Nicholas (Nico) Krämer

I am a machine learning researcher based in Copenhagen. I do research at the interface of numerical methods and machine learning, including topics such as probabilistic numerics, differentiable programming, state-space models, numerical linear algebra, Bayesian machine learning, or physics-informed machine learning (Google Scholar). I write a bunch of code, mainly in Python/JAX (Github: @pnkraemer).

I am currently a postdoc in Søren Hauberg's group at DTU. Before that, I did probabilistic numerics in Philipp Hennig's group in Tübingen, and before that, scattered data approximation with Christian Rieger in Bonn. I studied Mathematics and Business Mathematics in Bonn and Mannheim. I am a member of the ELLIS Society and an affiliated researcher at the Pioneer Centre for AI (P1).

If you need a three-sentence biography, e.g. for announcing a talk, [click here].

I go by "Nico", but use "Nicholas" when writing papers. Online, I tend to be @pnkraemer (and "pn" stands for "Peter Nicholas", not for "probabilstic numerics"). Find my papers on Google Scholar and my code on GitHub. Talk to me in English or German. I would love to hear from you.

Contact

Google Scholar: click here
GitHub: @pnkraemer
Bluesky: @pnkraemer.bsky.social
LinkedIn: link here
X: @pnkraemer
Email: pekra (at) dtu (dot) dk

News

• June 2025: My paper on "Adaptive probabilistic ODE solvers without adaptive memory requirements" has been accepted at the International Conference on Probabilistic Numerics.
• March 2025: Our preprint on "Numerically robust Gaussian state estimation with singular observation noise" is on arXiv now! Here is a link.
• January 2025: My paper on "Numerically robust fixed-point smoothing without state augmentation" has been now been published by TMLR! Here is a link to the PDF on OpenReview. Here is a link to the code on GitHub.
• January 2025: My paper on "Numerically robust fixed-point smoothing without state augmentation" has been accepted by TMLR! A link to the published paper will follow soon.
• December 2024: I am speaking at the D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers Workshop at Neurips 2024.
• November 2024: I will be again talking about adaptive ODE solvers, this time in Oxford.
• November 2024: I will be talking about adaptive ODE solvers at the Computer Lab in Cambridge. Here is a link to the announcement.
• November 2024: I will be visiting our CUQI-neighbours at DTU Compute to give a talk as a part of their seminar series.
• October 2024: Our paper on "Gradients of functions of large matrices" has been accepted as a spotlight at Neurips. Looking forward to presenting this in Vancouver! Here is a link to the arXiv version.
• October 2024: My preprint on "Adaptive probabilistic ODE solvers without adaptive memory requirements" entered arXiv. Here is a link.
• October 2024: My preprint on "Numerically robust fixed-point smoothing without state augmentation" entered arXiv. Here is a link.
• September 2024: My preprint on "A tutorial on automatic differentiation with complex numbers" entered arXiv. Here is a link.
• September 2024: If you're at GenU in Copenhagen (September 18-19), come and say hi! I'll bring a poster on computing gradients of functions of large matrices. Here is a link to the preprint.
• July 2024: I am giving a talk on the "Probabilistic numerical method of lines", this time at ProbNum24 in London on July 15.
• June 2024: I am giving a talk on the "Probabilistic numerical method of lines" at the Probabilistic Numerics and Physics-informed Learning workshop in Helsinki on June 28, which is a part of the ELISE Wrap Up Conference & ELLIS Community Event .
• June 2024: I am giving a talk on "Gradients of functions of large matrices" at the Pioneer centre in Copenhagen on June 7.
• May 2024: Our preprint on "Gradients of functions of large matrices" entered arXiv. Here is a link.
• May 2024: I reactivate this News section because I have not been using Twitter/X much recently.