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Tristan BereauView profile

Researcher

• Molecular Simulations
• Coarse-Grained Modeling
• Machine Learning in Materials Science
• +3 more

Tristan Bereau is a Principal Investigator at the Institute for Theoretical Physics (ITP) of Heidelberg University. His research focuses on computational methods for understanding molecular systems, including coarse-grained modeling, machine learning-driven materials discovery, and membrane dynamics. He leads projects on drug-membrane interactions, peptide structures, and data-centric approaches in materials science. His work emphasizes reproducibility and software development, as seen in tools like Martignac for coarse-grained simulations. Key research themes include exploring chemical space through high-throughput screening, optimizing crystal structures with data-driven methods, and analyzing membrane asymmetries using advanced simulations. He advocates for FAIR (Findable, Accessible, Interoperable, Reusable) data principles to enhance scientific collaboration and transparency. His contributions span interdisciplinary areas, such as integrating machine learning with thermodynamic integration for free-energy calculations and designing antimicrobial peptides via computational screening. Collaborations bridge computational physics, chemistry, and biology, addressing challenges in drug delivery, materials design, and viral infectivity mechanisms.

Xiaofei LIView profile

Assistant Professor

• Speech Processing
• Sound Source Localization
• Speech Enhancement
• +9 more

Xiaofei LI is an Assistant Professor at Westlake University, Hangzhou, China, a position he has held since March 2020. Prior to this, he was a post-doctoral researcher and later a starting research scientist at INRIA Grenoble Rhône-Alpes, France, from February 2014 to at least 2019. He is affiliated with the PERCEPTION team and has contributed to major projects such as the ERC VHIA and EARS projects, as well as industry collaboration with Samsung Electronics. Education Bachelor of Electronic Information, Beijing Institute of Machinery, 2007 PhD in Electronics, Peking University, 2007–2013 His research focuses on speech and audio signal processing , particularly in challenging real-world environments. Key areas include multi-microphone speech processing for sound source localization, separation, and dereverberation; single-microphone signal processing for noise estimation, voice activity detection, and speech enhancement; and audio-visual fusion for speaker diarization and tracking. He employs advanced techniques such as LSTM networks , Bayesian inference , convolutive transfer functions , and deep learning in the STFT domain. His recent publications (2017–2019) demonstrate a strong trend toward multichannel speech enhancement , online speaker localization and tracking , and audio-visual integration . His work frequently appears in top-tier IEEE journals such as IEEE/ACM Transactions on Audio, Speech and Language Processing and IEEE Transactions on Pattern Analysis and Machine Intelligence. He has developed and evaluated algorithms using real datasets like LOCATA and AVDIAR, emphasizing robustness in reverberant and multi-speaker environments. Scientific Awards and Recognition No specific awards are mentioned in the provided text. Advising and Grants Currently serves as an Assistant Professor, implying student supervision and research advising, though no specific students are named. Research has been funded by prestigious grants including the European Research Council (ERC) Advanced Grant VHIA #340113 and the EU-FP7 STREP project EARS (#609465). Labs and Research Teams PERCEPTION team, INRIA Grenoble Rhône-Alpes, France Involved in the EARS, ERC VHIA, and Samsung Electronics Lito projects Contributor to the development of the AVDIAR and AVTrack-1 datasets

Enrique Ter HorstView profile

Associate Professor

• Quantitative Finance
• Bayesian Statistics
• Statistical Modeling
• +12 more

Enrique Ter Horst is an Associate Professor in the Faculty of Management at Universidad de los Andes, Colombia, affiliated with the Finance department. His research spans quantitative finance, Bayesian statistics, sentiment analysis, and interdisciplinary applications in marketing, psychology, and public policy. He teaches courses such as Derivatives Markets, Multivariate Models, and Computational Methods in Finance at both undergraduate and graduate levels. PhD in Bayesian Statistics, Duke University, 2003 Maitrise de Sciences Economiques, Université Louis Pasteur, 1999 Licence d'Économétrie, Université Louis Pasteur, 1998 Ter Horst's research focuses on developing advanced statistical and machine learning models, particularly using Bayesian frameworks and maximum entropy methods. His work applies to diverse domains including financial risk modeling, art market analysis, climate change news impact, and brand ethics. He has made significant contributions to understanding sentiment-driven portfolio hedging, brand commitment during crises, and topic modeling in academic publishing. His recent publications (2020–2025) reveal a strong trend in applying Bayesian and entropy-based methods to interdisciplinary problems, especially in finance, marketing, and social media analytics. He frequently employs computational techniques such as multinomial inverse regression and deep reinforcement learning to analyze large-scale textual and financial data. Financial Risk Manager (GARP) Secretary, Economics, Finance and Business Section, ISBA (2015–2016) Ter Horst advises doctoral students such as Manuel Andrés Martínez Patiño and has previously mentored Jovelyn Ferrer Garcia. His industry experience includes quantitative research roles at Credit Suisse First Boston and Morgan Stanley, where he co-edited the 'Quant Strategist' research series. He has consulted for the European Central Bank, Eurostat, and the Inter-American Development Bank. He is a member of the Finance and Financial Economics research group at Uniandes, which supports collaborative work in asset pricing, risk management, and behavioral finance.

