Find Academic Supervisors

• Signal Processing
• Image Processing
• Machine Learning
• +7 more

Jian Qiu Zhang is a Professor at Fudan University in the School of Information Science and Technology , affiliated with the Key Laboratory for Information Science of Electromagnetic Waves and Research Center of Smart Networks and Systems in Shanghai, China. Previously, he was at the University of Greenwich (1999-2002) and earned his PhD in 1996 from Harbin Institute of Technology in the Department of Electrical Engineering . His research interests span Signal Processing , Image Analysis , and Machine Learning , with a focus on applications in Biomedical Imaging , Hyperspectral Data Analysis , and Smart Network Systems . His work often integrates Wavelet Transforms , Tensor Decomposition , and Bayesian Filtering to solve complex problems in Medical Imaging and Wireless Sensor Networks . Recent publications highlight advancements in Deep Learning for 3D Ultrasound , PARAFAC Decomposition , and Graph Neural Networks for Hyperspectral Classification . His technical contributions include Adaptive Filtering Algorithms , Nonlinear Unmixing , and Wavelet-Based Sensor Analysis . Key collaborations include researchers from institutions such as Harbin Institute of Technology, University of Greenwich, and international teams in IEEE Transactions and IGARSS conferences.

• Numerical analysis
• Machine learning
• Scientific computing
• +3 more

Dr. Dmitry Kabanov is a Researcher at the Institute for Analysis and Numerical Analysis, part of the Department of Mathematics and Computer Science at the University of Münster. His primary affiliations include membership in Mathematics Münster, a research cluster focusing on interdisciplinary mathematical challenges. His research expertise spans numerical analysis, machine learning, and scientific computing, with a focus on high-dimensional problems, fluid dynamics, and combustion processes. Key research interests include developing numerical methods for complex systems, applying machine learning to fluid dynamics, and analyzing detonation stability through computational techniques. His work bridges theoretical mathematics with practical applications in physics and engineering, leveraging advanced computational frameworks to address real-world phenomena. Publications highlight contributions to detonation stability analysis, neural network-based fluid flow modeling, and interdisciplinary projects like the MaRDI initiative for scientific computing interoperability. Collaborations involve international teams tackling challenges in computational physics, machine learning, and high-dimensional approximation methods. Dr. Kabanov’s research also emphasizes resolving parameters in elastic anisotropy and developing minimal hyperbolic systems for unstable shock waves. He is affiliated with the Applied Mathematics Münster group and contributes to the Institute’s advanced seminars and numerical analysis projects. His office is located at Orleans-Ring 10, and he can be reached via email at dmitry.kabanov@uni-muenster.de. Administrative support is provided by Ms. Silvia Wernke.

Nico SneeuwView profile

Professor

• Geodesy
• Satellite Gravimetry
• Hydrology
• +3 more

Prof. Dr.-Ing. Nico Sneeuw is a Professor and Vice Dean of the Geodetic Institute at the University of Stuttgart's Faculty of Aerospace Engineering and Geodesy. His research focuses on geodetic applications of satellite data, particularly in gravimetry, hydrology, and remote sensing. He leads projects like STREAM and HydroSat , advancing global water storage monitoring and runoff estimation. His work integrates satellite missions (GRACE, GRACE-FO, CryoSat-2) with advanced statistical and computational methods to address climate change impacts on water resources. Key projects include developing spatial downscaling techniques for GRACE data, improving satellite altimetry waveform processing, and quantifying flood dynamics using remote sensing. He has contributed to orbital optimization for future gravity missions and pioneered probabilistic drought characterization frameworks. His research bridges geophysics, hydrology, and computational methods, with applications in environmental monitoring and disaster assessment. Publications highlight innovations in inverse problem solving, gravitational modeling, and sensor data fusion. He collaborates internationally on initiatives like the ESA St3TART project and coordinates interdisciplinary teams for satellite-derived hydrological products.

Phaedon-Stelios KoutsourelakisView profile

Professor

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +3 more

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.

