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Robrecht Cannoodt

Data Science Consultant

Data Intuitive

Biography

Hello, my name is Robrecht Cannoodt. I just obtained by PhD as a computer scientist specialising in data science (dissertation). I work as a data science consultant at Data Intuitive and as a postdoctoral researcher at VIB - Ghent University.

I enjoy developing and contributing to data science software projects (See Software). In particular: developing or speeding up algorithms; adding functionality to existing software; and contributing to open-source projects. During my PhD, I developed tools for analysing single-cell omics data, and tools for benchmarking single-cell omics tools. However, many of the software packages I (co-)developed can be used for general-purpose data mining applications.

Interests

  • Computer Science
  • Machine Learning
  • Software Engineering
  • Bioinformatics
  • Single-Cell Omics

Education

  • PhD in Computer Science, December 2019

    Ghent University

  • MEng in Computer Science, 2013

    Ghent University

  • BSc in Informatics, 2011

    Ghent University

  • International Baccalaureate, 2007

    International School of Berne

Recent Posts

Posts related to the R will be contributed to the R-bloggers.com community.

lmds: Landmark Multi-Dimensional Scaling

A fast dimensionality reduction method scaleable to large numbers of samples.

Recent Publications

The Rockerverse: Packages and Applications for Containerization with R

A survey of projects building upon Rocker and presents the current state of R packages for managing Docker images and controlling …

The RNA Atlas, a single nucleotide resolution map of the human transcriptome

A comprehensive atlas of the human transcriptome that is derived from matching polyA-, total-, and small-RNA profiles of a …

Essential guidelines for computational method benchmarking

A summary of key practical guidelines and recommendations for performing high-quality benchmarking analyses.

Trajectory-based differential expression analysis for single-cell sequencing data

A powerful framework for inferring within-lineage and between-lineage differential expression.

A comparison of single-cell trajectory inference methods

Unsure which trajectory inference method to use for your single-cell dataset? Our benchmarking effort sheds light on this matter.

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