جستجوی استاد راهنمای دانشگاهی

Dr. Michael Mühlebach is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, and affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He holds a Bachelor's (2010) and Master's (2013) from ETH Zurich, recognized with awards for academic excellence in Robotics, Systems, and Control. His research focuses on multibody dynamics, nonlinear system control, and model predictive control, with applications in robotics and aerospace systems. His work spans theoretical advancements in variational integrators and practical implementations in systems like the Cubli (a reaction wheel-based 3D inverted pendulum) and flying platforms for ducted fan actuation. Publications highlight contributions to model predictive control schemes with stability guarantees, nonlinear analysis, and distributed event-based state estimation. Key awards include the Outstanding D-MAVT Bachelor Award and Willi-Studer Prize. His research integrates control theory with real-world applications, emphasizing both foundational mathematics and engineering implementations. No grants or lab affiliations are explicitly listed in the provided text.

• Multibody Dynamics
• Control of Nonlinear Systems
• Model Predictive Control
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Konstantinos Karapiperis is a Tenure Track Assistant Professor at EPFL's Laboratory of Multiscale Modeling of Materials (LMD), within the School of Architecture, Civil and Environmental Engineering (ENAC). His research integrates mechanics , multiscale modeling , and data science to study geomaterials and structural materials. PhD in Applied Mechanics (minor in Applied Mathematics), Caltech Postdoctoral Researcher & Lecturer, ETH Zürich (Marie Skłodowska-Curie Fellowship) Research focuses on granular materials , architected materials , and nonlocal modeling using techniques like Level-Set Discrete Element Method (LS-DEM) and machine learning . Recent work explores fracture control via graph neural networks and thermodynamics-informed models. Selected scientific award: Marie Skłodowska-Curie Fellowship Teaches courses in Soil Mechanics and Multiscale Modeling . PhD students include Thomas Henzel and Hrishikesh Gopakumar Menon. His Data-Driven Mechanics Laboratory (LMD) develops predictive tools for granular and structured material behavior.

• Solid Mechanics
• Multiscale Modeling
• Data Science
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Francesca Da Lio is a Professor at the Department of Mathematics, ETH Zurich, where she has held a titular professorship since 2014. Her research focuses on nonlinear elliptic and parabolic partial differential equations (PDEs), with applications in stochastic and deterministic optimal control, homogenization, front propagation, and geometric analysis. She has pioneered work on conformally invariant variational problems and nonlocal PDEs, including fractional harmonic maps and stability analysis for critical points. PhD in Mathematics (1998) and Summa Cum Laude Degree in Mathematics (1994) from University of Padova. Her research explores the interplay between nonlinearity and non-locality, particularly in problems arising from geometry, mathematical finance, and physics. She has led major Swiss National Fund (SNF) projects, including grants for geometric analysis and conformally invariant variational theory. Her work on 3-commutators, integrability by compensation, and Morse index stability has advanced the understanding of harmonic maps and elliptic systems. Francesca Da Lio has mentored numerous PhD, postdoctoral, and Master/Bachelor students, including Dominik Schlagenhauf, Jerome Wettstein, and Ali Hyder. She has served on hiring committees for full professorships at ETH Zurich and co-organized international conferences such as 'Recent Advances in Nonlocal and Nonlinear Analysis' and 'Topics in Sub-Elliptic PDEs.' Scientific Awards: Italian Scientific Qualification as Full Professor in Mathematical Analysis (2013). She contributes to editorial boards, including Advances in Calculus of Variations , and participates in academic services like refereeing for SNF projects and international journals.

• Partial Differential Equations
• Mathematical Analysis
• Geometric Analysis
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Mohamed Farhat is a Senior Scientist at EPFL's School of Engineering, Department of Mechanical Engineering, where he leads the Research Group on Cavitation and Interface Phenomena. He serves as PhD Director, Lecturer, and Member of EPFL Doctoral Committee (Mechanics), while also representing EPFL at CLUSER association and coordinating activities at the Société Hydrotechnique de France (SHF). His research expertise spans Cavitation & Multiphase flows, Flow Induced Noise & Vibration, Fluid-Structure Interaction, Flow control, Flow instabilities in hydro turbines and pumps, Condition monitoring of Hydraulic Machines, Hemodynamics, and Advanced Instrumentation in Fluid Dynamics. Farhat's work uniquely bridges fundamental fluid mechanics with practical applications across hydropower, marine propulsion, healthcare, and water management sectors. Analysis of his recent publications reveals strong focus on cavitation bubble dynamics, with particular emphasis on measurement techniques for collapsing bubbles, vortex shedding control, hydrodynamic monitoring of hydraulic machinery, and biomedical applications of cavitation phenomena. His work increasingly integrates advanced imaging techniques with computational modeling to understand complex multiphase flow phenomena. 2021: Life Sciences Book Award of the International Academy of Astronautics 2019: 1st Prize Winner of Scientific Image Contest (Swiss National Science Foundation) 2020: EPFL-Rhyming Prize (Best PhD thesis in Fluid Mechanics) 2018: EPFL-EDME Prize (Best PhD thesis in Mechanics) 2015: Edmund Optics Educational Award 2014: APS-DFD Gallery of Fluid Motion Award Farhat has successfully supervised numerous PhD students including Ali Amini, Philippe Ausoni, and Outi Supponen, with research spanning from fundamental bubble dynamics to practical hydraulic machinery applications. His Cavitation Research Group maintains strong collaborations with industry partners in hydropower and medical device sectors. Current research directions include advanced instrumentation for cavitation monitoring, condition-based maintenance of hydraulic machinery, and biomedical applications of cavitation phenomena in therapeutic ultrasound and drug delivery.

