Department of Computer Science
Computer Graphics Laboratory
ETH Zurich
Universitätsstrasse 6
CNB G 106.1
I am a senior research scientist at the Computer Graphics Laboratory at ETH Zurich, Switzerland, where I am heading the group on Simulation and Animation. I am also affiliated with the Institute for Advanced Study at the Technical University of Munich, where I hold a Hans Fischer Fellowship awarded by the Siemens AG. I received my Ph.D. in Computer Science from the University of Zurich. My research focuses on physics simulations and animations for computer graphics. I am particularly interested in methods for efficient, robust and artist-controllable simulations, and use data-driven techniques to transform and simplify workflows. I regularly serve on technical program committees of major graphics conferences including ACM SIGGRAPH, Eurographics and ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA), and was one of the conference chairs of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2016. I am also appointed as an Associate Editor of Computer Graphics Forum (CGF). I am a co-founder of Apagom AG that provides a real-time fluid engine using machine learning.
I am always looking for Master students, PhD students and Post-Docs to do research in physically-based simulation, fluid modeling and applied machine learning. Please contact me if you are interested!
Visit our group website Simulation and Animation
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Lagrangian Neural Style Transfer for Fluids
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Image Reconstruction of Tablet Front Camera Recordings in Educational Settings
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Glyph-Based Visualization of Affective States
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Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks
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Neural Smoke Stylization with Color Transfer
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Affective State Prediction Based on Semi-Supervised Learning from Smartphone Touch Data
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Transport-Based Neural Style Transfer for Smoke Simulations
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Affective State Prediction in a Mobile Setting using Wearable Biometric Sensors and Stylus
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Smoothed Particle Hydrodynamics for Physically-Based Simulation of Fluids and Solids
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Reliability of a Three-dimensional Facial Camera for Dental and
Medical Applications: A Pilot Study
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Deep Fluids: A Generative Network for Parameterized Fluid Simulations
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Ten Years of Research on Intelligent Educational Games for Learning Spelling and Mathematics
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Efficient Feature Embeddings for Student Classification with Variational Auto-encoders
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Anaglyph Caustics with Motion Parallax
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Temporally Coherent Clustering of Student Data
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Stealth Assessment in ITS - A Study for Developmental Dyscalculia
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Data-driven Fluid Simulation using Regression Forests
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HeapCraft: Interactive Data Exploration and Visualization Tools for Understanding and Influencing Player Behavior in Minecraft
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Quantifying and Predicting Collaboration in Shared Virtual Worlds
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Example-based Structure Synthesis
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HeapCraft Social Tools: Understanding and Improving Player Collaboration in Minecraft
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Statistical Analysis of Player Behavior in Minecraft
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On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models
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A Parallel Architecture for IISPH Fluids
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SPH Fluids in Computer Graphics
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Mass Preserving Multi-Scale SPH
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Modelling and Optimizing Mathematics Learning in Children
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Implicit Incompressible SPH
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Cluster-Based Prediction of Mathematical Learning Patterns
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Towards a Framework for Modelling Engagement Dynamics in Multiple Learning Domains
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Versatile Rigid-Fluid Coupling for Incompressible SPH
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Two-Scale Particle Simulation
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SPH Based Shallow Water Simulation
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Interactive SPH Simulation and Rendering on the GPU
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Inkompressible Fluid Simulation und verbesserte Oberflächenbehandlung mit SPH
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Incompressible Fluid Simulation and Advanced Surface Handling with SPH
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Performance Comparison of Parallel PCISPH and WCSPH
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Predictive-Corrective Incompressible SPH
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Density Contrast SPH Interfaces
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Adaptive Sampling and Rendering of Fluids on the GPU
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A Unifed Particle Model for Fuid-Solid Interactions
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Efficient Refinement of Dynamic Point Data
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Particle-Based Fluid-Fluid Interaction
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Simultaneous Topology and Stiffness Identification for Mass-Spring Models based on FEM Reference Deformations
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