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.

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

    University of Oxford

  • BSc in Physics, 2015

    University of Heidelberg

News

Recent Publications

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(2024). Targeted Reduction of Causal Models. UAI.

PDF Cite Code Poster Thread arXiv

(2023). Causal Component Analysis. NeurIPS.

PDF Cite Code Poster Slides Talk Thread arXiv

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

PDF Cite Code Poster Talk (short) Talk (long) Thread arXiv

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

PDF Cite DOI arXiv

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

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Experience

 
 
 
 
 
PhD Student (Machine Learning and Causality)
Sep 2021 – Present Tübingen, Germany
My main interest lies in developing methods for causal representation learning in realistic scenarios. I am a member of the Empirical Inference Department supervised by Bernhard Schölkopf.
 
 
 
 
 
Applied Scientist (Algorithmic Pricing)
Feb 2018 – Aug 2021 Berlin, Germany
At the Pricing and Forecasting Department, our main mission was to develop an automated desicion making system that selects optimal dynamic prices for fashion articles (millions of pricing decisions at each iteration). In particular, I modeled high-dimensional time series using deep learning to predict how price changes affect sales. To make good and reliable decisions in the real world, automated systems have to understand the difference between correlation and causation; this got me intersted in the topic of my PhD.
 
 
 
 
 
Researcher (Spectroscopic Networks)
Mar 2017 – Jan 2018 Heidelberg, Germany
We applied methods from network science to spectroscopic data of atoms and found that we can predict the existence of atomic transitions. Additionally, community structure in spectroscopic networks corresponds to physical properties of the quantum states. This project at the intersection of physics and computer science tried to explore what we can learn about physical systems by purely looking at data science methods, rather than building a microscopic physical model.