About Me

I'm a machine learning scientist and research engineer with a background in computational astrophysics and statistical physics. I am particularly interested in Bayesian statistics, machine learning, and time series analysis, as well as probabilistic programming with JAX and PyTorch.

My PhD research focused on the diversity of exoplanets in the Milky Way. Before that, I worked on tipping-point detection in paleoclimate time series at the Potsdam Institute for Climate Impact Research.

Skills

Languages

  • Python
  • SQL
  • Julia

JAX Ecosystem

  • JAX
  • Flax
  • Equinox
  • NumPyro
  • BlackJAX

PyTorch Ecosystem

  • PyTorch
  • PyTorch Lightning
  • Pyro

ML / Data

  • TensorFlow/Keras
  • Pydantic AI
  • Scikit-learn
  • NumPy
  • Pandas
  • Polars
  • Ray

HPC & Engineering

  • vLLM
  • Hugging Face
  • SLURM
  • GPU Programming
  • Docker
  • CI/CD
  • FastAPI
  • Git
  • Bash

Software

Here is some of the software I developed or contributed to. I am a big proponent of open-source software and believe in sharing knowledge and tools with the community. I'm also a big fan of probabilistic and differential programming in frameworks like JAX, PyTorch, and their ecosystems.

gallifrey

A JAX-based gaussian process structure learning package for time series modelling and forecasting.

View on GitHub   Documentation
Python Time Series Machine Learning

BlackJAX

Efficient implementation of a advanced sampling algorithms commonly used in Bayesian computation.

View on GitHub   Documentation
Python Bayesian Modelling Monte Carlo Methods Variational Inference

Projects

And here is a list of projects I've done. Some were part of my research, others are just for fun.

Planet Population Synthesis

A comprehensive model for estimating exoplanet population demographics across the Milky Way, combining galactic evolution simulations with planet formation models.

View on GitHub
Python Astronomy Exoplanets

PLATO Yield Predictions

Exoplanet detection predictions for the PLAnetary Transits and Oscillations of stars (PLATO) space mission of the European Space Agency.

View on GitHub
Python Astronomy Exoplanets

Bayesian Galaxy Evolution

A Bayesian framework for tracking galaxy-halo-super massive black hole connection through cosmic time, using MCMC sampling.

View on GitHub
Python Astronomy Galaxy Evolution Bayesian Modelling

Community

BLISS Berlin

Volunteer organiser of a Berlin-based ML research talks series, hosting speakers from Google DeepMind, HuggingFace, Cohere, Oxford, and Stanford. A way of staying connected to the academic ML community and helping build it locally.

Machine Learning Community Research

Education & Employment

2025–current

Applied Machine Learning Scientist

sidekicks AI, Berlin, Germany (Remote)

Serving large open-weight LLMs (up to ~300B parameters) on multi-GPU nodes via vLLM, applying tensor, pipeline, and expert parallelism. Building production-grade agentic ML systems with Pydantic AI, metric-driven evaluation suites, and data engineering pipelines for terabyte-scale daily processing.

2021–2025

Ph.D. in Astrophysics

University of Groningen, The Netherlands

Focus: Exoplanet Science, Bayesian Modelling, Astrostatistics

2020–2021

Research Intern (AI in the Anthropocene)

Potsdam Institute for Climate Impact Research, Germany

Developed a novel algorithm for out-of-distribution detection in non-stationary climate time series; authored two peer-reviewed publications on tipping-point detection in paleoclimate records. Explored LSTM/RNN approaches for climate-transition modelling.

2018-2021

M.Sc. in Physics

Humboldt University of Berlin, Germany

Focus: Complex Systems, Statistical Physics

2018–2019

Research Intern

Leibniz Institute for Astrophysics (AIP), Germany

Optimised a data-processing pipeline for terabyte-scale N-body cosmological simulations, replacing an O(n²) brute-force search with a k-d tree, reducing runtime from hours to minutes.

2015–2018

B.Sc. in Physics

Humboldt University of Berlin, Germany

Publications

  1. C. Boettner, Astronomy & Astrophysics 2024 gallifrey: JAX-based Gaussian process structure learning for astronomical time series Paper | GitHub | Documentation
  2. C. Boettner, A. Viswanathan, and P. Dayal, Astronomy & Astrophysics 2024 Exoplanets across galactic stellar populations with PLATO: Estimating exoplanet yields around fgk stars for the thin disk, thick disk and stellar halo Paper | GitHub | Zenodo
  3. C. Boettner, P. Dayal, M. Trebitsch, N. Libeskind, K. Rice, C. Cockell, and B. I. Tieleman, Astronomy & Astrophysics 2024 Populating the milky way: Characterising planet demographics by combining galaxy formation simulations and planet population synthesis models Paper | GitHub
  4. C. Boettner, M. Trebitsch, and P. Dayal, Astronomy & Astrophysics, 2025 A bayesian approach to the halo-galaxy-smbh connection through cosmic time Paper | GitHub | Zenodo
  5. C. Boettner and N. Boers, 2022, Physical Review Research Critical slowing down in dynamical systems driven by nonstationary correlated noise
  6. C. Boettner, G. Klinghammer, N. Boers, T. Westerhold, and N. Marwan, 2021, Quaternary Science Reviews Early-warning signals for cenozoic climate transitions