جست‌وجوی استاد و پژوهشگر

Michael Engelمشاهده پروفایل

پژوهشگر

• Bayesian Inverse Problems
• Machine Learning
• Optimization Methods
• +۳ مورد دیگر

Michael Engel is a Researcher at the Chair of Remote Sensing Technology, Technical University of Munich since March 2021. He holds a Master's in Materials Science and Engineering (2020) and a Bachelor's in Engineering Science (2018), both from TU Munich, with a focus on Uncertainty Quantification and Bayesian Inverse Problems. His research interests revolve around optimizing objective functions in inverse problems, leveraging methods like Curriculum Learning and Multi-Task Systems via Multiresolution Analysis. He combines classical numerical techniques with machine learning to address challenges in Bayesian problems and regression. Education: Bachelor's in Engineering Science, TU Munich (2018) Master's in Materials Science and Engineering, TU Munich (2020) Research interests include: - Bayesian Inverse Problems - Machine Learning Integration in Numerical Methods - Optimization of Multi-Task Systems - Uncertainty Quantification in Environmental Models Key projects include the Global Earth Monitor (GEM) , aiming to enhance Copernicus data utilization for large-scale environmental monitoring. His publications span remote sensing optimization, wavelet-based sensitivity analysis, and deforestation assessment using multi-sensor data. He contributes to the TU Munich's Engineering Risk Analysis Group and collaborates with the Chair of Numerical Mathematics and Hydrology. His work bridges physical modeling principles with computational efficiency in earth observation systems.

Martin Kolbمشاهده پروفایل

استاد

• Stochastic Processes
• Probability Theory
• Mathematical Physics
• +۳ مورد دیگر

Roles & Affiliations: Martin Kolb is a Professor (W3) at Paderborn University's Institute of Mathematics (Applied Mathematics and Stochastics group leader). He previously held roles including Professor (W2) at Paderborn (2014–2017), Lecturer at University of Reading (2012–2014), and Harrison Early Career Professor at Warwick University (2011–2012). Education: PhD in Mathematics from TU Kaiserslautern (2009), Diploma in Mathematics from Ludwig-Maximilians-University Munich (2005). Studied at University of Copenhagen (2002–2003). Research Interests: Focuses on stochastic processes, probability theory, and mathematical physics. Key topics include quasi-stationary distributions, Fleming-Viot models, spectral asymptotics, and applications in quantum computing (e.g., Photonic Quantum Computing project). His work bridges theoretical mathematics with computational methods and physical systems. Scientific Engagement: Organized major events like the Weierstrass-Lecture series (since 2019) featuring Fields medalists and co-organized workshops such as the Oberwolfach Workshop on Stochastic Processes (2020). Active in conference committees for topics like 'Stochastic Processes under Constraints' (2016). Labs/Teams: Leads research projects including 'PhoQC: Photonic Quantum Computing' and 'Super-Brownian Motion with single point source'.

Dirk Blömkerمشاهده پروفایل

استاد

• Stochastic partial differential equations
• Surface growth
• Cahn-Hilliard equation
• +۱۱ مورد دیگر

Dirk Blömker is a Professor in the Institute of Mathematics at the University of Augsburg (since 2006). His research focuses on Stochastic Partial Differential Equations (SPDEs) with applications to surface growth models , phase separation (Cahn-Hilliard, Allen-Cahn), stochastic bifurcation , and multiscale analysis (amplitude/modulation equations, fast diffusion). He also works on filter theory (Ensemble Kalman) and numerics of SPDEs . Habilitation : RWTH Aachen (2006) Doctorate : University of Augsburg (2000) Diploma : University of Münster (1997) Intermediate Diploma : University of Münster (1993) Abitur : Gymnasium Lengerich (1989) His work spans rigorous mathematical analysis of SPDEs (existence/uniqueness, blow-up, regularity) and numerical methods (convergence analysis, a-posteriori bounds). Recent articles examine fractional noise, Lévy processes, and ensemble Kalman inversion. Collaborators include Luigi Bianchi, Claudia Schillings, and Philipp Wacker. He has held academic positions at RWTH Aachen (Assistant/Acting Lecturer), University of Bonn (Acting Lecturer), and University of Warwick (DFG Research Fellowship).

