources for Houdini programming and scientific presentation design. Labs & Collaborations: His research is supported by institutions like the DFG Collaborative Research Center TRR 109 and the Einstein Foundation Berlin. He actively contributes to open-source projects, including implementations of his publications on GitHub.

Orkun Furatمشاهده پروفایل

مدرس

• Machine Learning
• Stochastic Modeling
• Spatial Statistics
• +۵ مورد دیگر

Dr. Orkun Furat is a Lecturer at the Institute of Stochastics, University of Ulm, Germany, where he conducts research at the intersection of machine learning, stochastic modeling, and image analysis for materials science applications. His work focuses on developing advanced computational methods to characterize and reconstruct 3D microstructures from 2D image data, with significant contributions to battery materials and particle systems. His primary research interests include generative adversarial networks (GANs) and spatial stochastic models for tomographic image analysis of functional materials. He has pioneered techniques for super-resolving microscopy images, quantifying electrode degradation in batteries, and modeling particle morphology/separation processes in mineral processing. His interdisciplinary approach bridges statistics, computer science, and materials engineering through rigorous mathematical frameworks. Recent publications (2024-2025) reveal a concentrated focus on lithium-ion and all-solid-state battery technologies, particularly analyzing how operating conditions (charge rate, temperature, cycling) induce electrode degradation. Simultaneously, his particle systems research employs multidimensional stochastic models to optimize mineral beneficiation processes like flotation, using copula-based approaches for particle property distributions. Dr. Furat actively supervises seminar students in generative machine learning and spatial stochastic modeling while teaching core courses including Point Processes and Advanced Statistics. His research impact is evidenced by numerous invited talks at premier venues like the Dagstuhl Seminar (2025) and European Congress for Stereology (2025), where he presents as a plenary speaker on AI-driven microstructure reconstruction. Collaborating with interdisciplinary teams across materials science and engineering, his work on digital twins for battery electrodes and virtual materials testing has been featured in University of Ulm press reports (2024) highlighting applications in efficient battery recycling and sustainable material design. Current projects integrate generative AI with stochastic geometry to solve industrial-scale challenges in energy storage and mineral processing.

• Parametric design in structural engineering
• CAD-integrated design and analysis
• Lightweight construction
• +۷ مورد دیگر

Dr.-Ing. Ann-Kathrin Goldbach is a Professor at the Chair of Statics and Dynamics within the School of Engineering and Design at the Technical University of Munich . She has served as Deputy Chair since 2021 and Habilitation candidate since 2022, with a focus on CAD-integrated design cycles for structural membranes and isogeometric analysis. Her roles include Chief Representative for Equality of Women in Science since 2023. Master's Degree in Civil Engineering, Technical University of Munich (2013) Bachelor's Degree in Civil Engineering, Technical University of Munich (2009-2012) PhD in Structural Engineering, Technical University of Munich (2021) Research Focus : Goldbach specializes in parametric design workflows, isogeometric analysis, and lightweight membrane structures. Her work bridges CAD/CAE integration, wind engineering, and multidisciplinary optimization for additive manufacturing. Key projects include the FlexWing and MistralWind initiatives, emphasizing fluid-structure interaction and digital twins for durable construction systems. Article Trends : Recent publications highlight CAD-integrated parametric modeling, nonlinear behavior of membrane structures, fluid-structure interaction under wind loads, and digital twin applications in construction. Collaborations with TUM faculty and international institutions reflect her multidisciplinary approach. Teaching Contributions : She leads courses on Isogeometric Analysis , Computational Design , and Membrane Workshop , while supervising student theses and project-based learning initiatives. 2024: Doce et Delecta Teaching Award for Membrane Workshop 2023: TUM Learning Professional Certification 2019: IASS Hangai Prize for CAD-integrated design research

Matthias Wesslingمشاهده پروفایل

استاد

• Membrane Technology
• Process Engineering
• Electrochemical Synthesis
• +۲ مورد دیگر

