I am a bridging postdoc at the EMBL Heidelberg in the group of Wolfgang Huber. In September 2023, I defended my PhD and am currently exploring opportunities for my time after EMBL.

I develop statistical methods and tools for the analysis of cutting edge biological data. I maintain ten R packages which are published on CRAN and Bioconductor and total more than 100,000 downloads per month. I have written multiple academic papers and you can find my full publication record on Google scholar. During my PhD, I worked on statistical developments for single-cell data (project 1, 2, and 3). Before that I worked on proteomics and clustering high-dimensional categorical data.

Download statistics of my R packages.

Remove the stroke around points to map the size argument accurately.

Find the standard deviation of $g(X)$

Visualization of test classification and underlying ground truth

Important properties and mathematical definitions

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#### transformGamPoi

#### einsum

#### glmGamPoi

#### sparseMatrixStats

#### proDA

#### ggupset

#### mixdir

#### tidygenomics

#### ggsignif

#### iGEM 2014

Variance stabilizing transformation for Gamma Poisson distributed data

Einstein Summation for Arrays in R

Fit Gamma-Poisson Generalized Linear Models Reliably

Implementation of the `matrixStats`

API for sparse matrices

R package for “Protein Differential Abundance Analysis for Label-Free Mass Spectrometry Data”

Plot a combination matrix instead of the standard x-axis and create UpSet plots with ggplot2.

R package for clustering high dimensional categorical data

Tidy Verbs for Dealing with Genomic Data Frames

A `ggplot2`

extension to add significance brackets

Student Competition for Synthetic Biology

Theoretical and empirical analysis of transformation methods for single-cell data.

A new method to analyze multi-condition single-cell data without clustering

Fit Gamma-Poisson Generalized Linear Models Reliably.

Decide which proteins are differentially abundant in label-free mass spectormetry without imputation.

Cluster high-dimensional categorical observations using an approximate Bayesian inference algorithm