Armin Kekić

Armin Kekić

PhD Student in Causality and Machine Learning

I’m a PhD Student at the Max Planck Institute for Intelligent Systems, specializing in integrating causal inference and reasoning into machine learning algorithms.

Prior to my PhD, I worked as an applied scientist at Zalando, where I developed machine-learning-based demand forecast models for price optimization.

My academic background includes studies in physics and applied mathematics at Heidelberg, Oxford, and Paris, with a specific focus on theoretical quantum dynamics and simulation methods.

On this website, I share projects that I work on.

  • Machine Learning
  • Causality
  • Time Series Forecasting
  • Network Science
  • MSc in Applied Mathematics, 2016

    University of Oxford

  • BSc in Physics, 2015

    University of Heidelberg


Recent Publications

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(2023). Targeted Reduction of Causal Models. arXiv.

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(2023). Causal Component Analysis. NeurIPS.

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(2023). Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS.

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(2023). A Network Approach to Atomic Spectra. J. Phys. Complex.

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(2023). Evaluating vaccine allocation strategies using simulation-assisted causal modeling. Cell Patterns.

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PhD Student
Sep 2021 – Present Tübingen, Germany
Working on Causality and Machine Learning.
Applied Scientist
Feb 2018 – Aug 2021 Berlin, Germany
Development of high-dimensional time series models based on deep neural networks that are used for algorithmic price optimisation.
Mar 2017 – Jan 2018 Heidelberg, Germany
Research on spectroscopic networks.
Research Intern
Jul 2014 – Sep 2014 Singapore
Design of an optical set-up for Rydberg-atom imaging using electromagnetically induced transparency.