Stephanie HofmannView profile

Researcher

• Mathematics Education
• Artificial Intelligence
• Data Science
• +12 more

Dr. Stephanie Hofmann is a researcher at the Faculty of Mathematics of the Karlsruhe Institute of Technology (KIT), affiliated with the Department of Didactics and the Working Group 5: Computational Science and Mathematical Methods at KIT's Scientific Center for Computing (SCC). Her work bridges mathematics education with artificial intelligence , focusing on digital learning materials and data science pedagogy . She has published extensively on AI applications in stochastic lessons, predictive text systems, and problem-based learning units.

Jin ZhangView profile

Researcher

• Computational Finance
• Derivative Pricing
• Portfolio Optimization
• +7 more

Jin Zhang is a Researcher in the Department of Quantitative Methods at the Faculty of Business and Economics, University of Basel. He holds a PhD in Computational Finance from the University of Essex, UK, awarded under the European Commission's Marie Curie Actions program, and has academic backgrounds in Engineering and Applied Statistics from institutions in China and Australia. Education: Bachelor of Engineering, Beijing University of Posts and Telecommunications, China M.Sc. in Mathematics and Applied Statistics, University of Wollongong, Australia PhD in Computational Finance, University of Essex, UK (Marie Curie Actions grant recipient) Jin Zhang's research lies at the intersection of computational methods and financial modeling. His work emphasizes trading strategy design , portfolio optimization , and derivative pricing using advanced numerical and computational techniques. He applies methods such as Laplace transforms, finite difference schemes, clustering algorithms, and copula modeling to solve complex financial problems. His interdisciplinary approach bridges applied mathematics, statistics, and finance. The analysis of his publications reveals a consistent focus on enhancing financial decision-making through computational innovation. His work spans algorithmic option pricing, risk-aware portfolio construction, and the application of machine learning-inspired clustering in finance. A recurring theme is the use of numerical and heuristic optimization to improve accuracy and stability in financial models. Scientific Awards and Honors: Marie Curie Actions Grant, European Commission Jin Zhang has been actively involved in research projects supported by competitive funding, notably the EC’s Marie Curie program. While no formal advising roles are listed, his collaborations with researchers like Dietmar Maringer and Songping Zhu indicate active participation in academic research teams. His publications in journals such as Expert Systems with Applications and Applied Mathematics and Computation reflect strong technical contributions to quantitative finance. Laboratories and Research Teams: Jin Zhang is affiliated with COMISEF (Computational Optimization and Modeling for Financial and Economic Applications), a European research network focused on training and knowledge transfer in computational finance. He contributes to working papers and collaborative research under this network, indicating engagement with an international academic community.

Alon Peled-CohenView profile

Senior Lecturer

• Machine Learning
• Statistical Learning
• Online Learning
• +3 more

Dr. Alon Peled-Cohen is a senior lecturer at the School of Electrical Engineering - Systems within The Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University, and a researcher at Google Research Tel-Aviv. Dr. Peled-Cohen received his PhD in Machine Learning from the faculty of Industrial Engineering & Management at the Technion - Israel Institute of Technology under the supervision of Professor Tamir Hazan. Dr. Peled-Cohen's research focuses on the intersection of statistical learning, online learning, and decision-making under uncertainty. His work particularly emphasizes the connections between online learning and reinforcement learning, with applications in control theory and optimization. His research addresses fundamental challenges in learning from sequential data, making decisions with limited information, and developing algorithms with provable performance guarantees. He has made significant contributions to understanding regret bounds in various learning settings and has developed novel algorithms for challenging control and optimization problems. Analysis of Dr. Peled-Cohen's publication record reveals a strong focus on theoretical machine learning, particularly in online learning, reinforcement learning, and control theory. His work consistently addresses fundamental questions about algorithmic performance, often establishing tight regret bounds for various learning settings. A notable theme throughout his research is the application of optimization techniques to challenging learning problems, with several papers focusing on linear quadratic control and Markov decision processes. His collaborations span multiple institutions and include prominent researchers in the machine learning community. Dr. Peled-Cohen has published in top-tier machine learning conferences including ICML, NeurIPS, UAI, and AAAI. His research has addressed important problems in online learning, reinforcement learning, and control theory, contributing both theoretical insights and practical algorithms. As a senior lecturer at Tel Aviv University and researcher at Google Research Tel-Aviv, Dr. Peled-Cohen bridges academic research with practical applications. His work has implications for various domains requiring sequential decision-making under uncertainty, including robotics, recommendation systems, and autonomous control systems. While specific grant information is not provided in the available materials, his publications in top venues suggest successful research funding. Dr. Peled-Cohen is actively involved in both academic and industrial research, contributing to the advancement of machine learning theory while maintaining strong connections with practical applications through his position at Google Research.

