Susanne Hindennach

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building Google Scholar

Biography

Susanne Hindennach is a PhD student in the Perceptual User Interfaces group since June 2020. She holds a Bachelor’s degree in Cognitive Science from the University of Osnabrück and a Master’s degree in Clinical and Experimental Neuroscience from the University of Cologne. After her studies, she worked as a data science consultant for three years, where she got her hands on data from various industries. Returning to research, she is interested in understanding and modelling concepts about human interaction such as Theory of Mind.

Teaching

2022

Machine Perception and Learning (Tutor)Master

 

Medieninformatik (Teaching Assistant)Bachelor

2021

Medieninformatik (Teaching Assistant)Bachelor

2020

Machine Learning Approaches to Understanding User Behavior (Hauptseminar)Master

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Supervision

2023

A tool for digitizing the meal ordering process at the Dreifürstensteinschule in Dornstetten (B.Sc.) Ramazan Sarica

2021

An Interactive Information Visualization Tool for Analyzing Relations Between Academic Papers Through User-defined Tags (B.Sc.) Michael Hersam*

*Co-supervised with Nele Quast

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Open Thesis Projects

 

Mind Attribution in CodeBachelor

 

Mind Attribution in RegulationsBachelor/Master

 

Explain Yourself to Reveal Your MindBachelor/Master

 

Probing Interactive Explainable AIBachelor/Master

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Publications

(Zhang et al., 2022)

1. 

   Predicting Next Actions and Latent Intents during Text Formatting

   Guanhua Zhang, Susanne Hindennach, Jan Leusmann, Felix Bühler, Benedict Steuerlein, Sven Mayer, Mihai Bâce, Andreas Bulling

   Proceedings of the CHI Workshop Computational Approaches for Understanding, Generating, and Adapting User Interfaces, pp. 1–6, 2022.

   Abstract Links BibTeX Project

   In this work we investigate the challenging task of predicting user intents from mouse and keyboard input as well as gaze behaviour. In contrast to prior work we study intent prediction at two different resolutions on the behavioural timeline: predicting future input actions as well as latent intents to achieve a high-level interaction goal. Results from a user study (N=15) on a sample text formatting task show that the sequence of prior actions is more informative for intent prediction than gaze. Only using the action sequence, we can predict the next action and the high-level intent with an accuracy of 66% and 96%, respectively. In contrast, accuracy when using features extracted from gaze behaviour was significantly lower, at 41% and 46%. This finding is important for the development of future anticipatory user interfaces that aim to proactively adapt to user intents and interaction goals.

   Paper: zhang22_caugaui.pdf

   @inproceedings{zhang22_caugaui, author = {Zhang, Guanhua and Hindennach, Susanne and Leusmann, Jan and Bühler, Felix and Steuerlein, Benedict and Mayer, Sven and Bâce, Mihai and Bulling, Andreas}, title = {Predicting Next Actions and Latent Intents during Text Formatting}, booktitle = {Proceedings of the CHI Workshop Computational Approaches for Understanding, Generating, and Adapting User Interfaces}, year = {2022}, pages = {1--6} }