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.

Karen Veroy-GreplView profile

Professor

• Numerical Methods for Partial Differential Equations
• Model Order Reduction
• Uncertainty Quantification
• +2 more

Karen Veroy-Grepl is a Full Professor of Computational Science at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science and the Institute for Complex Molecular Systems (ICMS). She leads research in numerical methods for PDEs, model order reduction, and uncertainty quantification. Her work focuses on multi-scale simulations and data-driven experimental design. Education: B.S., Ateneo de Manila University (Philippines) S.M. and Ph.D., Massachusetts Institute of Technology (USA) Professional Roles: Editorial Board Member: Journal of Computational Physics, SIAM Journals on Uncertainty Quantification and Scientific Computing Scientific Advisor, International Centre for Numerical Methods in Engineering (CIMNE) Chair, SIAM Activity Group on Uncertainty Quantification Her research bridges computational science and engineering applications, with recent work advancing reduced-order models for complex systems and Bayesian inverse problems. Notable contributions include ERC Consolidator Grant-funded research and collaborations in biomedical modeling for real-time treatment planning.

Jerome Bernard DarbonView profile

Associate Professor

• Hamilton-Jacobi Equations
• Imaging Sciences
• Optimal Control
• +7 more

Jerome Darbon is an Associate Professor of Applied Mathematics at Brown University, specializing in Hamilton-Jacobi equations, imaging sciences, optimal control, and optimization. His research emphasizes developing efficient algorithms for variational estimations, combinatorial optimization, and stochastic sampling, with applications in remote sensing, geomorphology, and medical imaging. He holds a Ph.D. from École Nationale Supérieure des Télécommunications (Paris, 2005) and has been recognized with awards including the CNRS Prime d’Excellence Scientifique (2010-2014) and the UCLA Chancellor’s Award for Postdoctoral Research (2009). His work bridges theoretical mathematics with practical computational methods, addressing high-dimensional problems through innovative approaches like neural network architectures and algorithm-architecture co-design. Research Interests: Hamilton-Jacobi PDEs and their applications in control theory Imaging sciences, including denoising and image restoration Optimization techniques for compressive sensing and inverse problems Algorithm development for parallel architectures Key Contributions: Developed methods to overcome the curse of dimensionality in Hamilton-Jacobi equations Advanced landscape evolution models using PDE-based simulations Contributed to SAR image regularization and InSAR phase reconstruction Awards and Grants: NSF Grant DMS 1820821 (2018-2021) for work on variational decomposition models ONR Grant N00014-11-1-0749 (2011-2014) for combinatorial scientific computing Teaching: Courses include Applied PDEs, Numerical Optimization, and Computational Linear Algebra.

Frédéric HermanView profile

Professor

• Climate Science
• Geomorphology
• Glaciology
• +3 more

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
• +4 more

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 ThuereyView profile

Professor

• Computer Graphics
• Fluid Dynamics
• Machine Learning
• +3 more

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 UllmannView profile

Associate Professor

• Uncertainty Quantification
• Scientific Computing
• Numerical Analysis
• +3 more

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 ZhanliView profile

Professor

• Computational Mechanics
• Material Failure Analysis
• Impact Engineering
• +3 more

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 DavisView profile

Associate Professor

• Structural Vibration
• Acoustics
• Bayesian Inference
• +5 more

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 EngelView profile

Researcher

• Bayesian Inverse Problems
• Machine Learning
• Optimization Methods
• +3 more

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 PrabhudesaiView profile

Researcher

• Artificial Intelligence
• Human-AI Decision-making
• Uncertainty Quantification
• +7 more

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 StuytsView profile

Visiting Professor

• Offshore Wind Energy
• Geotechnical Engineering
• Soil-Structure Interaction
• +3 more

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.