Find Academic Supervisors

• Civil Engineering
• Computational Fluid Dynamics
• Porous Media Flow
• +3 more

Dr. Luka Malenica is a dedicated researcher at ETH Zurich's Professorship for Durability of Engineering Materials, focusing on computational modeling of material degradation processes in civil infrastructure. His work bridges theoretical numerical methods with practical engineering applications through direct involvement in the department's research operations. His research program centers on multiphase flow in porous media , corrosion mechanisms in reinforced concrete , and advanced numerical techniques . He employs pore-scale direct numerical simulations to investigate steel-concrete interface phenomena, developing predictive models for infrastructure durability. His methodological innovations include control volume isogeometric analysis, adaptive multiresolution modeling, and deep learning applications for adaptive meshing—addressing challenges in heterogeneous media flow and transport phenomena. Analysis of his 15 most recent publications reveals a consistent research trajectory since 2015, with accelerating output in corrosion science (5 papers in 2024-2025) and computational methods (7 papers 2019-2022). Key interdisciplinary connections span civil engineering (corrosion in reinforced concrete), chemical engineering (bubble column reactors), and hydrology (karst aquifer modeling), unified by his expertise in numerical simulation of transport phenomena. Recent work demonstrates increasing sophistication in modeling macrovoid formation at steel-concrete interfaces and capillary-driven multiphase systems.

• Multiresolution Methods
• Numerical Analysis
• Computational Mathematics
• +4 more

Michael Multerer is an Associate Professor at the Faculty of Informatics, Università della Svizzera italiana (USI). His research focuses on multiresolution methods, scattered data analysis, and numerical analysis with applications in computational mathematics and engineering. He leads projects such as the SNSF Starting Grant on multiresolution methods for unstructured data, emphasizing nonlinear approximation and kernel-based techniques. Research Interests: Development of fully discrete multiresolution methods for unstructured data Wavelet theory and kernel matrix algebra Uncertainty quantification in partial differential equations Scattered data compression and approximation Key Software Contributions: FMCA: Fast multiresolution covariance analysis for scattered data Bembel: Boundary element library for solving Laplace and Helmholtz equations SPQR: Anisotropic sparse grid quadrature in MATLAB Funding: Holder of the SNSF Starting Grant (2025) for advancing multiresolution techniques in unstructured data processing. Labs/Teams: Active in the research group at USI’s Faculty of Informatics, collaborating with institutions like TU Darmstadt and University of Basel on numerical methods and engineering applications.

• Image Processing
• Medical Imaging
• Biological Imaging
• +3 more

Michaël Unser is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Engineering , leading the Biomedical Imaging Laboratory . He serves as Academic Director for Imaging at EPFL and contributes to cross-departmental teaching in Microengineering , Mathematics , and Life Sciences Engineering . His research spans Image Processing , Medical Imaging , Wavelets , and Spline-based Modeling , with a focus on multiresolution analysis and single-molecule localization microscopy . He has mentored over 30 PhD students and supervised numerous research projects. Recent publications highlight advancements in super-resolution microscopy , deep learning integration , and inverse problem solving for biomedical imaging. His work emphasizes mathematical rigor and open-source software development for accessible bioimaging tools. IEEE Technical Achievement Award (2008) IEEE EMBS Career Achievement Award (2020) Three ERC Advanced Grants (FUNSP, GlobalBioIm, FunLearn) As Academic Director for Imaging , he leads EPFL's cross-disciplinary imaging initiatives. His teaching includes Fundamentals of Image Analysis and Signals and Systems courses.