Prof. Dr.-Ing. Matthias Wessling is Vice-Rector for Transfer at RWTH Aachen University and holds the Chair for Chemical Process Engineering. An Alexander von Humboldt Professor since 2010, he directs research on membrane systems and process intensification. His research integrates selective mass transport with chemical conversion across micro-to-macro scales, developing advanced membrane systems for water treatment, energy storage, and carbon capture. Current innovations include electrochemical reactors and hybrid membrane processes. Wessling received the Gottfried Wilhelm Leibniz Prize (2019) and ERC Advanced Grant (2016) for transformative work in membrane science. As Editor of the Journal of Membrane Science, he shapes research dissemination in separation technologies.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Bo Wang represents multiple academic researchers across various institutions worldwide, with prominent affiliations at Tsinghua University, Michigan State University, University of York, and Hamad Bin Khalifa University. The collective profile shows active research contributions across computer science, engineering, and applied mathematics disciplines. These researchers hold faculty positions primarily as Professors or Associate Professors in departments including Computer Science, Electrical Engineering, and Applied Mathematics. Research interests span artificial intelligence, machine learning, biomedical engineering, wireless communications, and computational mathematics. The collective publication record demonstrates significant contributions to federated learning, medical image analysis, control systems, and biomolecular modeling. Recent work shows particular strength in developing novel AI architectures for scientific computing and healthcare applications. Publication trends reveal increasing interdisciplinary collaboration, with substantial output in top-tier journals and conferences from 2020-2025. Key research areas include deep learning for medical imaging, optimization algorithms for engineering systems, and computational methods for biological data analysis. The collective work shows strong methodological innovation combined with practical applications. No specific scientific awards were identified in the disambiguation page, though the high publication volume in reputable venues suggests recognition within respective fields. The collective research demonstrates strong connections between theoretical advances and practical implementations across multiple domains. While specific student information isn't provided in the disambiguation page, the extensive publication record suggests active supervision of graduate students. Research appears well-funded through various institutional and potentially national funding mechanisms, supporting the diverse range of projects spanning theoretical and applied domains.

Winnifried Wollnerمشاهده پروفایل

استاد

• Optimization
• Applied Mathematics
• PDE-Constrained Optimization
• +۷ مورد دیگر

Prof. Dr. Winnifried Wollner is a Professor of Optimization at the University of Hamburg's Department of Mathematics, within the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on optimization with partial differential equations (PDEs), numerical methods for PDEs, and computational mechanics, particularly in the context of shape optimization and fracture mechanics. He holds a PhD from Heidelberg University (2010) and has held academic positions at TU Darmstadt and the University of Hamburg. He is actively involved in editorial boards of journals like the SIAM Journal on Control and Optimization and the OPTPDE Problem Collection. His work emphasizes adaptive numerical methods, phase-field fracture modeling, and optimization under constraints. Awards include the 2014 Teacher of the Summer Term award at Hamburg. He teaches courses on optimization, numerical methods, and PDE-constrained problems, and leads research projects funded by DFG and the Excellence Cluster CLICCS. Professional roles include membership in the MIN Faculty Council, the Department's Equal Opportunities Team, and leadership of the GAMM Student Chapter. His research spans interdisciplinary applications in engineering and computational science, with over 66 publications in top journals. Current projects include the DFG Research Training Group 2583 on fluid dynamics and optimization, and the SPP 1962 project on phase-field fracture simulation.

Wolfgang Polifkeمشاهده پروفایل

استاد

• Thermoacoustic Instabilities
• Aeroacoustics
• Combustion Technology
• +۳ مورد دیگر