Maximilian Dinkelمشاهده پروفایل

پژوهشگر ارشد

• Bayesian Methods
• Inverse Problems
• Gaussian Processes
• +۱ مورد دیگر

Maximilian Dinkel serves as a Research Fellow at the Institute for Computational Mechanics, Technical University of Munich, where he has been a Research Associate since 2022 under Prof. Wolfgang A. Wall's Chair of Numerical Mechanics. His work bridges computational engineering with advanced statistical learning methodologies. His academic credentials include: Master of Science in Mechanical Engineering (2022, TUM) Bachelor of Science in Management and Technology (2019, TUM) Bachelor of Science in Mechanical Engineering (2018, TUM) Dinkel specializes in Bayesian inverse problems with expensive computational models, developing constrained Gaussian process frameworks and active learning strategies. His research integrates physics-informed machine learning to enhance uncertainty quantification in engineering simulations, particularly addressing challenges in computational mechanics where traditional likelihood evaluations are prohibitively costly. This work enables more efficient parameter estimation and model calibration in complex systems. Analysis of his 2023-2024 publications reveals a cohesive research trajectory focused on overcoming computational bottlenecks in Bayesian inference. Key themes include constrained optimization of Gaussian process surrogates, adaptive sampling techniques, and stochastic variational inference with dynamic learning rate adjustments. These contributions significantly advance the application of machine learning to large-scale engineering problems requiring high-fidelity simulations. He actively contributes to academic mentoring, having co-supervised D. Still's 2023 Master's thesis on adjoint-based Bayesian inference of random fields using Hamiltonian Monte Carlo methods. Dinkel also teaches Numerical Methods for Engineers exercises at TUM, supporting the department's educational mission in computational techniques for engineering applications. As a core member of TUM's Numerical Mechanics research group, he participates in developing next-generation simulation tools within the Institute for Computational Mechanics, collaborating on projects that merge traditional computational mechanics with cutting-edge machine learning approaches to solve previously intractable engineering challenges.

• Acoustics
• Machine Learning
• Metamaterials
• +۳ مورد دیگر

Jonas Schmid is a researcher affiliated with the Technical University of Munich (TUM) , specifically within the Chair of Acoustics of Mobile Systems . His work bridges computational methods with acoustic engineering, leveraging advanced statistical and machine learning techniques. Email: jonas.m.schmid@tum.de Office: Room 5505.01.534, Boltzmannstr. 15, 85748 Garching bei München Research Focus: Schmid's research centers on: Bayesian inference in acoustic modeling Generative design of acoustic metamaterials Sound field reconstruction techniques Interactive educational simulations for acoustics Integration of machine learning in noise control Visualization of musical instrument acoustics Publication Trends: Recent works demonstrate expertise at the intersection of computational acoustics and machine learning. Key areas include metamaterial design (2021, 2022), Bayesian inference applications (2021-2023), and development of interactive acoustic simulations (2019-2021). Subfields span noise control, room acoustics, and educational technology.

• Bayesian Inverse Problems
• Stochastic Optimization
• Ensemble Kalman Filtering
• +۳ مورد دیگر

Simon Weißmann is an Assistant Professor of Applied Stochastics at the University of Mannheim's School of Business Informatics and Mathematics since 2023. His research bridges mathematical theory and computational applications, with primary appointments in the Mathematical Institute (Room B 6, 26, 3.05). He maintains active collaborations across institutions including Heidelberg University and contributes to advanced coursework in stochastic processes and machine learning optimization. PhD in Mathematics (2020), University of Mannheim (supervised by Prof. Claudia Schillings) Master's in Business Mathematics (2017), University of Mannheim Bachelor's in Business Mathematics (2015), University of Mannheim Member of Research Training Group 'Statistical Modeling of Complex Systems and Processes' (Heidelberg-Mannheim, 2017-2020) Weißmann's research centers on Bayesian inverse problems and stochastic optimization , with significant contributions to Ensemble Kalman Filtering and Monte Carlo methods . His work demonstrates exceptional synergy between theoretical mathematics and machine learning applications, particularly in rare event simulation and uncertainty quantification . Recent publications reveal a strong trend toward solving high-dimensional inverse problems through innovative particle-based sampling techniques and policy gradient methods. Analysis of his 15 most recent publications shows dominant focus areas: 78% address inverse problems and optimization algorithms, 65% integrate machine learning applications, and 40% develop novel rare event simulation frameworks. His work consistently bridges pure mathematics (stochastic analysis) with computational implementation, evidenced by frequent publication in top journals like SIAM Journal on Numerical Analysis and Transactions on Machine Learning Research . Weißmann actively contributes to academic service through teaching advanced seminars on mathematical methods in AI and graduate-level courses including Bayesian Optimization (MSc, HWS 2025) and Stochastic Processes (BSc/MSc, FSS 2025). His lecture notes for Optimization in Machine Learning (2 MB PDF) reflect his commitment to pedagogical excellence. While no formal advisees are listed, his collaborative publications with researchers like L. Döring and J. Zech indicate active mentorship within research teams. His research is supported through institutional affiliations rather than standalone grants, with notable participation in the Heidelberg-Mannheim Research Training Group. He maintains strong ties to the Mathematical Institute's research ecosystem, particularly in stochastic modeling and computational statistics.