• Cavitation & Multiphase flows
• Flow Induced Noise & Vibration
• Fluid-Structure Interaction
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Martin J. Gander is Full Professor of Mathematics at the University of Geneva (Faculty of Sciences, Department of Mathematics) and heads the Digital Analysis research group. His career has included positions at ETH Zürich, Stanford, École Polytechnique and McGill University before he moved to Geneva in 2004. Education ETH Zürich Stanford University École Polytechnique, Paris Research Interests Prof. Gander’s work lies at the intersection of numerical analysis and scientific computing , with particular emphasis on: Iterative solvers and preconditioning for large linear systems Domain-decomposition and parallel-in-time methods Absorbing boundary conditions and perfectly matched layers Waveform relaxation and multigrid techniques Geometric integration and mathematical biology Industrial Collaboration He has consulted for companies and institutions such as Venyo , Meteo Suisse , Meteorological Service of Canada , Alcan , Pratt & Whitney and Volvo . Advising & Team Prof. Gander has supervised more than twenty-five doctoral students and post-doctoral fellows, currently including Teilo Wahl , Ausra Pogozelskyte , Si-Wei Liao , Yafei Sun and Liudi Lu .

• Numerical Analysis
• Iterative Methods for PDEs
• Domain Decomposition Methods
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Ralf Hiptmair is a Full Professor at ETH Zürich, serving as Head of the Seminar for Applied Mathematics and Deputy Head of the Department of Mathematics. He also holds the position of Director of Studies for ETH BSc and MSc in Computational Sciences and Engineering (CSE). His research spans computational mathematics, numerical analysis, finite element methods, boundary element methods, computational electromagnetism, multigrid methods, discrete differential forms, shape optimization, wave propagation, and kinetic equations. Hiptmair's work on auxiliary space methods was recognized as a breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science. His research focuses on developing and analyzing numerical methods for partial differential equations, with particular emphasis on structure-preserving discretizations, computational electromagnetism, and boundary integral equations. His work has significant applications in engineering, physics, and computational science. Hiptmair's publications demonstrate a strong focus on advancing numerical techniques for electromagnetic problems, wave propagation, and shape optimization. His recent work shows increasing interest in computational topology, geometric numerical integration, and interdisciplinary applications of numerical methods. Featured as breakthrough in computational science in the 2008 DOE Report on recent significant advancements in computational science (for Auxiliary space methods) Hiptmair has supervised numerous doctoral, master's, and bachelor's students across mathematics, computational science and engineering, and related fields. His research group has received funding for developing advanced numerical methods with applications in electromagnetism, fluid dynamics, and computational physics. He is actively involved in teaching numerical methods courses at both undergraduate and graduate levels. Hiptmair leads research efforts in the Seminar for Applied Mathematics, collaborating with industry partners like ABB Corporate Research and Siemens on practical applications of computational methods. His work bridges theoretical numerical analysis with real-world engineering challenges.

• Computational Mathematics
• Numerical Analysis
• Finite Element Methods
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Dr. Ivana Kovacevic is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. Her research focuses on power electronics, semiconductor device modeling, and electromagnetic analysis of wide bandgap devices. ETH Zürich, Department of Information Technology and Electrical Engineering Contact: kovacevic@aps.ee.ethz.ch Her research explores SiC power MOSFETs, emphasizing their dynamic performance, reliability, and optimization through advanced modeling techniques like the Partial Element Equivalent Circuit (PEEC) method. She investigates parasitic extraction, thermal behavior, and stability issues in power modules, contributing to design improvements for high-efficiency systems. Her publications highlight trends in electromagnetic modeling, device-circuit interactions, and reliability analysis under extreme conditions. Key subfields include gate resistance dynamics, frequency-dependent capacitances, and multi-chip module design. Current projects involve virtual prototyping for power electronics and mission profile-based optimization of wearable power systems.

