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Nicolas CourtyView profile

Professor

• Optimal transport
• Statistical learning
• Machine learning
• +7 more

Nicolas Courty is a Full Professor in Computer Science at University of Brittany South since 2018, where he leads the Obelix research team at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires). His research sits at the intersection of machine learning and Earth Observation, with a focus on optimal transport theory and its applications. Dr. Courty obtained his PhD in 2002 from INSA Rennes and his Habilitation à diriger des recherches in 2013, with early work focused on computer graphics and animation. Since 2014, he has developed expertise in optimal transport and its applications to machine learning, becoming a principal investigator in an ANR Chair program on AI (OTTOPIA) in 2020, focusing on applied optimal transport for Remote Sensing. Since 2024, he serves as co-director of Cluster SequoIA, a center of excellence in artificial intelligence research and training in Brittany. His research interests span optimal transport, statistical learning, kernel methods, manifold and geometric approaches to machine learning, with applications to computer graphics, vision, and remote sensing. He is particularly known for pioneering work at the intersection of non-Euclidean geometry and optimal transport, developing novel methods for dimensionality reduction, domain adaptation, and representation learning in complex data spaces. Analysis of his recent publications reveals a strong trend toward geometric machine learning, with increasing focus on non-Euclidean spaces (particularly hyperbolic geometry), sliced optimal transport methods, and applications to remote sensing and neuroscience. His work consistently bridges theoretical advances in optimal transport with practical applications in Earth observation, demonstrating a commitment to both theoretical rigor and real-world impact. Best paper prize at Eurographics 2024 Stanford/Elsevier's Top 2% Scientist Rankings Multiple papers accepted at top conferences including NeurIPS (with oral presentations), ECCV, BMVC, and Eurographics As principal investigator of the ANR Chair program OTTOPIA, Dr. Courty has secured significant research funding for applied optimal transport in Remote Sensing. His leadership extends to directing the Obelix research team at IRISA and co-directing Cluster SequoIA, demonstrating his growing influence in the AI research community in Brittany. He is actively involved in mentoring researchers and fostering collaborations across institutions. Dr. Courty leads the Obelix research team at IRISA, which focuses on the intersection of machine learning and Earth Observation. Under his direction, the team has developed innovative approaches to applying optimal transport theory to remote sensing data, contributing significantly to the field of geospatial AI. The team's work has practical applications in environmental monitoring, land use classification, and climate change analysis.

Alexander HeinleinView profile

Assistant Professor

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +6 more

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Machine Learning
• Data Science
• Mathematical Physics
• +3 more

Rostyslav Hryniv serves as Professor of Applied Mathematics and Statistics at Ukrainian Catholic University (UCU), holding dual leadership roles as Deputy Dean for Research and Head of the Machine Learning Laboratory. His academic profile uniquely bridges rigorous mathematical physics with cutting-edge artificial intelligence applications, reflecting UCU's interdisciplinary research vision. Hryniv's educational foundation includes dual PhDs in Mathematics (1996, 2007) and a higher doctorate (dr hab.) from the Institute of Mathematics of the Polish Academy of Sciences (2009). His postdoctoral trajectory features prestigious appointments as a Humboldt Research Fellow at the University of Bonn (2003-2005) and PIMS Postdoctoral Fellow at the University of Calgary (1998-1999), complemented by teaching roles across institutions in Ukraine, Canada, Poland, and the United States. His research demonstrates a strategic evolution from foundational work in spectral theory and inverse problems to contemporary machine learning. Early career contributions focused on Schrödinger operators with singular potentials, trace formulas, and quantum scattering phenomena. Recent publications (2021-2025) reveal a decisive pivot toward applied AI, including Ukrainian NLP benchmarks, symmetry-based 3D reconstruction, and minimal solvers for computer vision. This transition exemplifies his commitment to leveraging deep mathematical insights for real-world technological challenges. Analysis of his publication trends indicates a clear interdisciplinary trajectory: while maintaining mathematical rigor, his work increasingly addresses Ukrainian language technology and geometric deep learning. The Machine Learning Laboratory under his direction has become a regional hub for AI innovation, particularly in developing resources for low-resource languages and symmetry-aware computer vision systems. Hryniv's scholarly recognition includes: Humboldt Research Fellowship (University of Bonn, 2003-2005) PIMS Postdoctoral Fellowship (University of Calgary, 1998-1999) As Head of UCU's Machine Learning Laboratory, Hryniv leads research initiatives spanning Ukrainian NLP, 3D vision, and quantum machine learning. The lab's recent focus on symmetry-based algorithms and Ukrainian language resources positions it at the forefront of culturally contextual AI development in Eastern Europe, while maintaining connections to his foundational work in mathematical physics through quantum-inspired computing approaches.

