جست‌وجوی استاد و پژوهشگر

Frédéric Hermanمشاهده پروفایل

استاد

• Climate Science
• Geomorphology
• Glaciology
• +۳ مورد دیگر

Frédéric Herman is a full professor at the Institute of Earth Surface Dynamics , part of the Faculty of Geosciences and Environment at the University of Lausanne . He holds a PhD in Earth Sciences from the Australian National University and has held academic positions at ETH Zurich and the University of Lausanne since 2007. His research focuses on the interplay between climate, tectonics, and Earth surface processes, with emphasis on glacial erosion, sediment transport, and low-temperature thermochronology. Joined University of Lausanne in 2012 as a scholarship professor Promoted to associate professor in 2013 and full professor in 2019 Key affiliations: Institute of Earth Surface Dynamics (IDYST) His work integrates field observations, numerical modeling, and geochronological techniques to study mountain erosion, glacial dynamics, and tectonic-climate interactions. Recent publications highlight studies on Himalayan tectonics, subglacial sediment transport, and the impact of glaciers on landscape evolution. He has advised multiple PhD students and contributes to advancing methodologies in luminescence dating and inverse modeling.

• Virtual Screening
• Cytochrome P450
• Small Molecule
• +۴ مورد دیگر

Johannes Kirchmair is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods in drug discovery, particularly virtual screening, machine learning applications, and understanding drug metabolism. He is actively involved in projects such as the Anti-Infectives Drug Discovery initiative and the AIDD: Advanced Machine Learning for Innovative Drug Discovery program. His work addresses challenges in toxicity prediction, enzyme inhibition, and assay interference detection. Key areas include cytochrome P450 metabolism, small molecule drug design, and natural product analysis. Research interests span virtual screening methodologies, metabolite identification, and computational toxicology. He has contributed to the development of tools like E-GuARD for detecting assay interference and aweSOM for predicting metabolic sites. His collaborations emphasize cross-disciplinary approaches to advance drug discovery and chemical safety. He has delivered talks on computational methods for early drug discovery and assay interference prediction, reflecting his commitment to both academic research and practical applications. Current projects aim to leverage machine learning to tackle antimicrobial resistance and enhance drug development efficiency.

Nils Thuereyمشاهده پروفایل

استاد

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +۳ مورد دیگر

Prof. Nils Thuerey is a Professor of Physically Based Simulation at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. He specializes in computer graphics, fluid dynamics, and machine learning applications in physics-based simulations. His work focuses on developing algorithms for fluid simulations, turbulence modeling, and differentiable physics solvers. He earned his doctorate in fluid simulations from the University of Erlangen-Nuremberg (2006) and held positions at ETH Zurich, ScanlineVFX, before joining TUM in 2013. Research Interests: Fluid Dynamics and Turbulence Modeling Machine Learning for Physics Simulations Deep Learning in Computer Graphics Differentiable Simulations and Inverse Problems Key Awards: Technical Oscar (2012) for contributions to fluid effects in films/games ERC Starting Grant (2014) for 'realFlow' research University of Erlangen Staedtler Graduation Award (2008) His recent work emphasizes integrating deep learning with physical models, such as differentiable solvers for fluid dynamics and probabilistic methods for weather forecasting. He leads projects like WeatherBench and develops tools like φ-ML and phiflow for scientific computing.

Elisabeth Ullmannمشاهده پروفایل

دانشیار

• Uncertainty Quantification
• Scientific Computing
• Numerical Analysis
• +۳ مورد دیگر

Prof. Elisabeth Ullmann is an Associate Professor for Scientific Computing and Uncertainty Quantification at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information, and Technology and the Department of Mathematics. She holds a PhD from TU Bergakademie Freiberg (2008) and has held academic roles including postdoctoral positions at the University of Bath, University of Hamburg, and University of Maryland. Her research focuses on developing efficient algorithms for uncertainty quantification in partial differential equations with random coefficients, Bayesian inverse problems, and rare event simulation. She is an Associate Editor for the SIAM Journal on Scientific Computing and SIAM/ASA Journal on Uncertainty Quantification , and teaches courses on numerical methods for uncertainty quantification and partial differential equations. Her work emphasizes multilevel Monte Carlo methods, stochastic Galerkin discretizations, and probabilistic numerical methods. Notable contributions include error analysis for rare event probabilities and multilevel estimators for high-dimensional problems. She collaborates internationally and maintains an active research group in Scientific Computing & Uncertainty Quantification at TUM.