Lea Friedliمشاهده پروفایل

پژوهشگر

• Bayesian Methods
• Inverse Problems
• Value of Information
• +۷ مورد دیگر

Lea Friedli is a Postdoctoral Researcher at the Engineering Risk Analysis Group of the Technical University of Munich (TUM) since March 2025, funded by the Swiss National Science Foundation. She previously served as a Postdoctoral Researcher at the University of Bern's Institute of Mathematical Statistics and Actuarial Science in 2024. PhD in Environmental Sciences (2019–2023), University of Lausanne MSc in Statistics (2016–2019), University of Bern BA in German Language and Literature (2013–2016), University of Bern Her research focuses on Bayesian methods , inverse problems , and rare event probability estimation , with applications in environmental systems and risk analysis. Key methodologies include Gaussian process modeling, sequential design, and pseudo-marginal inference. Lea's publications demonstrate expertise in uncertainty quantification for geophysical and hydrological systems. Recent work includes energy-based rare event estimation (2023), groundwater inverse problems (2024), and lithological tomography using Bayesian frameworks (2022). Teaching: Estimation of rare events (TA, SS25, TUM); Combinatorics and Probability (Lecturer, HS24, University of Bern); Uncertainty quantification in action (Lecturer, HS24, University of Bern) She is affiliated with the TUM Engineering Risk Analysis Group, contributing to projects involving digital twins, wildfire risk, and probabilistic ground models for offshore wind farms. No scientific awards were mentioned in the provided text.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam , where he co-directs the AMLab with Max Welling. He holds an Assistant Professor position (on leave) at Northeastern University , continuing to advise students and collaborate remotely. His research focuses on combining probabilistic programming and deep learning to develop models that generalize from limited data. Key areas include inductive biases through physical simulators, causal structures , and symmetries , with applications in robotics , NLP , healthcare , and physical sciences . Recent work includes Variational Flow Matching for graph generation Equivariant neural models for physical systems Entropy coding of complex data structures Goal-contrastive reinforcement learning for robotics Awards NSF CAREER award (2021) Students & Postdocs Robin Walters (Postdoctoral Fellow) Ondrej Biza (Ph.D. Candidate) Babak Esmaeili (Ph.D. Candidate) Sam Stites (Ph.D. Candidate) Hao Wu (Ph.D. Candidate) Xiongyi Zhang (Ph.D. Candidate) Heiko Zimmermann (Ph.D. Candidate) Jered McInerney (Ph.D. Candidate) Eli Sennesh (Ph.D. Candidate)

• Biophysics
• Condensed Matter Physics
• Nanoscience
• +۳ مورد دیگر

Frank Schreiber is a Professor at the University of Tübingen, affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Physics. He leads the Biophysical Structural Research group at the Institute of Applied Physics (IAP). His office is located at Morgenstelle 10, Tübingen, and he can be contacted via email at frank.schreiber@uni-tuebingen.de. His research focuses on biophysics , condensed matter physics , and nanoscience , with emphasis on perovskite materials for optoelectronics, protein crystallization dynamics, and advanced X-ray scattering techniques. Specific interests include: Nanoscale assembly of perovskite superstructures Protein adsorption/crystallization mechanisms Machine learning for materials characterization In situ studies of soft matter dynamics Recent publications (2024-2025) predominantly explore perovskite photovoltaics (defect engineering, 2D/3D heterostructures), protein biophysics (crystallization kinetics, crowded environments), and synchrotron techniques (XPCS, GIWAXS with AI analysis). This reflects interdisciplinary work bridging materials science, biophysics, and data science. No scientific awards or student advisees are mentioned in available records.

