Emanuel Slany

Research Assistant, Doctoral Candidate

Fraunhofer IIS - Comprehensible Artificial Intelligence
BMBF Project hKI-Chemie

Office: WE5/04.025
Office hours: by appointment

Email:
emanuel.slany(at)uni-bamberg.de 
emanuel.slany(at)iis.fraunhofer.de

 

Emanuel Slany holds a bachelor degree in politcal science as well as a master degree in statistics, both obtained at the University of Bamberg. He currently is a scientific researcher in the project group Comprehensible Artificial Intelligence at the Fraunhofer IIS. His research focuses on developing Explanatory Interactive Machine Learning algorithms.

 

since 10/2021

Research Assistant at Fraunhofer IIS - Comprehensible Artificial Intelligence

07/2020 -

09/2021

Data Scientist at HUK-Coburg

09/2018 -

06/2020

Student Research Assistant at Fraunhofer IIS - Facial Analysis Solutions and Digital Sensory Perception

04/2018 -

03/2020

M.Sc. Survey Statistics at University of Bamberg

10/2014 -

03/2018

B.A. Political Science at University of Bamberg

Heidrich, L., Slany, E., Scheele, S., Schmid, U. (2023). FairCaipi:  A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction. Mach. Learn. Knowl. Extr. 2023, 5, 1519-1538. doi.org/10.3390/make5040076

Slany, E., Scheele, S., Schmid, U. (2023). Bayesian CAIPI: A Probabilistic Approach to Explanatory Interactive Machine Learning. European Conference on Artificial Intelligence. ECAI Workshop Proceedings. ECAI 2023. to appear

Slany, E., Ott, Y., Scheele, S., Paulus, J., Schmid, U. (2022). CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. doi.org/10.1007/978-3-031-08341-9_31

 

Talks

Slany, E. PHAL Post-Hoc model Approximation with Logic. Nordic Probabilistic AI Summer School 2023. Trondheim (Norway), 15 June 2023.

Slany, E. Explainable Gaussian Process Regression with Probabilistic Influence and Logic. Heinz-Nixdorf Symposium 2022. Paderborn (Germany), 15 September 2022.

Slany, E. CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. Heinz-Nixdorf Symposium 2022. Paderborn (Germany), 16 September 2022.

Reviews

The 38th Annual AAAI Conference on Artificial Intelligence (2023)

45th German Conference on Artificial Intelligence (2022)

KI - Künstliche Intelligenz (2022)

Lukas Gernlein (2023). An Explanatory and Interactive Machine Learning Approach for Multi-Label Classification. Master Thesis, supervision by Emanuel Slany and Stephan Scheele.

Felix Hempel (2023). Explainable and Interactive Machine Learning with Counterfactuals and Ordinal Data. Master Thesis, supervision by Emanuel Slany and Stephan Scheele.

Solveig Rabshal (2023). Exploring the Impact of Scale of Measurement on Counterfactual Explanations. Bachelor Thesis, supervision by Emanuel Slany and Stephan Scheele.

Yannik Ott (2022). An explanatory interactive machine learning approach for image classification in medical engineering. Bachelor Thesis, supervision by Emanuel Slany and Ute Schmid.

Oraz Serdarov (2022). Explainable Unsupervised Learning for Fraud Detection. Cooperation with HUK-Coburg, Master Thesis, supervision by Emanuel Slany and Ute Schmid.