Prof. Dr. Wolfgang Polifke is a Professor in the Department of Thermofluid Dynamics at the TUM School of Engineering and Design, Technische Universität München. His research focuses on thermoacoustic instabilities, aeroacoustics, and turbulent flow dynamics. He earned his doctorate from the City University of New York in 1990, specializing in turbulent flow helicity, and later held a research position at ABB in Switzerland. He joined TUM in 1999 and served as Editor-in-Chief of the International Journal of Spray and Combustion Dynamics (2016–2021). His awards include the ASME/IGTI Best Paper Award (2024), the School of Engineering Supervisory Award (2023), and Fellow of The Combustion Institute (2021). His work explores combustion instabilities, flame dynamics, and hydrogen-enriched fuels, with contributions to computational fluid dynamics and machine learning applications in combustion analysis. Education: Bachelor/Master studies in Physics at University of Regensburg, University of Colorado Boulder, and City University of New York (1981–1987) PhD in Physics from City University of New York (1990), dissertation on turbulent flow helicity Research Interests: Combustion instabilities, thermoacoustic modeling, aeroacoustics, turbulent flame dynamics, hydrogen-enriched combustion systems, and application of advanced numerical methods (e.g., LES, machine learning) to combustion analysis. Publications: Recent work addresses hydrogen effects on flame dynamics, entropy wave generation, and nonlinear flame behavior. Key trends include exploring sustainable fuels, improving combustion stability, and developing predictive models for industrial applications. Awards: Best Paper Award (Combustion, Fuels & Emissions Committee of ASME/IGTI) – 2024 Golden Apprenticeship Teaching Award – 2002/2004/2006 Fellow of The Combustion Institute – 2021 Grants & Labs: Leads the Thermofluid Dynamics group at TUM, focusing on experimental and numerical studies of combustion systems. Collaborates with industry on combustion instability mitigation and clean energy technologies.

Evangelos Tsotsasمشاهده پروفایل

استاد

• Drying Technology
• Particle Formulation
• Thermal Process Engineering
• +۳ مورد دیگر

Evangelos Tsotsas is a Full Professor and Chair of Thermal Process Engineering at the Faculty of Process and Systems Engineering, Otto von Guericke University Magdeburg since 1994. He obtained his Dr.-Ing. in 1985 and Habilitation in 1990 from Karlsruhe Institute of Technology, following a Dipl.-Ing. in Mechanical Engineering from Aristotle University Thessaloniki (1981). His research focuses on drying technology , particle formulation , and thermal process modeling , with particular emphasis on spray fluidized bed agglomeration, granulation, coating, pore network models, Monte-Carlo methods, DEM-CFD coupling, and population balance approaches. The group maintains extensive facilities for particle characterization, 3D imaging, and process monitoring. Recent publications highlight applications in single-droplet evaporation modeling, pore-scale freeze-drying analysis, and fluidized bed discharge dynamics. Scientific Awards : 2018: Professor Arun S. Mujumdar Medal for Outstanding Mentorship and Sustained Excellence in Drying Research 2017: Honorary Professor at University of Chemical Technology and Metallurgy, Sofia 2017: Award for Excellence in Drying R&D 2016: Best Research Award at International Drying Symposium 2013: Otto von Guericke Research Award 2010: Award for Excellence in Drying Research 2002: Award for Innovation in Drying Research As Editor-in-Chief of the International Journal of Chemical Engineering and editor of the five-volume series Modern Drying Technology , he has shaped academic discourse in drying and particulate processes. His research group has advised numerous PhD and postdoctoral researchers including Rui Wang (CSC scholarship) and Stutee Bhoi (Humboldt Foundation). The group maintains technical collaborations with institutions such as Novozymes AS , Nestlé , BASF , Evonik , and Princeton University , with industry partnerships spanning from Sanofi-Aventis to Glatt Ingenieurtechnik . His editorial roles include Drying Technology Journal , Processes , and ChemEngineering .

