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's degree in Political Science, which he obtained from the University of Bamberg in 2018. Also at the University of Bamberg, he achieved a Master's degree in Survey Statistics in 2020. His research was focused on numerical approximation of Bayesian neural networks. During his master studies, Emanuel Slany worked at Fraunhofer IIS. Afterwards, Emanuel Slany worked as a Data Scientist for HUK-Coburg. A year and a half later, he returned to the Fraunhofer IIS, where he is currently working on the Human-Centered AI project to integrate logic with numerics in order to make Bayesian multi-objective optimization methods interactive and explainable. He is also pursuing a doctoral degree in this research area at the chair of Cognitive Systems.

 

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

The Comprehensible Artificial Intelligence (AI) group at Fraunhofer IIS builds trustworthy AI systems that actively interact with humans.

Our current research focus is Neuro-Symbolic Integration - thus, a combination of state-of-the-art Machine Learning algorithms with symbolic approaches like probabilistic logic.

This includes but is not limited to the following topics:

  • Bayesian Learning
  • Probabilistic Inductive Logic Programming
  • Computational Statistical Algorithms

 

Emanuel Slany, Yannik Ott, Stephan Scheele, Jan Paulus, Ute Schmid (2022). CAIPI in Practice: Towards Explainable Interactive Medical Image Classification. 18th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2022), Springer LNCS.

 

Talks

11th Heinz Nixdorf Symposium - Explainable AI (2022)

Chairs

18th International Conference on Artificial Intelligence Applications and Innovations AIAI 2022 - Evolutionary Computing (2022)

Reviews

45th German Conference on Artificial Intelligence (2022)

Springer (2022)

KI - Künstliche Intelligenz (2022)

Felix Hempel (2023). Explainable and Interactive Machine Learning with Counterfactuals and Ordinal Data. Master 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.