جست‌وجوی استاد و پژوهشگر

• AI-based Applications
• Computer Vision
• Environmental and Energy Domain
• +۴ مورد دیگر

Prof. Martin Melchior is a Professor at FHNW University of Applied Sciences, serving as Deputy Head of the Institute for Data Science. His primary roles include lecturing in Data Science and Computer Science, supervising thesis projects, and leading research in AI applications for computer vision, environmental energy systems, and astro-informatics. He holds a PhD in Physics from ETH Zurich and has extensive experience in risk modeling and space instrumentation projects. Education highlights include a PhD in theoretical polymer science (1993-1996) and studies in physics/mathematics at ETH Zurich (1986-1992). His research focuses on machine learning in astrophysics, cosmological surveys like Euclid, and large-scale data management for space missions. Key projects include contributions to the Euclid mission's ground segment and SwissPRODEX-funded operations. Recent research interests emphasize gravitational lensing analysis, galaxy clustering, and cosmological parameter estimation via Euclid data. He has led/co-led numerous applied science projects in energy optimization, medical diagnostics, and environmental monitoring technologies. His work bridges academia and industry through collaborations with institutions like EPFL, University of Geneva, and companies such as AINEAS GmbH and Swisens AG.

Marco Zaffalonمشاهده پروفایل

استاد

• Machine Learning
• Causal AI
• Probabilistic Graphical Models
• +۳ مورد دیگر

Marco Zaffalon is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and Scientific Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). He holds an MSc in Computer Science (cum laude) and a PhD in Applied Mathematics. His research focuses on causal AI, machine learning, and probabilistic models under uncertainty, with applications in defense, environment, medicine, engineering, and business. He leads a 30-member research group in probabilistic machine learning and has secured over 15 million Swiss Francs in competitive grants. Education: MSc in Computer Science (cum laude), PhD in Applied Mathematics. Professional milestones include roles as a software engineer at an Italian insurance group, consultant for SGS-Thomson, post-doctoral fellow at IDSIA, and tenure as a researcher since 2001. Research interests emphasize foundational and applied aspects of probability theory, particularly imprecise probability, and causal reasoning. His work integrates knowledge-based systems, probabilistic graphical models, and robust algorithms for real-world problems. Notable projects include OmniProfiler , Credal Model Averaging , and Learning under Near-Ignorance . Awards & Recognition: President of SIPTA (2019–present) Senior Area Editor for International Journal of Approximate Reasoning (IJAR) Top 2% Scientist (Stanford’s ranking since 2019) Grants & Leadership: Oversaw 15+ million CHF in competitive research funding Founded and directs a 30-member research group He collaborates on interdisciplinary projects, including environmental modeling, medical applications, and defense-related AI systems through IDSIA’s labs and partnerships.

Nicolas Flammarionمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Optimization
• +۳ مورد دیگر

Nicolas Flammarion is a tenure-track Assistant Professor in Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL). He holds positions in multiple departments including the Theory in Machine Learning (TML) lab under the School of Computer and Communication Sciences (IC). His roles include teaching and doctoral program leadership across disciplines like Communication Systems and Computer Science. Education: PhD in 2017 from École Normale Supérieure (Paris), advised by Alexandre d’Aspremont and Francis Bach. Postdoctoral fellowship at UC Berkeley under Michael I. Jordan. Research focuses on machine learning theory, optimization, and statistical methods. Key areas include adversarial robustness, algorithmic generalization, and optimization dynamics. Notable contributions include work on SGD/GD comparisons, adversarial benchmarking (RobustBench), and safety in AI systems. Awards include the 2018 Fondation Mathématique Jacques Hadamard PhD Prize, 2021 NeurIPS Outstanding Paper Award, and 2024 Trust & Safety Google Research Award. Supervises PhD students in topics like algorithm design and machine learning theory. Labs/Teams: Leads TML lab and contributes to EPFL's doctoral programs in Informatics and Communication Sciences. Active in curriculum development for advanced machine learning topics.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +۳ مورد دیگر

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.

• Theoretical Computer Science
• Computational Complexity
• Optimization Algorithms
• +۳ مورد دیگر

Dr. Lucas Slot is a Lecturer at the Department of Computer Science, ETH Zurich, specializing in theoretical computer science, computational complexity, and optimization algorithms. His research focuses on polynomial optimization, sum-of-squares hierarchies, and semidefinite programming, with applications to algorithmic design and complexity analysis. Recent work includes studies on computational thresholds in stochastic block models, convergence rates of optimization hierarchies, and kernel-based methods for high-dimensional inference. His contributions span theoretical foundations and algorithmic advancements in mathematical programming and geometric data analysis. Lacking explicit mentions of academic awards or grants, Dr. Slot’s scholarly activities emphasize computational and mathematical challenges in optimization and discrete geometry. No student advisees are listed in the provided materials.

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +۳ مورد دیگر

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.

Erwin Rieglerمشاهده پروفایل

مدرس

• Information Theory
• Signal Processing
• Communications
• +۳ مورد دیگر

Dr. Erwin Riegler is a Lecturer at the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zurich. His research focuses on information theory, signal processing, and their applications in communication systems, with particular expertise in analog compression, matrix completion, and uncertainty principles. He has contributed to theoretical frameworks for rate-distortion analysis, lossless compression techniques, and the design of efficient algorithms for MIMO systems. Key research areas include: Information-theoretic limits of data compression Signal processing in analog systems Algorithmic approaches for sparse signal recovery Optimization techniques in MIMO communication channels Recent work emphasizes interdisciplinary applications, such as neural networks for measure theory and probabilistic methods for signal separation. No scientific awards are explicitly mentioned in the provided texts. His academic contributions span over 30 peer-reviewed articles since 2001, covering topics from string theory to modern communication systems.

