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Pier Luigi DragottiView profile

Professor

• Signal Processing
• Wavelet Theory
• Sampling Theory
• +7 more

Prof. Pier Luigi Dragotti is a Professor of Signal Processing in the Department of Electrical and Electronic Engineering at Imperial College London, Faculty of Engineering. He leads the Communications, Signal Processing, and Control (CSP) research group and is actively engaged in both theoretical and applied research in signal processing and computational imaging. His research focuses on wavelet theory, sampling theory, sparse signal processing, computational imaging, and data-driven image processing . He integrates classical signal processing frameworks with modern deep learning techniques, particularly in solving inverse problems and image restoration. His work bridges model-based algorithms with data-driven approaches, enabling breakthroughs in fields such as art conservation and neuroscience imaging. The recent publications highlight a strong trend towards invertible neural networks, physics-informed deep learning, and hybrid model-data methods for image restoration, microscopy, and cultural heritage analysis. His team develops algorithms that are not only accurate but also interpretable and grounded in physical models. Scientific Awards and Recognitions: Editor-in-Chief of IEEE Transactions on Signal Processing (2018–2020), Certificate of Merit Supervised students winning: Eurasip 3M Thesis Competition, IEEE MMSP Best Paper Award, Outstanding PhD Thesis Awards (2020–2022), Ivor Tupper Prize Student Adam won Gold at the Commonwealth Games Advising and Grants: Prof. Dragotti has successfully supervised numerous PhD and MSc students, many of whom have gone on to achieve significant recognition. His group has secured funding that supports interdisciplinary research in imaging science, and several of his former students have launched innovative projects, including a start-up company. He fosters a dynamic research environment with strong industry and cross-institutional collaborations. Labs and Research Groups: He is a key member of the CSP (Communications, Signal Processing, and Control) group at Imperial College London, where he leads a vibrant team working on cutting-edge signal processing challenges. The group is known for its contributions to sampling theory, wavelets, and the integration of deep learning in imaging systems.

Volker ZiebartView profile

Researcher

• Machine Learning
• Optimal Control
• Energy Optimization
• +3 more

Dr. Volker Ziebart is a Researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, focusing on Machine Learning in Optimal Control for Industry . He has led and contributed to projects addressing energy optimization in building control, ecological and economic process optimization in cement production using reinforcement learning and physics-informed machine learning. Role: Researcher at ZHAW School of Engineering Key Projects: Reinforcement Learning for heating systems, energy optimization in buildings, physics-informed ML for cement production His research bridges machine learning with industrial applications, particularly in control systems and spectroscopy. He has published peer-reviewed articles and posters in journals and conferences such as the Journal of Magnetic Resonance and the ANNPR Workshop . While no formal awards are documented, his work reflects expertise in deep learning, predictive modeling, and sustainable manufacturing.

Tailin WuView profile

Assistant Professor

• Generative AI for scientific simulation
• AI Agents for scientific discovery
• Representation learning with graph neural networks
• +3 more

Tailin Wu is an Assistant Professor at Westlake University in the Department of AI within the School of Engineering . He leads the AI for Scientific Simulation and Discovery Lab , focusing on integrating machine learning with fundamental scientific domains. Previously, he conducted postdoctoral research at Stanford Computer Science (2020-2023) under Jure Leskovec , earned his PhD in Physics from MIT (2019) under advisors Isaac Chuang and Max Tegmark , and completed his BSc in Physics at Peking University (2012). Research Interests : Generative AI for scientific simulation (diffusion models, flow matching), AI agents for automated discovery (neural-symbolic integration), representation learning with graph models and information theory His work spans physics-informed machine learning, with applications in fluid dynamics, energy systems, and life sciences. Key contributions include developing methods for safe PDE control with conformal prediction and creating interpretable theory-learning frameworks through the AI Physicist paradigm. Scientific Awards : Best Poster Award, ICML Time Series Workshop (2019) Outstanding Postdoc, Westlake University (2024) Nominated for Outstanding Youth Paper Award, China Embodied AI Conference (2025) As an educator, he teaches Frontiers in Computer Science and Technology at Westlake University and has guest-lectured at Caltech and MIT. His lab actively recruits postdocs, PhD students, and interns.

Ravi Kiran SarvadevabhatlaView profile

Associate Professor

• Computer Vision
• Machine Learning
• Human-Computer Interaction
• +9 more

Ravi Kiran Sarvadevabhatla is an Associate Professor at the International Institute of Information Technology Hyderabad (IIIT-H), with additional roles as Lead (Applied Projects and Academic Programs) at iHub-Data Mobility. His research spans Computer Vision , Machine Learning , and Human-Robot Interaction , focusing on interdisciplinary challenges involving multimodal data (images, sketches, eye-tracking, audio). Research Themes : Computer vision projects in Document Image Understanding , Sketch Recognition , and Depiction-Invariant Object Recognition Cognitive modeling through Eye Tracking Analysis and Pictionary-inspired Models Human-robot interaction studies on Proxemics , Panoramic Attention , and Extended Interaction Analysis Technical Contributions : Pioneered Category-Epitome representations for sparse sketch recognition Developed SketchParse - a deep network for automated sketch parsing Created PPSS-12 benchmark for evaluating object recognition robustness

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.