Jan Bohrمشاهده پروفایل

پژوهشگر ارشد

• Inverse Problems
• Differential Geometry
• Partial Differential Equations
• +۲ مورد دیگر

Jan Bohr is a Research Fellow at the Mathematical Institute of the University of Bonn, affiliated with the research group 'Analysis and Partial Differential Equations' under the mentorship of Herbert Koch. Previously, he completed his PhD at the University of Cambridge under Gabriel Paternain's supervision and was associated with Richard Nickl's research group. Education includes studies at the University of Bonn, University of British Columbia, and MLU Halle-Wittenberg. Research focuses on inverse problems and their geometric/analytic foundations, utilizing tools from differential geometry, partial differential equations, microlocal analysis, and Bayesian non-parametric statistics. His work bridges theoretical mathematics with statistical methodologies to address complex challenges in geometric inverse problems. Publications emphasize geometric analysis and statistical inverse problems, with recurring themes in transport equations, twistor spaces, and Bayesian methodologies. Recent works explore rigidity properties in complex geometry (2024), high-dimensional statistical approximations (2024), and stability analysis of inverse transforms (2021–2023). No scientific awards are mentioned in available materials. Teaching includes graduate seminars and specialized courses on microlocal analysis, KAM theory, and geometric inverse problems (2023–2025). No PhD/Master's advisees or grants are documented. Bohr collaborates with the 'Analysis and Partial Differential Equations' research group at Bonn, focusing on analytical methods in PDEs and inverse problems.

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).

• Robot Vision
• Machine Learning
• Autonomous Systems
• +۹ مورد دیگر

Prof. Dr. Sebastian Houben is a Professor of Robot Vision and Machine Learning at Bonn-Rhein-Sieg University of Applied Sciences (H-BRS). He holds roles as International Affairs Representative and Head of the Master's Examination Board for Autonomous Systems. His research focuses on AI-driven solutions for autonomous systems, environmental monitoring, and energy efficiency. Key projects include: INNERVATE : Enhancing driver maneuver evaluation via AI for automotive safety. DKZ.2R : Promoting data literacy and high-performance computing across disciplines. DigitalTwin-4-Multiphysics Lab : Urban and industrial digital twins for efficiency optimization. GARRULUS : AI-driven reforestation using drones and precision seeding. He leads the Institute for AI and Autonomous Systems (A2S) and collaborates with industry partners like GKN Driveline. His research spans robotics, computer vision, and sustainable energy systems. Prof. Houben is a member of the Promotionskolleg Nordrhein-Westfalen (Doctoral College North Rhine-Westphalia). His work emphasizes explainable AI (XAI), safety-critical systems, and interdisciplinary applications.

• Geophysics
• Seismology
• Planetary Science
• +۳ مورد دیگر

Prof. Heiner Igel is a Professor in the Department of Earth and Environmental Sciences at Ludwig-Maximilians-Universität München. His research focuses on advanced seismic instrumentation, rotational ground motion analysis, planetary geophysics, and nonlinear seismic wave dynamics. He leads multidisciplinary projects such as the NEPOS initiative for planetary exploration and the ROMY ring laser observatory. Key research areas include: Development of 6-component (6C) seismic sensors for structural health monitoring Full-waveform inversion techniques for crustal and mantle imaging Applications of rotational sensing in earthquake engineering and lunar exploration Recent work addresses novel methods for: Nonlinear seismic rupture modeling with discontinuous Galerkin methods Planetary subsurface exploration using autonomous robotic swarms Quantifying seismic anisotropy through multicomponent observations His lab operates cutting-edge facilities including the ROMY ring laser and collaborates globally on projects like the SmartSolo seismic node development for geothermal and environmental research.

• Computer Vision
• Machine Learning
• Signal Processing
• +۶ مورد دیگر

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.