Jürgen DölzView profile

Professor

• Uncertainty Quantification
• Computational Electromagnetism
• Isogeometric Analysis
• +3 more

Jürgen Dölz is a Professor at the Institut für Numerische Simulation (University of Bonn), specializing in uncertainty quantification, computational electromagnetism, and numerical methods for partial differential equations. His research bridges theoretical mathematics with engineering applications, focusing on efficient simulation techniques and data-driven modeling. Current research: Shape uncertainty in Maxwell eigenproblems, fast kernel methods, quantum computing algorithms Teaching: Advanced topics in scientific computing, randomized sketching, numerical simulation Projects: DFG-funded data-driven electromagnetic resonator modeling, CRC 1639 NuMeriQS (projects B04 and A03) Recent publications address Bayesian inverse problems, spectral clustering under uncertainty, and isogeometric boundary element methods for acoustic and electromagnetic wave scattering. He leads the development of Bembel (Boundary Element Based Engineering Library) and contributes to H-matrix acceleration techniques for rough random fields.

Raphael WittkowskiView profile

Professor

• Active Soft Matter
• Theoretical Physics
• Statistical Physics
• +12 more

Raphael Wittkowski is a Professor of Theory of Active Soft Matter at RWTH Aachen University and the DWI – Leibniz Institute for Interactive Materials. He leads a research group focused on theoretical and computational physics of active systems, with strong affiliations in the Department of Physics at RWTH Aachen. Educational Background: Bachelor of Science in Mathematics, Heinrich Heine University Düsseldorf (2009) Bachelor of Science in Physics, Heinrich Heine University Düsseldorf (2009) Master of Science in Physics, Heinrich Heine University Düsseldorf (2010) PhD in Physics, Heinrich Heine University Düsseldorf (2012) Research Interests: His work spans active soft matter, acoustofluidics, microfluidics, microrobotics, programmable and intelligent materials, 3D bioprinting, and theoretical modeling. He investigates systems driven by sound or light, develops simulation software (AcoDyn), and explores applications in medicine and engineering. His research integrates statistical physics, field theory, and artificial intelligence concepts in material design. Publication Trends: Recent publications emphasize field theories for active matter, computational modeling of active Brownian particles, intelligent materials, and biomedical applications of microrobots. His work frequently appears in high-impact journals such as Science Advances , Nature Communications , and Physical Review Letters , reflecting a strong focus on theoretical innovation and interdisciplinary applications. Scientific Contributions: Development of AcoDyn, a Rust-based software for acoustofluidic and microfluidic simulations Leadership in major research projects including Sonocraft and CRC 1459 B01 Heisenberg Grant recipient for research in active soft matter and statistical physics Advising and Grants: He advises PhD students such as Adrian Paskert and leads a dynamic research group. He has secured competitive funding, including a Heisenberg Grant, and is involved in collaborative, interdisciplinary projects that bridge physics, engineering, and medicine. Research Groups and Projects: He leads the Theory of Active Soft Matter group at RWTH Aachen. Key projects include Sonocraft (ultrasound-based 3D bioprinting), CRC 1459 B01 (intelligent light-driven microsystems), and foundational work on active matter theory and simulation frameworks.