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +5 more

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.

Christopher BishopView profile

Professor

• Machine Learning
• Artificial Intelligence
• Natural Sciences
• +7 more

Christopher Bishop is a Microsoft Technical Fellow, Director of Microsoft Research AI for Science, and Honorary Professor of Computer Science at the University of Edinburgh. He is a Fellow of Darwin College, Cambridge, the Royal Academy of Engineering (2004), the Royal Society of Edinburgh (2007), and the Royal Society (2017). His career spans theoretical physics, machine learning research, and leadership roles across academia and industry. BA in Physics, University of Oxford PhD in Theoretical Physics, University of Edinburgh His research focuses on applying machine learning to the natural sciences, including quantum field theory, fusion plasma physics, and computational modeling. He pioneered foundational textbooks such as Neural Networks for Pattern Recognition (1995), Pattern Recognition and Machine Learning (2006), and the recent top-selling Deep Learning: Foundations and Concepts (2024), which have profoundly shaped AI education and practice. His publications reflect expertise in probabilistic methods, neural networks, and AI-driven scientific discovery, with a strong emphasis on interdisciplinary applications in physics, mathematics, and computational sciences. Fellow of the Royal Academy of Engineering (2004) Fellow of the Royal Society of Edinburgh (2007) Fellow of the Royal Society (2017) Delivered Royal Institution Christmas Lectures (2008) Founding member of the UK AI Council (2019) Appointed to Prime Minister’s Council for Science and Technology (2019) Bishop established the Neural Computing Research Group at Aston University and led Microsoft Research Cambridge (2015–2022) before founding the AI for Science team. He actively promotes public science engagement and contributes to global AI research strategy.

Manfred OpperView profile

Professor

• Statistical inference
• Bayesian nonparametric models
• Statistical learning theory
• +5 more

Manfred Opper serves as Professor of Computer Science at the School of Electrical Engineering and Computer Science (School IV EECS) of Technische Universität Berlin and holds a Fellowship at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), a joint research institute with the Hasso Plattner Institute. His research program integrates statistical physics with machine learning, focusing on Bayesian nonparametric models, statistical learning theory, and stochastic dynamics on complex networks. Key themes include data assimilation for nonlinear systems, optimal stochastic control applications, and information-theoretic approaches to inference. This interdisciplinary framework bridges theoretical physics and computational intelligence. His recent publication analyzes stochastic control techniques for Bayesian neural network training, reflecting his signature approach of applying statistical physics principles to modern machine learning challenges. This work demonstrates convergence between control theory and probabilistic deep learning. Opper's recognition includes the German Physical Society's Physics Award for early-career contributions. Physics Award of German Physical Society (1992) As a BIFOLD Fellow, he contributes to foundational research in learning systems and data science. The available text does not specify advising activities, grant funding, or laboratory infrastructure details. His professional engagement extends to the German Physical Society (DPG), where he maintains active membership in theoretical physics and statistical mechanics communities.

• Machine Learning
• Economics/Blockchain
• Mathematical Physics
• +8 more

Franck Gabriel is an Associate Professor at University Claude Bernard Lyon 1 , affiliated with the Institut de Science Financière et d'Assurances (ISFA) . His research bridges Machine Learning , Economics/Blockchain , Mathematical Physics , and Random Matrices , with notable work on neural tangent kernels, DeFi protocols, and asymptotic matrix theory. Research Focus : Machine Learning: Theoretical analysis of neural networks, kernel methods, and generalization bounds. Blockchain Economics: Decentralized finance, staking mechanisms, and smart contract design. Mathematical Physics: Holonomy fields, Yang-Mills theory, and random matrix asymptotics. Recent Publications highlight trends in denoising diffusion models, free probability in matrix theory, and DeFi credit systems. His work often integrates cross-disciplinary approaches, merging deep learning with financial technology and quantum field theory. Scientific Awards : 2025 AI 2000 Most Influential Scholar Award in Theory 2024 AI 2000 Most Influential Scholar Award in Theory 2023 AI 2000 Most Influential Scholar Award in Theory As an organizer of the ISFA Seminar , he fosters interdisciplinary discussions in insurance, economics, and machine learning. His collaborations span institutions like Ecole Polytechnique Fédérale de Lausanne, Courant Institute, and EPFL.