Liu Zhanliمشاهده پروفایل

استاد

• Computational Mechanics
• Material Failure Analysis
• Impact Engineering
• +۳ مورد دیگر

Liu Zhanli is a Full Professor at the Department of Engineering Mechanics, School of Aerospace, Tsinghua University. He holds dual B.S. and Ph.D. degrees in Engineering Mechanics from Tsinghua University (2004 and 2009 respectively), followed by postdoctoral research at Northwestern University, USA (2009-2012). His research focuses on computational mechanics methods for understanding material and structural deformation/failure, particularly in aerospace and biomedical engineering contexts. Key areas include impact loading dynamics, machine learning-driven reverse engineering, lightweight protective structures, and biological tissue mechanics. Professional roles include Regional Editor for the International Journal of Fracture , Editorial Board member of Applied Mathematics and Mechanics , and leadership positions in multiple Chinese mechanical engineering societies. Notable awards include the 2020 Aeronautical Society Science Prize and the National Science Fund for Excellent Young Scholars (2017). Major research initiatives span: Dynamic response modeling under impact loads Machine learning-based inverse design methodologies Biomedical tissue damage and repair Ballistic-resistant composite material design His work bridges computational theory with practical applications in defense, aerospace, and medical engineering domains.

R. Benjamin Davisمشاهده پروفایل

دانشیار

• Structural Vibration
• Acoustics
• Bayesian Inference
• +۵ مورد دیگر

R. Benjamin Davis, Ph.D. is an Associate Professor at the University of Georgia, affiliated with the School of Electrical & Computer Engineering. His research focuses on structural vibration and acoustics, with a strong emphasis on Bayesian methods, nonlinear dynamics, and experimental modal analysis. He has contributed to advancements in vibration isolation, energy harvesting, and structural health monitoring through innovative methodologies like system-equivalent singular vector basis and dynamic substructuring techniques. His academic career includes expertise in pavements backcalculation using Markov chain Monte Carlo methods and the development of open-source tools for frequency-based substructuring. Davis has also explored interdisciplinary applications, such as impact mechanics in agriculture (e.g., pecan cracking) and hydroelastic damping of marine structures. His work integrates computational modeling with experimental validation, exemplified by studies on piezoelectric actuators for modal testing and generative adversarial networks for vibration data generation. Notable contributions include optimizing sensor placement for structural load determination and validating impulse response filters for impact force reconstruction. His research spans academic partnerships with industry, addressing practical challenges like vibration mitigation in bladed systems and reducing cavity response in coupled acoustic-structure systems. Davis's publications reflect a balance between theoretical innovation and real-world engineering solutions.

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.

Snehal Prabhudesaiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Human-AI Decision-making
• Uncertainty Quantification
• +۷ مورد دیگر

Snehal Prabhudesai is a Researcher at the Department of Computer Science and Engineering, College of Engineering, University of Michigan, Ann Arbor. Her work focuses on leveraging AI uncertainty as a generative resource to support Human-AI decision-making through computational algorithms, frameworks design, and empirical studies on uncertainty in human-centered contexts. Research Themes: Artificial Intelligence, Human-AI Decision-making, Uncertainty Quantification, Machine Learning, Bayesian Inference, Medical Imaging AI, Science Communication, and Disaster Risk Management. Collaborations: Mentored by Nikola Banovic (Computer Science and Engineering), Xun Huan (Mechanical Engineering), and Arvind Rao (Michigan Medicine), with global partnerships spanning Microsoft Research NYC, Kyushu Institute of Technology Japan, Australian National University, University of Toronto, and University College London. Grants & Recognition: Her dissertation work secured a $7.5 million Office of Naval Research grant for disaster risk management. Recognized by the CSE Department for excellence in climate and outreach. Her research has been published in venues like ACM CHI, IUI, Nature Communications, and Frontiers in Computer Science. She is actively involved in open-source projects such as the Uncertainty Quantification 360 Toolkit (UQ360) and will be on the 2025-2026 job market for academic and industry roles.

Bruno Stuytsمشاهده پروفایل

استاد مهمان

• Offshore Wind Energy
• Geotechnical Engineering
• Soil-Structure Interaction
• +۳ مورد دیگر

Bruno Stuyts is a Visiting Professor at Ghent University and a postdoctoral researcher in the Applied Mechanics Acoustics & Vibration Research Group. His research focuses on offshore wind energy, geotechnical engineering, and soil-structure interaction, with emphasis on data-driven modeling and in-situ monitoring. He leads projects like WINDSOIL (2020–2024) and SOIL-TWIN (2020–2023), addressing soil-structure interaction and smart monitoring of offshore foundations. His work integrates geophysics, computational mechanics, and artificial intelligence to improve offshore wind foundation design and geohazard assessment. Key research interests include seabed response under wave loading, eigenfrequency analysis of wind turbines, and seismic inversion for subsurface characterization. He collaborates globally on projects involving pore pressure monitoring, data fusion, and real-time modeling of monopile foundations. His contributions span experimental studies (e.g., 3D seabed response experiments) and numerical simulations (e.g., finite element modeling of monitored monopiles). Stuyts has contributed to over 30 peer-reviewed articles, covering topics like CPT-based correlations for shear modulus, scour protection effects on turbine stiffness, and Bayesian estimation techniques. His work bridges academic research and industry applications, aiming to optimize offshore infrastructure reliability through advanced data science and geotechnical innovations. He actively participates in conferences, presenting on data science applications, seismic inversion, and offshore wind challenges. Collaborations include institutions like Zenodo (dataset sharing) and ASME. Current efforts focus on integrating geohazard mapping, AI-driven geotechnical analysis, and open-source modeling frameworks for sustainable offshore energy systems.