Holger HeitschView profile

Researcher

• Stochastic Optimization
• Gas Network Optimization
• Probabilistic Constraints
• +7 more

Dr. Holger Heitsch is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany. He works in the research group Nonlinear Optimization and Inverse Problems under Prof. Dietmar Hömberg's leadership. Currently, he is a research associate in the collaborative project SFB Transregio 154 focused on mathematical modeling and optimization of gas networks. Research Interests : His work centers around stochastic optimization , particularly in energy systems and gas network modeling . Key areas include probabilistic constraints , scenario tree generation , and uncertainty quantification . He has developed computational methods like the Matlab-based spherical cap discrepancy code for academic use. Publications : Spanning 2003–2025, his research addresses Stochastic modeling in energy portfolios Scenario reduction algorithms Probabilistic capacity planning for gas networks Computational methods for quasi-Monte Carlo integration Labs & Teams : Collaborates with Prof. Römisch, Prof. Henrion, and international researchers on gas network optimization and stochastic programming. His work is applied in power management , energy economics , and network flow problems.

Elizabeth QianView profile

Assistant Professor

• Scientific machine learning
• Model reduction
• Multifidelity methods
• +5 more

Elizabeth Qian is a tenure-track Assistant Professor at Georgia Tech with joint appointments in the Schools of Aerospace Engineering and Computational Science and Engineering. She holds a visiting Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2023. Her research develops computational methods for scientific machine learning, model reduction, and multifidelity approaches to accelerate engineering decision-making. Education : PhD (2021), SM (2017), and SB (2014) in Aerospace Engineering from MIT Her work focuses on enabling efficient simulations for complex systems through model reduction , scientific machine learning , and multifidelity methods . Recent publications address Bayesian inverse problems, neural operator learning trade-offs, and ensemble Kalman methods. Scientific awards include the 2025 NSF CAREER Award, 2024 AFOSR YIP Award, 2023 TUM-IAS Fellowship, 2022 SIAM Student Paper Prize, and 2014 Hertz/NSF/Fulbright fellowships. She mentors GT PhD researchers and contributes to diversity initiatives in engineering communities.

Ingo SteinwartView profile

Professor

• Statistical Learning Theory
• Kernel-based learning algorithms
• Cluster Analysis
• +6 more

Ingo Steinwart is a Full Professor and Head of the Institute for Stochastics and Applications at the University of Stuttgart, within the Faculty of Mathematics and Physics. He holds a Chair for Stochastics and has held academic positions since 2010, including roles at Los Alamos National Laboratory and the University of California, Santa Cruz. His research focuses on statistical learning theory, kernel-based methods, cluster analysis, and neural networks, with significant contributions to the theoretical foundations of machine learning. Education: He earned a Doctorate (Dr. rer. nat.) in Mathematics from Friedrich-Schiller University, Jena (2000) and a Diploma in Mathematics from Carl-von-Ossietzky University, Oldenburg (1997). Research Interests: Steinwart's work emphasizes rigorous mathematical analysis of learning algorithms, including kernel methods, support vector machines (SVMs), and density estimation. His research explores topics like the capacity of function classes, generalization bounds, and applications to large-scale data. Notable contributions include advancements in SVM theory, reproducing kernel Hilbert spaces, and cluster analysis techniques. Publications: His work spans foundational papers on SVMs, kernel methods, and learning theory, with a focus on theoretical guarantees and algorithmic efficiency. Recent contributions address topics like adaptive learning rates, neural network initialization, and the theoretical limits of kernel-based methods. Grants & Editorial Roles: Steinwart serves as Associate Editor for journals such as the Journal of Complexity and the Annals of Statistics. He has organized conferences like COLT and contributed to software tools like liquidSVM for SVM implementation. Labs & Software: He leads development of software packages like liquidSVM (for SVMs) and liquidCluster (for cluster analysis), emphasizing computational efficiency and automated hyperparameter selection.