• Cardiovascular Engineering
• Hemodynamics
• Machine Learning
• +4 more

Sokratis Anagnostopoulos serves as a Doctoral Assistant at the Hemodynamics and Cardiovascular Technology Laboratory (LHTC) within the Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), while concurrently pursuing doctoral studies in the Mechanics program. His dual role bridges academic research and technical application in cardiovascular systems. His research integrates cardiovascular engineering, computational hemodynamics, and machine learning to develop diagnostic models for arterial waveform analysis and pressure decay phenomena. Key focuses include physics-informed neural networks for fluid dynamics simulation, cardiac output estimation algorithms, and biomedical applications like tracheomalacia correction using hydrogels, demonstrating interdisciplinary innovation across medical and engineering domains. Publication trends (2019-2025) reveal a strong trajectory in machine learning-driven cardiovascular modeling, with recent work emphasizing physics-informed neural networks for hemodynamic diagnostics and renewable energy optimization. Cross-cutting themes include in silico population modeling, diastolic pressure decay analysis, and multi-fidelity transfer learning for engineering systems. No scientific awards were documented in available sources. No student advisement or grant information was specified in the provided materials. He operates within EPFL's Hemodynamics and Cardiovascular Technology Laboratory (LHTC), contributing to the Institute of Bioengineering's mission of advancing cardiovascular technology through computational and experimental approaches.

• Machine Learning
• Predictive Analytics
• Computational Astrophysics
• +3 more

Elena Gavagnin is a Senior Lecturer and Project Leader for Data Science at the ZHAW School of Management and Law's Institute of Business Information Technology. She holds a PhD in Computational Science from the University of Zurich, an MSc in Astrophysics from the University of Bologna, and a BSc in Physics from the same institution. Her work focuses on machine learning, predictive analytics, computational astrophysics, and AI applications in healthcare and technology. Research interests include data science foundations, multimodal AI systems, and radiation hydrodynamics. Notable projects include leading initiatives on AI-driven radiative transfer simulations for the Square Kilometre Array (SKAO), toxicity detection in video games, and webcam-based stroke rehabilitation assessment. Her work bridges astrophysics, healthcare, and information technology. Elena has authored/co-authored over 15 peer-reviewed articles, spanning topics from clinical movement analysis to NLP frameworks and computational astrophysics. Her collaborations include institutions like the University of Zurich and the University of Bologna. She has also contributed to educational initiatives, such as the CAS Higher and Professional Education at ZHAW. Her research emphasizes practical applications of data science, including clinical decision-making tools and ethical AI design. Projects like 'Tele-Assessment' and 'Digital Companion' highlight her focus on real-world healthcare solutions. Ongoing work includes generative deep learning for astrophysical simulations and stateful prompt orchestration in conversational 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.

Freya BehrensView profile

Researcher

• Statistical Physics
• Machine Learning
• Artificial Intelligence
• +3 more

Freya Behrens is a Researcher at the Statistical Physics of Computation Laboratory (SB) within the École Polytechnique Fédérale de Lausanne (EPFL). Her work bridges statistical physics and computational systems, focusing on topics like neural networks, cellular automata, and AI applications in education. She investigates phase transitions in attention mechanisms and the theoretical underpinnings of deep learning. Recent publications explore AI's impact on higher education and the interplay between Hessian matrices and decision boundaries. Her research integrates interdisciplinary methods from physics and computer science, addressing questions such as how memorization occurs in neural networks and the dynamics of spin glasses. Freya’s contributions span theoretical frameworks (e.g., dynamical cavity methods) and applied AI systems (e.g., knowledge graph exploration). While no specific grants or awards are listed, her work demonstrates engagement with cutting-edge challenges in machine learning and computational modeling. She is affiliated with EPFL’s School of Basic Sciences, reflecting her foundational research in physics and mathematics-driven computation.

Berend SmitView profile

Professor

• Molecular Simulation
• Metal-Organic Frameworks (MOFs)
• CO2 Capture and Carbon Capture Technologies
• +4 more

Berend Smit is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences (SB) and the Molecular Simulation Laboratory (LSMO) . His research focuses on molecular simulation, metal-organic frameworks (MOFs), carbon capture technologies, and energy materials. Leads the LSMO, a lab specializing in computational chemistry and materials design. Advises 15+ PhD students in areas like CO2 capture, photocatalysis, and materials informatics. Develops algorithms like MOFChecker for MOF validation and PrISMa for holistic carbon capture assessment. Research Interests: Design and evaluation of nanoporous materials for environmental applications. Integration of machine learning and high-throughput screening for material discovery. Process-informed approaches to carbon capture and DAC (direct air capture). Recent Work: Focuses on MOF-based CO2 capture, thermal transport in ZIFs, and AI-driven inverse design methods. His articles highlight advancements in adsorption modeling, lifecycle assessment for materials, and photocatalytic hydrogen generation. Lab and Teams: Directs the LSMO, collaborating with experimental and computational groups to bridge theory and application in energy and environmental materials.

• Computer Vision
• Machine Learning
• Deep Learning
• +2 more

Thomas Koller is a Professor and Head of the Master's Program in Engineering at Lucerne University of Applied Sciences and Arts, affiliated with the School of Computer Science and Information Technology. His research focuses on computer vision, machine learning, and medical image analysis applications. His primary research domains include: Computer Vision and Image Processing Deep Learning and Reinforcement Learning Medical Imaging and Diagnostic Applications His publications demonstrate strong focus on applying computer vision and deep learning techniques to medical challenges, particularly in dermatology and vascular imaging. Recent work shows increasing emphasis on real-world healthcare applications and interactive systems. Scientific Awards: Best Poster Video Award (2022) aha! Award des Allergiezentrums Schweiz (2017)