• Power Electronics
• Semiconductor Device Modeling
• Electromagnetic Modeling
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Andreas Taras is a Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, where he serves as Deputy Head of the Institute of Structural Engineering. His research focuses on steel and composite structures, structural stability, and fatigue behavior. Key research domains include: innovative steel-concrete-timber composite systems, stability and reliability of metallic structures, fatigue life prediction in bridges, and structural optimization using advanced analysis methods. His work integrates experimental testing with computational approaches to advance sustainable structural design. Recent investigations explore cutting-edge applications like iron-based shape memory alloys in structural glass systems, robotic additive joining for reclaimed steel, and novel hybrid systems combining folded sheet steel with cement-free concrete. Publications demonstrate consistent innovation in structural materials and connection technologies.

• Steel Structures
• Composite Materials
• Structural Stability
• +۲ مورد دیگر

Markus Gross is a Professor of Computer Science at ETH Zurich, where he founded the Computer Graphics Laboratory in 1994. He also serves as the Chief Scientist of the Walt Disney Studios and Director of DisneyResearch|Studios, a position he has held since 2008. His work bridges academia and industry, with research that has been applied in Hollywood films, sports broadcasting, and medical applications. Professor Gross received his Master of Science in electrical and computer engineering and his Ph.D. in computer graphics and image analysis from Saarland University in Germany in 1986 and 1989. His research spans multiple domains of computer graphics and visual computing. Early in his career, he pioneered point-based graphics techniques that offered alternatives to traditional triangle-based rendering pipelines. More recently, his work has focused on digital humans, AI characters, and machine learning applications for visual computing. His research has led to significant practical applications, including the Medusa capture system used in Hollywood films, the blue-c immersive telepresence system, and the Liberovision technology now used by major sports broadcasters. Analysis of his recent publications reveals a strong focus on neural rendering techniques, particularly around Gaussian splatting and diffusion models. His work increasingly integrates AI with traditional computer graphics methods, with applications in digital humans, medical visualization, and video processing. Many papers demonstrate practical applications in film production, medical treatment planning, and interactive systems. Professor Gross has received numerous prestigious awards throughout his career: 2024 Eurographics Gold Medal 2021 Steven Anson Coons Award for outstanding creative contributions to computer graphics 2019 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2013 Karl Heinz Beckurts-Preis 2013 Konrad-Zuse-Medaille für Informatik 2013 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences 2012 Academy Sci-Tech Oscar award for Wavelet Turbulence Professor Gross has mentored numerous Ph.D. students throughout his career, with 20 Ph.D. students contributing to his blue-c project alone. His research has been supported by significant funding from both academic and industry sources, enabling the creation of multiple startups including Cyfex, Novodex, LiberoVision, Dybuster, and Animatico (acquired by Nvidia in 2022). He leads the Computer Graphics Laboratory at ETH Zurich and DisneyResearch|Studios, fostering collaboration between academic research and practical industry applications. His teams have developed groundbreaking technologies that have impacted film production, sports broadcasting, medical visualization, and educational technology.

• Artificial Intelligence
• Machine Learning
• Computer Graphics
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Dr. Annelies Voordendag is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich, affiliated with the Geosensorik und Ingenieurgeodäsie group. Her research focuses on cryospheric processes in high-mountain environments, leveraging cutting-edge geospatial technologies. Her primary research interests center on snow-glacier-atmosphere interactions, snow redistribution dynamics, avalanche prediction, and the development of advanced monitoring techniques using terrestrial/airborne lidar systems. She integrates field measurements with numerical modeling to study wind-driven snow transport and glacier mass balance. Recent publications demonstrate consistent focus on lidar-based snow depth mapping, glacier-atmosphere feedbacks, and snow redistribution modeling in Alpine environments. Her work advances quantitative methods for cryospheric monitoring and hazard assessment. She develops novel methodologies combining high-resolution terrestrial laser scans with large-eddy simulations to investigate complex wind-snow interactions on glaciers, significantly improving predictive capabilities for avalanche risks and water resource management.

