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
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MSc in Applied Mathematics, 2016
University of Oxford
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BSc in Physics, 2015
University of Heidelberg
News
- Jul. 2024: I am attending UAI 2024 in Barcelona and ICML 2024 in Vienna. Feel free to contact me if you’d like to chat about causality in general or its applications to simulations, time series or representation learning.
- May 2024: Our paper on Tageted Reduction of Causal Models was accepted as an oral at UAI 2024.
- Mar. 2024: Our paper on Tageted Reduction of Causal Models was accepted at the AI4DifferentialEquations workshop at ICLR 2024.
- Sep. 2023: Our papers on Causal Component Analysis and multi-environment causal representation learning were accepted at NeurIPS 2023.
- Jul. 2023: Our paper on understanding atomic spectra using network science was published in the Journal of Physics: Complexity.
Recent Publications
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(2023).
Nonparametric Identifiability of Causal Representations from Unknown Interventions.
NeurIPS.
(2023).
A Network Approach to Atomic Spectra.
J. Phys. Complex.
(2023).
Evaluating vaccine allocation strategies using simulation-assisted causal modeling.
Cell Patterns.
Experience
PhD Student (Machine Learning and Causality)
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)
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)
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.