Teaching & theses

In the spring term 2026, we are teaching:

• Mobile Health and Activity Monitoring 2026 master course
• Adaptive User Interfaces through Machine Learning 2026 bachelor seminar
• Egocentric Computer Vision: Perception and Understanding from the First-Person View 2026 master seminar
• Reinforcement Learning for Understanding and Modeling Human Behavior 2026 master seminar

Available student thesis projects

8 projects are available (via 8 master’s theses and 6 semester theses). Currently, all bachelor’s theses have been assigned. The list below is up-to-date.

Please submit your CV and transcripts through SiROP. In case of questions, please contact us via SiROP.

• Interactive Video Generation via Iterative Keyframe and Temporal Suggestions

  We will enable controllable video generation to follow the user's intent. Specifically, we will develop an interactive, suggestion-based interface that generates candidate keyframes and temporal structures from weak user prompts, enabling users to iteratively refine video content through selection rather than manual specification.

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  • contact: Zhipeng Li
  • master's thesis

  • Human-AI Interaction
  • Video Generation
  • Interactive Systems
  • Graphics

• Reasoning for Implicit Hand-Object Contact Estimation from Monocular RGB

  This project explores a new approach to hand-object interaction by predicting dense physical contact directly on the surfaces of the hand and object. Given only a monocular RGB image and a canonical 3D object template, we will develop a model for interaction-aware reasoning that infers object-space contact without relying on explicit poses. The project draws on current directions in canonical-space prediction, physically grounded scene understanding, and 3D correspondence learning to reason about visual evidence of contact regions on the object surface.

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  • contact: Andela Ilic
  • semester thesis
  • master's thesis

  • Computer Vision
  • Hand-Object Interaction
  • 3D Vision
  • Contact Estimation
  • Monocular Reconstruction
  • Geometric Deep Learning
  • Interaction-Aware Learning

• Explainable Multimodal Emotion Classification with Chain-of-Thought Prompting

  We will leverage Multimodal Large Language Models (MLLMs) to bridge the gap between "black-box" classification and human-like emotional reasoning. Specifically, we will evaluate Chain-of-Thought prompting for emotion estimation during human-to-human interaction to investigate reasoning traces and jointly evaluate motion and affect dynamics.

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  • contact: Rachel Schuchert
  • semester thesis
  • master's thesis

  • Explainable AI
  • Affective Computing
  • MLLMs
  • Chain-of-Thought
  • Emotion Recognition

• Making AI agents robust for using imperfect tools

  Tool-augmented LLM agents often rely on tools that are usually correct but can occasionally return plausible, incorrect outputs. In this project, we will mitigate these silent errors through a cost-aware strategy that combines cross-tool consistency checks with targeted verification at high-risk steps.

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  • contact: Tommaso Bendinelli
  • semester thesis
  • master's thesis

  • Agent reliability
  • Tool-augmented LLM Agents
  • Robust reasoning

• Benchmarking agentic reasoning robustness on time series

  We will create a research benchmark for evaluating AI agents on decision making from time series. Our benchmark will integrate simulations that expose realistic reasoning failures and edge cases. We will evaluate open-source agentic systems using this benchmark on robustness, reliability, and cost-aware performance.
  The project combines benchmark engineering, controlled experimentation, and empirical analysis to identify which agentic strategies are reliable in realistic time-series decision settings.

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  • contact: Tommaso Bendinelli
  • semester thesis
  • master's thesis

  • ⁠Time-series reasoning
  • Agent reliability
  • Benchmark design
  • AI

• Physics-Based Force Simulation for Hand-Object Interaction

  We will develop and validate a physics-based pipeline to estimate interaction forces from hand-object motion data. Using an existing dataset that provides synchronized hand and object mesh sequences together with measured interaction forces, we will simulate contact forces from motion and compare them against recorded ground truth.

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  • contact: Andela Ilic
  • semester thesis
  • master's thesis

  • hand-object interaction
  • physics simulation
  • biomechanics
  • contact modeling
  • robotics
  • 3D meshes

• Learning-Based Variational Inference for Joint Motion and Sensor Estimation

  Today's sparse inertial sensing for full-body pose estimation relies on fixed, deterministic sensor positions to estimate motion from Inertial Measurement Units (IMUs).
  However, in real-world applications, both body motion and sensor location are unknown variables. In this thesis, we will develop a probabilistic framework to estimate human pose without prior knowledge of sensor placement.

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  • contact: Dominik Hollidt
  • semester thesis
  • master's thesis

  • Probabilistic Deep Learning
  • Variational Autoencoders (VAEs)
  • Sparse Inertial Posing
  • Human Motion Estimation
  • Optimization
  • IMU
  • Bayesian Inference
  • Latent Variable Modeling

• Curved surface routing on discrete surfaces for quad meshing and surface parameterization

  We will develop geometry-processing algorithms for routing coherent curve families on discrete surfaces (triangle meshes) using connection- and field-based transport (in the spirit of trivial connections). The project applies these methods to a classic graphics problem: generating direction fields and separatrix networks that support quad meshing and surface parameterization.

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  • contact: Christian Holz
  • master's thesis

  • geometry processing
  • surface parameterization
  • vector fields
  • discrete surfaces

Current thesis students

• Tim Goppelsroeder master's ETH Data Science, D-INFK
• Nicolas Baeriswyl master's ETH Computer Science, D-INFK
• Qi Ma master's ETH Computer Science, D-INFK
• William Jabbour master's ETH Data Science, D-INFK
• Rafael del Rio Alfonso master's TU/e Electrical Engineering

• Yannic Hostettler semester ETH Robotics, D-MAVT, RSC
• Fei Gao semester UZH Computer Science

Past Courses and Seminars

• Human-Computer Interaction bachelor course
  • 2025
  • 2024
  • 2023
  • 2022
  • 2021
  • 2020
  • 2019
• Mixed Reality master course
  • 2025
  • 2024
  • 2023
• Mobile Health and Activity Monitoring master course
  • 2025
  • 2024
  • 2023
  • 2022
• Ubiquitous Computing master course
  • 2021
  • 2020
• Adaptive User Interfaces through Machine Learning bachelor seminar
  • 2025
• Egocentric Perception for Human-World Understanding master seminar
  • 2025
• Reinforcement Learning for Understanding and Modeling Human Behavior master seminar
  • 2025
• Advanced Topics in Mixed Reality bachelor seminar
  • 2024
  • 2023
  • 2022
• Advanced Topics in Technical Human-Computer Interaction bachelor seminar
  • 2021
  • 2020