Alexander Haggمشاهده پروفایل

پژوهشگر

• Evolutionary Computation
• Quality Diversity Algorithms
• Machine Learning
• +۷ مورد دیگر

Dr. Alexander Hagg is a Researcher at Hochschule Bonn-Rhein-Sieg (H-BRS) in the Department of Engineering and Communication, affiliated with the Institute of Technology, Resource Conservation and Energy Efficiency (TREE). With a PhD from Leiden University, he specializes in evolutionary computation, machine learning, and computer-aided ideation, focusing on applications in climate adaptation, energy efficiency, and resource conservation. His work bridges theoretical research with practical applications across multiple domains including urban planning, computational chemistry, and robotics. PhD in Computer Aided Ideation (2017-2020) - Leiden University Master's in Autonomous Systems (2013-2016) - Bonn-Rhein-Sieg University of Applied Sciences Bachelor's in Computer Science (2009-2013) - Bonn-Rhein-Sieg University of Applied Sciences Dr. Hagg's research centers on efficient computer-aided ideation algorithms that help understand early on what good solutions to complex problems might look like. His primary focus is on quality diversity algorithms, which efficiently create diverse sets of high-performing solutions to inform engineers' intuition. His work spans multiple application domains including urban climate resilience, structural chemistry, robotics, and digital twins for urban planning. He is particularly interested in how AI can serve as a co-designer, helping humans explore and understand complex optimization and data domains. His recent publications reveal a strong trend toward applying evolutionary computation and machine learning to real-world engineering problems. A significant portion of his work focuses on computational chemistry and force-field parameter optimization, where machine learning substitutes expensive molecular dynamics calculations. Another major theme involves quality diversity algorithms applied to urban planning and building design. His research consistently emphasizes practical applications in climate adaptation and resource efficiency, with growing interest in digital twins for urban sustainability. 2023: GECCO Best Paper Award (honourable mention) 2022: ACM SIGEVO Best Dissertation Award (honourable mention) 2017: AFCEA Studienpreis 2017: GECCO Best Student Paper Award (honourable mention) 2016: RoboCup Symposium Best Paper Award Dr. Hagg has led multiple research projects including OpenSKIZZE (open-source tools for climate-adaptive urban development), Digital Twin-4-Multiphysics Lab (DT4MP), and KISs-BiS (AI for elite sports). He teaches courses on evolutionary computation, AI, and machine learning, and has developed workshops on digital twins for urban sustainability. His research is supported by collaborations with institutions including University of Siegen, University College London, University of Leiden, and various Fraunhofer institutes. He leads the Digital Twin-4-Multiphysics Lab (DT4MP) which focuses on urban digital twins and multiphysics twins for industry. Dr. Hagg is also active in several research groups including the Computational Chemistry Working Group at H-BRS and serves as a representative for H-BRS in the GeoIT Round Table NRW. His work often involves interdisciplinary teams spanning computer science, engineering, urban planning, and environmental science.

Stefan Ulbrichمشاهده پروفایل

استاد

• Nonlinear Optimization
• PDE-Constrained Optimization
• Optimal Control
• +۳ مورد دیگر

Stefan Ulbrich is a Professor of Nonlinear Optimization at the Department of Mathematics, Technische Universität Darmstadt. His research focuses on optimization theory, algorithms, and applications, particularly in PDE-constrained optimization, optimal control, and numerical methods. He co-authored influential textbooks on nonlinear and PDE-constrained optimization. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in fluid dynamics, electromechanical systems, and energy processes. Key contributions include robust optimization frameworks, sensitivity analysis for complex systems, and model reduction techniques. His research emphasizes interdisciplinary applications, such as electric machine design, industrial robotics, and sustainable energy systems. Research Interests: Nonlinear Optimization and Numerical Methods PDE-Constrained Optimization and Optimal Control Robust Design and Uncertainty Quantification Applications in Engineering and Energy Systems Publications: Ulbrich's work spans over 100 peer-reviewed articles and books. Notable contributions include the textbook *Nichtlineare Optimierung* (with Michael Ulbrich) and research on optimal control of fluid flows, robust optimization of electromechanical systems, and Bayesian model calibration. His recent focus includes parallel-in-time optimization, topology optimization under uncertainty, and exergy-based process optimization in green energy systems. Awards and Recognition: While specific awards are not explicitly listed, his leadership in optimization theory and impactful contributions to applied mathematics and engineering reflect sustained academic excellence.