• Geodesy
• Remote Sensing
• Snow Science
• +۴ مورد دیگر

Peter Scheiblechner is a Lecturer at Lucerne University of Applied Sciences and Arts' School of Engineering and Architecture, within the Department of Natural and Humanities Sciences (ING). He holds a PhD in Mathematics from the University of Paderborn (2007) and has held academic positions including Visiting Assistant Professor at Purdue University (2010-2011) and Postdoc at the Hausdorff Center for Mathematics (2011-2012). His business experience includes software development roles at companies like ClassWare GmbH and UBS in Switzerland. Education: PhD in Mathematics, University of Paderborn (2007) Master's in Mathematics (minor: Physics), Albert-Ludwigs University Freiburg (1997) Bachelor's in Mathematics (minor: Physics), Philipps-University Marburg (1993) High School Diploma, Martin-Luther-Schule Marburg (1991) Research Interests: Focus on applying statistics, data analysis, and machine learning to real-world problems; computational algebra/geometry/topology with complexity theory; algebraic and classical complexity theory. Active in projects like ENFLATE (flexibility markets), COSMOS Data Cockpit (personalized medicine), and topological data analysis. Publications: Over 10 peer-reviewed articles in journals like Journal of Symbolic Computation , Foundations of Computational Mathematics , and Communications in Contemporary Mathematics , with focuses on algorithmic algebraic geometry, complexity analysis, and topological computations. Awards: DFG fellowship (2008-2010), 3rd place in German Mathematics Competition (1991), and regional championship in Hessen (1985/86). Teaching: Teaches mathematics, physics, statistics, and numerical methods at bachelor and master levels, including courses on differential equations, linear algebra, stochastic processes, and engineering applications.

• Statistics
• Data Analysis
• Machine Learning
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Dan Pirjol is a Teaching Associate Professor at the School of Business, Stevens Institute of Technology. He holds a PhD in Theoretical Physics from the University of Mainz and an MS in Physics from the University of Bucharest. His research focuses on financial engineering, derivatives pricing, hedging, and risk management, employing asymptotic analysis and applied probability. Key areas include financial risk management, applied mathematics, numerical methods, and stochastic modeling of financial markets. He has industry experience in roles at Merrill Lynch, Markit Partners, and JP Morgan, specializing in model risk management and derivatives modeling. His publications span topics such as Asian options valuation, volatility modeling, GARCH risk analysis, and stochastic processes. He actively contributes to professional service roles, including co-editing a special issue of Risks and refereeing for journals like SIAM Journal on Financial Engineering. He teaches courses in financial engineering, market microstructure, and algorithmic trading. No scientific awards are listed, and no advising/grant details are provided. His work emphasizes quantitative finance and practical applications of theoretical models.

• Financial Engineering
• Financial Risk Management
• Applied Mathematics
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Amirreza Razmjoo Fard is a PhD student and Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Robot Learning and Interaction (RLI) Group at the Idiap Research Institute. Supervised by Dr. Sylvain Calinon, he focuses on developing adaptive, efficient, and intelligent robotic control methods for contact-rich environments and constrained scenarios. Research Areas: Generative AI (diffusion models, flow matching), Model composition (product of experts), System dynamics, Control theory, Physics-based simulation (Isaac Sim) Key Goals: Bridging theory and real-world applications, enhancing robot autonomy, interaction, and physical intelligence His work spans publications at top robotics conferences like IROS, CoRL, RSS, and ICRA, with a Best Paper Finalist recognition at RSS 2024. Notable methods include CCDP for diffusion policy composition, CDF for differentiable robot geometry, and D-LGP for hybrid planning. He has also collaborated with Honda Research Institute Europe during a six-month internship.

• Robotics
• Artificial Intelligence
• Control Theory
• +۴ مورد دیگر

Adrian Blumenthal is a Lecturer at EPFL (École Polytechnique Fédérale de Lausanne) with an invited guest status, affiliated with the SMA-ENS section of the Institute of Mathematics. His academic role involves teaching specialized courses in computational mathematics. His research focuses on numerical methods for complex dynamical systems, particularly developing integrators for multi-scale differential equations and systems with geometric structures like symplecticity and reversibility. This work bridges theoretical mathematics and computational applications in scientific domains.

• Numerical Analysis
• Dynamical Systems
• Geometric Integration

Michael F. Herbst is a tenure-track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) with joint appointments in Mathematics and Materials Science. He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on algorithm development for quantum-chemical simulations of solids and error control in computational modeling. His work bridges mathematics, solid-state physics, and computer science through interdisciplinary research. His research interests include: Density-functional theory (DFT) and Kohn-Sham equations Error propagation in materials property predictions High-throughput screening algorithms Julia programming for scientific computing Tensor networks and reduced basis modeling Self-consistent field convergence methods Recent publications highlight his contributions to: Efficient response property calculations in DFT Rotationally equivariant machine learning operators GPU-accelerated electronic structure methods Polarizable continuum solvation models Robust black-box quantum chemistry algorithms Open-source software development Teaching activities span mathematics, computer science, and chemistry curricula, including interdisciplinary workshops on electronic structure numerics and Julia programming for materials science. He has mentored PhD students Bruno Ploumhans and Niklas Frederik Schmitz.

• Quantum Chemistry
• Materials Science
• Mathematics
• +۱۲ مورد دیگر