All the research projects I have undertaken have involved some amount of software development. I view publishing implementations of models and algorithms as an integral part of the reproducible research process. My languages of choice are R, Stan, python and C++.


DeLorean is an R package I developed to order single cells in pseudotime. The latent variable Gaussian process model is coded and inference is performed using the Stan probabilistic programming language and library.


STEME is a C++ implementation of a branch-and-bound algorithm I developed to speed up the Expectation-Maximisation algorithm for finding motifs in biological sequences. It makes heavy use of the SeqAn library for sequence analysis. In particular most of the computational savings result from the use of suffix trees.


pybool is a python package that infers Boolean regulatory networks given time course expression data under perturbations and a set of temporal constraints on expression. Prior knowledge about the presence or absence of regulatory connections can be incorporated into the inference. Inference is performed in parallel allowing larger networks to be inferred on clusters.


HMM is a C++ library with a python interface that relies on templated generic programming techniques to efficiently implement the Baum-Welch and Viterbi algorithms for hidden Markov models. Markov models of any (reasonable) order are supported.


infpy is a python library that implements various machine learning inference algorithms and models. In particular it has an implementation of the collapsed variational Bayes inference scheme for the hierarchical Dirichlet process model used in my transcriptional programs work. It also contains a python implementation of Gaussian processes. The implementation focusses on ease of composition of kernels and MAP inference of kernel parameters.


biopsy is a C++ library with a python interface that implements the phylogenetic transcription factor binding scanning algorithms developed in my thesis. It contains a templated generic implementation of the maximal chain algorithm that is integral to this work.


pyicl is a python interface to the Boost interval container library. From the library’s documentation:

Intervals are almost ubiquitous in software development. Yet they are very easily coded into user defined classes by a pair of numbers so they are only implicitly used most of the time. The meaning of an interval is simple. They represent all the elements between their lower and upper bound and thus a set. But unlike sets, intervals usually can not be added to a single new interval. If you want to add intervals to a collection of intervals that does still represent a set, you arrive at the idea of interval_sets provided by this library.

One of the most advantageous aspects of interval containers is their very compact representation of sets and maps. Working with sets and maps of elements can be very inefficient, if in a given problem domain, elements are typically occurring in contiguous chunks. Besides a compact representation of associative containers, that can reduce the cost of space and time drastically, the ICL comes with a universal mechanism of aggregation, that allows to combine associated values in meaningful ways when intervals overlap on insertion.