Luiz ChamonView profile

Assistant Professor

• Machine Learning
• Reinforcement Learning
• Constrained Optimization
• +3 more

Luiz Chamon is an Assistant Professor (tenure-track) and Hi!PARIS Chair holder at the Center for Applied Mathematics (CMAP) , École polytechnique, Paris, France. His research bridges machine learning with control theory and signal processing, focusing on intelligent systems that extract, process, and act on information under constraints. Research Interests: He specializes in constrained learning frameworks for machine learning, with applications to reinforcement learning, statistical signal processing, and optimization. His work includes adversarial robustness, safe policy design, and duality analysis in constrained settings. Article Trends: Recent publications span differential equation solving (ICLR 2025), primal-dual sampling (NeurIPS 2024), and resilient reinforcement learning (IEEE Trans. Autom. Control 2024). Common themes include constrained optimization, robustness, and bridging theory with practical applications in wireless systems and graph signal processing. Scientific Awards: Hi!PARIS Chair (2025) ELLIS Scholar (2025) Best Student Paper Award (ICASSP 2020) Best Paper Award (ICASSP 2020) Top 50 Most Accessed Articles in IEEE TSP (2019) Grants & Projects: Currently leads a 4-year ANR JCJC project on "Beyond (Robust) Learning: Striking Compromises in Machine Learning" (2024). Previously involved in technical reports on aircraft vibroacoustics and wireless power systems. Labs & Teams: Heads a research group at École polytechnique developing tools for constrained learning, collaborating with institutions like University of Pennsylvania and IMPRS-IS. Organizes tutorials and panels on constrained learning and career development.

Markos DiomatarisView profile

Research Fellow

• computer vision
• machine learning
• neural networks
• +2 more

Markos Diomataris is a doctoral fellow at the Max Planck ETH Center for Learning Systems (CLS) , a collaborative program between ETH Zurich and the Max Planck Institute for Intelligent Systems . He is supervised by Professors Otmar Hilliges, Michael Black, and Bernt Schiele. His research bridges the gap between geometric perception and semantic understanding in human-machine interaction, focusing on enabling machines to infer goals, actions, and intentions from human behavior. Key disciplines include computer vision, machine learning, neural networks, human-object interactions, and intuitive physics .

John H. MaddocksView profile

Professor

• Nonlinear Mechanics
• Computational Analysis
• Biophysics
• +6 more

John H. Maddocks is an Honorary Professor at EPFL's School of Basic Sciences (SB), Department of Mathematics. His research focuses on nonlinear mechanics, computational analysis, and the structural dynamics of DNA. He holds a BSc from the University of Glasgow (1978) and a PhD from the University of Oxford (1981). Prior to his honorary role, he served as Full Professor of Applied Analysis at EPFL (since 1997) and previously held positions at the University of Maryland (Full Professor, 1993–1997) and Stanford University. His work spans interdisciplinary fields including robotics, satellite dynamics, and biophysical systems. Maddocks has developed computational tools like the cgDNA+ model for DNA structural analysis and pioneered multiscale simulations of biomolecules. He has advised 12 PhD students at EPFL and contributed to over 100 publications, emphasizing sequence-dependent DNA mechanics and elastic systems. Education: BSc in Mathematics, University of Glasgow (1978) PhD in Mathematics, University of Oxford (1981) Research Interests: Nonlinear mechanics and stability theory Computational modeling of DNA and biomolecules Hamiltonian systems and bifurcation phenomena Scientific computing and visualization tools Award Highlights: Extensive contributions to DNA biophysics and computational mechanics, though no specific named awards are cited. Labs/Teams: Active within EPFL's Laboratory of Computational Vision and Medical Imaging (LCVM). His recent work explores nucleosome energetics, knot mechanics in elastic systems, and multiscale DNA simulations. Collaborations include projects on continuum robotics and single-cell protein dynamics.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +3 more

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +3 more

Timon Gehr is part of the Professorship for Computer Science at ETH Zurich's Department of Computer Science, affiliated with the Institute of Programming Languages and Systems. His research focuses on quantum computing, probabilistic programming, neural network robustness, and privacy enforcement. He has contributed to the development of quantum languages like Silq and frameworks for certifying adversarial robustness in machine learning systems. His research interests include formal methods for programming languages, scalable symbolic reasoning, and applying probabilistic techniques to security and privacy. Recent work emphasizes robustness certification of neural networks and symbolic integration in machine learning. Key projects involve exact inference for probabilistic programs and differential privacy violation detection. Notable publications include work on quantum uncomputation, adversarial examples, and integrating logic into neural networks. No specific advising or grant details are provided in the text, but his involvement with the Institute of Programming Languages and Systems indicates active participation in academic collaborations and research teams.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +3 more

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

• Pattern Recognition
• Applied Machine Learning
• Document Analysis
• +4 more

Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework

Kai HormannView profile

Professor

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +3 more

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

Tomislav MedićView profile

Lecturer

• Geomatics
• Remote Sensing
• Laser Scanning
• +6 more

Dr. Tomislav Medić is a Lecturer and PostDoc at the Department of Civil, Environmental and Geomatic Engineering, ETH Zurich. His research focuses on advanced geospatial technologies, particularly terrestrial laser scanning (TLS) applications in deformation monitoring, sensor calibration, and precision agriculture. MSc in Geodesy and Geoinformation, University of Zagreb, Croatia PhD in Geodesy, Bonn University (IGG), Germany Dr. Medić’s research spans geomatics, remote sensing, and sensor engineering. Key areas include TLS radiometric calibration, point cloud processing for 3D displacement analysis, and multispectral LiDAR applications in agriculture. He contributes to improving geodetic measurement accuracy through innovative calibration strategies. His recent publications emphasize TLS integration with RGB data for geomonitoring (2025), hyperspectral scanning for fruit quality assessment (2024), and calibration field design for panoramic scanners (2023). Articles demonstrate expertise in error modeling, multi-sensor fusion, and environmental monitoring applications. At ETH Zurich’s Geosensors and Engineering Geodesy (GSEG) group, he leads the Alpine Measurement Lab collaboration project. He also participates in PhenoRob, a cluster of excellence in robotics and phenotyping for sustainable crop production at Bonn University.