I implemented a Python tool to estimate the uncertainty of dimensionality reduction on population genetics datasets.

The tool is available on GitHub (WIP).

The respective preprint publication will be available soon.

Dimensionality reduction techniques, for example Principal Components Analysis (PCA) or Multidimensional scaling (MDS) are frequently used in population genetics and ancient DNA studies. To the best of my knowledge, there exists no standard in either field to report an estimate of the uncertainty or instability of such methods. Without such an estimate, the accuracy of a conclusion based on dimensionality reduction cannot be assessed by the reader of the study. In my current work, I'm implementing the Python framework Pandora that is able to provide such an uncertainty for a given genotype dataset based on bootstrapping. Pandora supports stability analyses for PCA and MDS analyses. In addition to an overall stability of the dataset, Pandora can also compute the stability of subsequent K-Means clustering analysis, as well as bootstrap support values for all samples in the input dataset.

The tool is unit-tested and I set up CI using GitHub actions.

We provide empirical evidence that current state-of-the-art models of sequence evolution cannot reproduce empirical-like evolutionary data.

The publication is available as preprint in BioRxiv.

Simulating sequence evolution plays an important role in the development and evaluation of phylogenetic inference tools. Naturally, the simulated data needs to be as realistic as possible to be indicative of the performance of the developed tools on empirical data. Over the years, numerous phylogenetic sequence simulators, employing various models of evolution, have been published with the goal to simulate such empirical-like data. In this study, we simulated DNA and protein Multiple Sequence Alignments (MSAs) under increasingly complex models of evolution with and without insertion/deletion (indel) events using a state-of-the-art sequence simulator. We assessed their realism by quantifying how well supervised learning methods are able to predict whether a given MSA is simulated or empirical.

Our results show that we can distinguish between empirical and simulated MSAs with high accuracy using two distinct and independently developed classification approaches across all tested models of sequence evolution. Our findings suggest that the current state-of-the-art models fail to accurately replicate the process of evolution.

This work is a joint work with my colleagues at HITS and a team of researchers in Lyon. Johanna Trost, Dimitri Höhler, and I contributed all equally to this work.

I implemented a Python library for predicting the difficulty of multiple sequence alignments in phylogenetics.

PyPythia is available on GitHub.

The peer-reviewed publication is available in Molecular Biology and Evolution.

Phylogenetic analyses under the Maximum Likelihood model are time and resource intensive. To adequately capture the vastness of tree space, one needs to infer multiple independent trees. On some datasets, multiple tree inferences converge to similar tree topologies, on others to multiple, topologically highly distinct yet statistically indistinguishable topologies. At present, no method exists to quantify and predict this behavior. We introduce a method to quantify the degree of difficulty for analyzing a dataset and present Pythia, a Random Forest Regressor that accurately predicts this difficulty. Pythia predicts the degree of difficulty of analyzing a dataset prior to initiating Maximum Likelihood based tree inferences. The prediction and computation of all required attributes is substantially faster than a single Maximum Likelihood tree inference. Pythia can be used to increase user awareness with respect to the amount of signal and uncertainty to be expected in phylogenetic analyses, and hence inform an appropriate (post-)analysis setup. Further, it can be used to select appropriate search algorithms for easy-, intermediate-, and hard-to-analyze datasets.

In the current version of Pythia we replaced the Random Forest Regressor with boosted trees implemented in LightGBM.

PyPythia is unit-tested and I set up CI using GitHub Actions.

Pythia is available as open source software libraries in C (CPythia) and Python (PyPythia).

CPythia is also available on GitHub. CPythia can only be used in conjunction with Coraxlib.

I performed large scale numerical analysis of the influence of threshold parameters on the runtime and results of Maximum Likelihood phylogenetic inferences.

The peer-reviewed publication is available in Bioinformatics Advances.

Maximum Likelihood (ML) is a widely used phylogenetic inference model. ML implementations heavily rely on numerical optimization routines that use internal numerical thresholds to determine convergence. We systematically analyze the impact of these threshold settings on the log-likelihood and runtimes for ML tree inferences with RAxML-NG, IQ-TREE, and FastTree on empirical datasets. We provide empirical evidence that we can substantially accelerate tree inferences with RAxML-NG and IQ-TREE by changing the default values of two such numerical thresholds. At the same time, altering these settings does not significantly impact the quality of the inferred trees. We further show that increasing both thresholds accelerates the RAxML-NG bootstrap without influencing the resulting support values. For RAxML-NG, increasing the likelihood thresholds ϵ_LnL and ϵ_brlen to 10 and 10³ respectively results in an average tree inference speedup of 1.9 ± 0.6 on Data collection 1, 1.8 ± 1.1 on Data collection 2, and 1.9 ± 0.8 on Data collection 2 for the RAxML-NG bootstrap. Increasing the likelihood threshold ϵ_LnL to 10 in IQ-TREE results in an average tree inference speedup of 1.3 ± 0.4 on Data collection 1 and 1.3 ± 0.9 on Data collection 2.

Our research comprises four studies, I conducted Study 1 during my Master's thesis with the CME group at HITS. You can find the full thesis here

I implemented a private web-based diary to keep track of vacations.

The source code is available on GitHub.

To remember vacations, holidays and trips I implemented a diary website using the django web framework. This website also stores images associated with certain trips and image descriptions, so I can show the best images to friends and family. It also includes a map, so I can see where on this beautiful earth I have already travelled to ☺︎ In its latest version, I can also upload files, e.g. GPS tracks of hikes.

I implemented a web-based tool to organize courses for the computer science master's degree at KIT.

The source code is available on GitHub.

For my master’s studies at KIT I implemented a web-based tool to organize the courses I intend to take. The tool checks if the planned courses satisfy all requirements to get the master’s degree. It further shows a schedule of exam dates, as well as an overview of all grades. Since the computation of the final grade at KIT is a bit tedious, I also implemented an automatic computation of the final grade, as well as the average grades per module.

I implemented a markerless patient movement tracking algorithm for cochlear implant surgeries.

The source code is available on GitHub.

The peer-reviewed paper is available Frontiers in Surgery.

As my bachelor's thesis, at the Intelligent Process Automation and Robotics Lab (IPR) at KIT, I implemented a system for markerless patient movement tracking during cochlear implant surgeries. The approach uses only the images obtained by the microscopic camera used by the surgeon. I implemented the algorithm using Python and the image processing frameworks OpenCV and scikit-image. It also includes a neural network for semantic image segmentation. My bachelor's thesis is available upon request only as it contains sensitive patient images.

I implemented a website for storing and sharing recipes.

The source code is available on GitHub.

This project was inspired by my love for baking. It is meant for storing all the ideas and recipes of me and my friends. Different users can create new recipes, categorize them or search for a specific recipe. The website also includes functionality to filter recipes by ingredients and categories.

This website is an ongoing project and I keep adding new features. The latest feature I added was a shopping list that is especially useful when planning to bake multiple different recipes.

We implemented a web-based Lambda Calculus IDE and interpreter for the programming paradigm lecture at KIT.

The source code is available here.

As part of the undergraduate studies, we implemented a browser-based lambda calculus IDE and interpreter in a team of six students. We used Java with the Google Web Toolkit, JavaScript and CSS and wrote about 10 000 lines of code plus 6000 lines of tests. The project was graded with mark 1.0 (best possible grade). I mostly implemented the interactive frontend to explore the lambda terms and their reduction steps. Also, I wrote lots of tests for the controller layer (MVC architecture).