Andreas Hinterreiter

Andreas Hinterreiter

I’m a postdoctoral researcher and Project Assistant in the Visual Data Science Lab. My research focuses on bringing techniques from visualization and machine learning together. I am especially interested in dimensionality reduction and explainbale AI.

My PhD project was a collaboration with Imperial College London. As a part of that project, I spent one year at the Biomedical Image Analysis (BioMedIA) group at Imperial. I finished my PhD in December 2022 with my thesis on Visual Explanations of High-dimensional and Temporal Processes.

I received my Masters degree in Technical Physics at Johannes Kepler University Linz, working on the structural and chemical analysis of surfaces. As I had grown increasingly interested in machine learning, visualization and graphic design, I decided to transition to visualization research for my PhD.



Peer-Reviewed Journal and Conference Papers

Fuzzy Spreadsheets screenshot

Vaishali Dhanoa, Conny Walchshofer, Andreas Hinterreiter, Eduard Groeller, Marc Streit
Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations
IEEE Transactions on Visualization and Computer Graphics (Early Access), 2021

InstanceFlow screenshot

Michael Pühringer, Andreas Hinterreiter, Marc Streit
InstanceFlow: Visualizing the Evolution of Classifier Confusion at the Instance Level
2020 IEEE Visualization Conference – Short Papers, 2020

ConfusionFlow screenshot

Andreas Hinterreiter, Peter Ruch, Holger Stitz, Martin Ennemoser, Jürgen Bernard, Hendrik Strobelt, Marc Streit
ConfusionFlow: A Model-Agnostic Visualization for Temporal Analysis of Classifier Confusion
IEEE Transactions on Visualization and Computer Graphics, 2022

Provectories screenshot

Conny Walchshofer, Andreas Hinterreiter, Kai Xu, Holger Stitz, Marc Streit
Provectories: Embedding-based Analysis of Interaction Provenance Data
IEEE Transactions on Visualization and Computer Graphics (Early Access), 2021

Projective Latent Interventions screenshot

Andreas Hinterreiter, Marc Streit, Bernhard Kainz
Projective Latent Interventions for Understanding and Fine-tuning Classifiers
Interpretable and Annotation-Efficient Learning for Medical Image Computing. Proceedings of the 3rd Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2020), 2020
 Best Paper Award at iMIMIC 2020


Selected Abstracts and Posters

Christina Humer, Mohamed Elharty, Andreas Hinterreiter, Marc Streit,
Interactive Attribution-based Explanations for Image Segmentation   Abstract     Poster     Video  
EG Conference on Visualization (EuroVis '22), Rome, IT, 2022.