Dimitri Van De VilleView profile

Professor

• Signal Processing
• Image Processing
• Functional Neuroimaging
• +9 more

Dimitri Van De Ville is a Full Professor of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva (UniGE), affiliated with the School of Engineering at EPFL and the Faculty of Medicine at UniGE. He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech in Geneva and is a key figure at the CIBM Center for Biomedical Imaging. His work bridges signal processing, computational neuroscience, and clinical neuroimaging. Education: M.S. and Ph.D. in Computer Science, Ghent University, Belgium (1998, 2002) Post-doctoral Fellow, Biomedical Imaging Group, EPFL (2002–2005) His research focuses on advancing non-invasive brain imaging through methodological innovations in signal and image processing. He investigates the dynamic and network aspects of brain function using fMRI and EEG, with a special emphasis on dynamic functional connectivity, graph signal processing, and real-time neurofeedback. His work has demonstrated that EEG microstate sequences exhibit scale-free dynamics, linking fast electrophysiological events to slow hemodynamic changes. He pioneered connectivity decoding and contributed to the development of sparsity-based deconvolution methods for fMRI. The recent articles reflect a strong trend toward modeling brain function as a dynamic network process. Key themes include graph signal processing on brain connectomes, decomposition of transient brain activity, and the use of machine learning to decode brain states. His work increasingly integrates structural and functional data to understand brain organization at multiple scales. Scientific Awards: Technical Achievement Award, IEEE EMBS (2024) Fellow, EURASIP (2023) Distinguished Lecturer, IEEE Signal Processing Society (2021–2022) Fellow, IEEE (2020) Leenaards Award (2016) NARSAD Independent Investigator Award (2014) NeuroImage Editors' Choice Award (2013) Pfizer Research Award (2012) Van De Ville has secured substantial research funding through grants such as the SNSF Professorship and has advised numerous researchers. He plays a major role in the scientific community as founding chair of the EURASIP BISA SAT and former chair of the IEEE BISP TC. He has held editorial roles in top journals including IEEE Transactions on Signal Processing , SIAM Journal on Imaging Sciences , and Imaging Neuroscience . He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech, which specializes in developing advanced signal processing tools for neuroimaging. The lab is part of a broader collaborative ecosystem involving EPFL, UniGE, and the CIBM, fostering interdisciplinary research in biomedical imaging and brain science.

Sereina RinikerView profile

Professor

• Computational Chemistry
• Molecular Dynamics Simulations
• Cheminformatics
• +3 more

Sereina Riniker is a Full Professor of Computational Chemistry at the Department of Chemistry and Applied Biosciences , ETH Zurich. Her career at ETH Zurich began in June 2014 as Assistant Professor (tenure track), followed by promotions to Associate Professor (April 2020) and Full Professor (April 2025). The group focuses on developing advanced computational methods combining molecular dynamics simulations with cheminformatics techniques . Born in Switzerland (1985) MSc in Chemistry (ETH Zurich, 2008) PhD in Molecular Dynamics (ETH Zurich, 2013) Research interests include: QM/MM hybrid simulations Machine learning for molecular modeling Free energy calculations Implicit solvent models Conformational analysis Drug discovery applications Recent publications show strong trends in combining quantum mechanical accuracy with machine learning efficiency , including novel approaches like neural network potentials and multiresolution modeling . The group has received multiple grants from the Swiss National Science Foundation (SNF) for their work on free energy calculations and molecular modeling . Awards and honors include: 2024 Hansch Award 2018 ETH Latsis Prize 2017 OpenEye Outstanding Junior Faculty Award 2017 Silver Jubilee Award 2015 Ewald Wicke Prize The Computational Chemistry group maintains strong industrial collaborations, particularly with Novartis and Givaudan AG. Their research combines theoretical method development with practical applications in biological and chemical systems.

Sergey LitvinovView profile

Researcher

• Computational Mechanics
• Biomedical Engineering
• Industrial Applications
• +6 more

Sergey Litvinov is a Researcher at ETH Zürich, affiliated with the Department of Structural Mechanics and Monitoring within the College of Civil, Environmental and Geomatic Engineering. His work focuses on computational and statistical models for biomedical and industrial applications, leveraging tools like C++, PyTorch, and cloud computing. He contributes to hierarchical Bayesian analysis on big data and probabilistic programming, emphasizing data-driven methodologies. Current projects include the DCoMEX initiative (Data-Driven Computational Mechanics at Exascale), aiming to advance large-scale computational frameworks for structural mechanics and monitoring. His research interests span computational mechanics, fluid dynamics, and machine learning applications, with a strong emphasis on hybrid numerical methods and high-performance computing for multiphase systems. He has no listed scientific awards or advisees. His contributions are centered on software development (e.g., Aphros and Mirheo ) for multiphase flow simulations and novel optimization techniques for inverse problems, particularly in biomedical and engineering contexts. Recent articles highlight his work on discrete loss optimization, reinforcement learning for flow control, and data-driven methods for tumor growth modeling and drug delivery systems. He is part of interdisciplinary teams addressing challenges in microfluidics, turbulence modeling, and biomedical applications, with a focus on scalable solutions for complex systems. His projects often involve collaboration with industrial and academic partners on computational models for real-world applications.