Evren Mert Turanمشاهده پروفایل

مدرس

• Process Systems Engineering
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

Dimosthenis Pasadakisمشاهده پروفایل

پژوهشگر ارشد

• Graph Theory and Algorithms
• Data Science
• Machine Learning
• +۳ مورد دیگر

Dimosthenis Pasadakis is a Research Fellow at the Institute of Computing (CI) of the Università della Svizzera italiana (USI), where he serves as the Principal Investigator (PI) of the Huawei Research Center Zürich-funded project on Directed Acyclic Graph Partitioning for Scheduling Tasks. His research focuses on graph learning, combinatorial optimization, and large-scale algorithms for clustering, anomaly detection, and data analysis. He also holds the role of Chief Operating Officer (COO) at Panua Technologies Sagl, a Lugano-based software company specializing in high-end solutions for graph analytics and optimization. His academic journey includes a PhD successfully defended in 2023, supported by the Swiss National Science Foundation (SNSF) project on Balanced Graph Partition Refinement. He has organized minisymposia at SIAM LA 24 and PASC 24, and his work has been recognized with awards such as the IEEE HPEC Outstanding Paper Award (2024) and the IEEE SDS24 Best Poster Award (2024). Key research interests include spectral clustering, graph-based fraud detection, and high-performance computing. He actively collaborates with industry through grants and maintains an academic-industrial bridge through Panua Technologies.

Martin Krögerمشاهده پروفایل

مدرس ارشد

• Polymer Physics
• Computational Physics
• Applied Mathematics
• +۳ مورد دیگر

Prof. Dr. Martin Kröger is a Senior Lecturer at the Department of Materials, ETH Zurich. His research focuses on polymer physics, computational physics, applied mathematics, stochastic differential equations, coarse-graining, and biophysics. He leads courses such as Computational Thinking Labs and Polymers I , and serves as the D-MATL Research Integrity Advisor. His work bridges theory and simulation, addressing complex systems like polymer dynamics, nanocomposites, and epidemic modeling. He has pioneered studies on polymer confinement, nanoparticle interactions, and chromatin conformation. His research tools include molecular dynamics simulations and analytical models for epidemics. Key Projects: Computational Polymer Physics, NOVA & POLYHUB initiatives. Roles: Course coordinator, research advisor. Publications span Soft Matter, Physical Review, and interdisciplinary journals. His lab develops models for material behavior under extreme conditions and biological systems.

Federica Lanzaمشاهده پروفایل

مدرس

• Seismology
• Geophysics
• Volcanology
• +۳ مورد دیگر

Federica Lanza is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zurich, affiliated with the Schweiz. Erdbebendienst (SED), the Swiss Seismological Service. Her work focuses on seismology, geophysics, and geothermal systems, with expertise in induced seismicity, fault dynamics, and advanced monitoring technologies like Distributed Acoustic Sensing (DAS). She teaches courses such as Seismic Waves II in the Autumn Semester 2025. Her research integrates field experiments, computational modeling, and machine learning to address challenges in seismic hazard assessment, geothermal energy development, and tectonic processes. Key areas include forecasting induced earthquakes at geothermal sites, analyzing fault interactions in fold-and-thrust belts, and developing innovative sensor systems for subsurface monitoring. Dr. Lanza collaborates on large-scale projects like the Utah FORGE initiative, advancing techniques for real-time seismic monitoring and fracture network characterization. Her contributions bridge fundamental geophysical research with practical applications in energy systems and risk mitigation.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +۳ مورد دیگر

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Markus Püschelمشاهده پروفایل

استاد

• Program Generation
• Fourier Analysis
• Signal Processing
• +۶ مورد دیگر

Markus Püschel is a Professor of Computer Science at ETH Zurich, leading the Advanced Computing Laboratory. He previously served as Head of the Department of Computer Science at ETH from 2013 to 2016. Before joining ETH in 2010, he was a Professor at Carnegie Mellon University (CMU) and retains adjunct status there. He holds a PhD in Computer Science (1998) and a Mathematics Diploma (1995) from the University of Karlsruhe. His research focuses on program generation for performance, Fourier analysis, signal processing, machine learning, and compiler optimization. He pioneered the SPIRAL system for generating optimized signal processing libraries and has contributed to algebraic signal processing theory. His work bridges mathematical foundations with practical high-performance computing, emphasizing automated code generation and domain-specific languages. Research trends in his articles include causal inference on directed acyclic graphs, quantized neural network inference, and efficient compiler techniques. His work on graph signal processing and causal analysis has led to novel methods in time-series data and DAG learning. Awards: IEEE Fellow (2020), Golden Owl Teaching Award (2015), NSF Discovery Grant (2008), and multiple best paper awards. Grants/Projects: Co-PI for Making Program Analysis Fast (SNF), SPIRAL GPU projects, and collaborations with industry partners like Intel and AMD. He advises over 40 PhD and master’s students, many of whom contribute to impactful projects in compilers, machine learning, and signal processing. His lab also co-founded the Swiss Data Science Center.

Kai Hormannمشاهده پروفایل

استاد

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +۳ مورد دیگر

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.

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +۲ مورد دیگر

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.