Thorsten HohageView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Regularization Theory
• +4 more

Prof. Thorsten Hohage is a Full Professor at the Institute for Numerical and Applied Mathematics, Georg-August University of Göttingen since 2009. He leads the Inverse Problems Group, focusing on computational and theoretical aspects of inverse problems in partial differential equations, regularization theory, and numerical algorithms. His research spans applications in optics, medical imaging (MRI), helioseismology, and X-ray phase contrast imaging. **Career**: Full Professor (W3) since 2009 Associate Professor (W2) 2007–2009 Junior Professor 2002–2007 Postdoc at Zuse Institute Berlin (2000–2002) Research Assistant at Johannes-Kepler University Linz (1996–2000) **Research Interests**: Inverse scattering and PDE-based inverse problems Regularization theory for statistical and deterministic inverse problems Efficient numerical methods (e.g., learned infinite elements) Applications in helioseismology, phase retrieval, and medical imaging **Notable Contributions**: Developed methodologies for transparent boundary conditions, variational source conditions, and regularization techniques in Besov spaces. His work bridges theory and practice in computational inverse problems. **Affiliations**: Member of the Max Planck Institute for Solar System Research (since 2017) and part of the GGNB Physics of Biological and Complex Systems program.

Navid AnsariView profile

Researcher

• Deep Learning
• Generative Models
• Optimization
• +4 more

Navid Ansari is a doctoral researcher at the Max Planck Institute for Informatics (MPI-INF) and Saarland University, affiliated with the Artificial Intelligence Aided Design and Manufacturing group under the Computer Graphics department. His work bridges academia and industry, with internships at Amazon AWS and the Max Planck Institute for Brain Research. PhD in Computer Science (2021–Present), Saarland University & MPI-INF MSc in Visual Computing (2018–2021), Saarland University BSc in Electrical Engineering (2013–2017), Shiraz University Navid's research focuses on deep learning, generative models, and optimization techniques. His work includes mixed-integer optimization for neural networks, Bayesian design optimization, uncertainty quantification in AI systems, and applications in computational manufacturing and molecular design. He has contributed to top-tier venues like AAAI, NeurIPS (Spotlight), and SIGGRAPH. His publications address trends in AI-aided design optimization, uncertainty-aware modeling, sparsity in large language models, and inverse molecular design aligned with molecular dynamics. This work spans applications in manufacturing, computational chemistry, and natural language processing. Navid has collaborated with institutions such as Amazon AWS (LLM sparsification) and the Max Planck Institute for Brain Research (behavioral neuroscience). He is part of the Saarland Informatics Campus, a hub for visual computing and AI research.

• Machine Learning
• Physics-Informed Neural Networks
• Computational Fluid Dynamics
• +12 more

Dr. Michael Rom is a researcher at the Institute for Geometry and Practical Mathematics (IGPM), part of the Faculty of Mathematics, Computer Science, and Natural Sciences at RWTH Aachen University, Germany. He is actively engaged in applied mathematics and computational engineering, with a strong focus on fluid dynamics, tribology, and machine learning applications in industrial processes. Research Interests: Physics-Informed Neural Networks Transpiration Cooling and Porous Media Flow Hydrodynamic Lubrication and Cavitation Modeling Reduced Basis and Homogenization Methods Grid Generation and B-Spline Surfaces Grinding Wheel and Surface Modeling His recent publications (2021–2025) show a clear trend toward integrating deep learning and data-driven methods into classical engineering problems, particularly in manufacturing, aerospace thermal protection, and tribological systems. He frequently collaborates with experts in mechanical engineering and materials science, bridging disciplines to solve complex multiphysics problems. Scientific Awards: No awards mentioned in the provided text. Dr. Rom has supervised or co-supervised several students, as evidenced by co-authored works with individuals like M. Schultz, F. Brumand-Poor, and V. König. He has also contributed to teaching at RWTH Aachen, offering courses in numerical computation and finite element methods. His research is supported by collaborations with industrial and academic partners, particularly in digital production and thermal spray technologies. Laboratories and Research Teams: Institute for Geometry and Practical Mathematics (IGPM), RWTH Aachen Collaboration with the Chair of Production Metrology and Quality Management (F. Klocke) Part of interdisciplinary projects such as Internet of Production and Monetization of Technical Data