Ankush Aggarwalمشاهده پروفایل

مدرس ارشد

• Computational Biomechanics
• Cardiovascular Healthcare
• Soft Tissue Mechanics
• +۳ مورد دیگر

Dr. Ankush Aggarwal is a Senior Lecturer in Engineering at the University of Glasgow, affiliated with the Glasgow Computational Engineering Centre (GCEC). His research focuses on computational biomechanics applied to cardiovascular healthcare, particularly in understanding soft tissue mechanics, heart valve dynamics, and medical image analysis. He holds a PhD in Mechanical Engineering from UCLA and has held postdoctoral positions at the University of Texas and Swansea University. Education: Bachelor's in Aerospace Engineering (Indian Institute of Technology, Kharagpur) PhD in Mechanical Engineering (University of California, Los Angeles) Research Interests: Dr. Aggarwal's work integrates advanced computational methods with clinical data to address cardiovascular challenges. His projects span four key areas: Computational modeling of heart valve mechanics Image-based strain estimation using 4D echocardiography Stochastic finite element analysis for soft tissues Multi-scale vascular modeling linking molecular to organ-level behavior Recent research trends show focus on translational applications like drug-coated balloon modeling and open-source tools for cardiac image workflows. His team develops Python packages (e.g., pyMechT) to democratize biomechanical simulations. Grants and Collaborations: EPSRC Centre for Future PCI Planning (2024-2027) Chan Zuckerberg Initiative Essential Software Award (2020-2021) National Research Network Fellowship (Swansea University, 2015-2018) Labs/Teams: Active contributor to GCEC, leading interdisciplinary projects combining biomechanics, imaging, and clinical translation. Mentor for 20+ graduate students across PhD, masters, and undergraduate levels.

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'.

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +۲ مورد دیگر

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis

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).

• Mathematics
• Statistics and probability
• Geometric Analysis
• +۳ مورد دیگر

Eero Saksman is a Professor at the Department of Mathematics and Statistics , University of Helsinki . He is affiliated with the Faculty of Science and serves as a supervisor for the Doctoral Programme in Mathematics and Statistics . His research focuses on Mathematics , Statistics and probability , with specific expertise in Geometric Analysis , Partial Differential Equations , and Gaussian Multiplicative Chaos . Research Outputs : Active in 2025 with studies on stochastic pressure equations with log-correlated Gaussian coefficients, quasiconformal mappings in Triebel-Lizorkin spaces, Nevanlinna measures structure, and interdisciplinary projects like the Centre of Excellence in Randomness and Structures (FiRST) . Academic Collaborations : Involved in international collaborations with institutions such as Kings College London , EPFL , and University of Geneva . Scientific Awards : Recipient of the Lorenz Lindelöf Prize (2013) Magnus Ehrnrooth Prize (2018) Nevanlinna Prize for best dissertation (1995) Väisälä Prize for Mathematics (2007) Academic Activities : Regularly participates in conferences like the Barcelona Analysis Conference , Modern Aspects of Complex Analysis , and serves on doctoral evaluation committees.

• Bayesian Statistics
• Stochastic Differential Equations
• Mathematical Statistics
• +۴ مورد دیگر

Shota Gugushvili is a Lecturer in the Mathematical and Statistical Methods - Biometris group at Wageningen University & Research. His academic work focuses on advanced statistical methodologies with applications across various scientific domains. Dr. Gugushvili's research spans several key areas in modern statistics and mathematical modeling. His primary expertise lies in Bayesian statistics, where he develops novel approaches for complex statistical problems. He has made significant contributions to the theory and application of stochastic differential equations, particularly in financial mathematics contexts. His work on inverse problems and regularization techniques has provided new insights into statistical estimation methodologies. Dr. Gugushvili also applies his statistical expertise to biological and agricultural sciences, developing frameworks for modeling complex genotype-environment interactions. His recent publications demonstrate a strong trend toward developing rigorous statistical frameworks for complex mathematical problems, with particular emphasis on Bayesian approaches. His work bridges theoretical statistics with practical applications in finance, biology, and agricultural science, showing remarkable versatility across disciplines while maintaining mathematical rigor. Dr. Gugushvili actively collaborates with researchers across multiple institutions and disciplines, as evidenced by his diverse publication record. His fingerprint analysis shows strong connections to Bayesian methodology (100%), stochastic differential equations (58%), posterior distribution theory (47%), and discrete time modeling (41%).