About me
I am a postdoctoral researcher at Ghent University, working under the supervision of Prof. Lieven Clement in the statOmics group. My research currently focuses on the analysis of mass spectrometry-based single-cell proteomics.
I have a background in biomedical sciences and in bioinformatics, which I combined during a PhD in Prof. Laurent Gatto’s lab. During this academic track, I contributed several articles in scientific journals, but I must admit that my biggest scientific contribution are the software tools that I develop and maintain. My goal is to use the knowledge and skills that developed to solve real-life problem and help improve society.
I am a happy husband and a happy father of two boys. Strangely enough, my high-tech and innovation-driven career is counter-balanced by a low-tech and back-to-nature lifestyle at home. I am really passionate about growing my own food, with a fascination for agro-ecology that aims to produce food while having a positive environmental impact (from an ecological point of view). Still, a scientific mindset is what drives me day after day.
You are very welcome to get in touch with me in English, French, or Dutch through any of the channels listed at the bottom of this website.
(checkout my one-page resume here)
Career path
My Biomedical Science background allowed me to gain insights in the intricate biological processes involved in human physiology and disease. During my first master’s thesis in the lab of Prof. Sophie Lucas (UCLouvain), my interest grew towards bioinformatics as my project on protein structure modelling involved advanced data analysis. However, the frustration associated with my lack of computational background led me to pursue a second master’s in Bioinformatics. I developed a fascination for single-cell technologies through an integrated project with Prof. Rob Jelier (KULeuven) where I analyzed a C.elegans scRNA-seq data set. In parallel, I conducted my second master’s thesis with Prof. Tom Wenseleers (KULeuven) where I processed mass spectrometry (MS) data to characterize the chemical composition of over 200 Belgian beers. Tom hired me for another year, during which I met Prof. Laurent Gatto who offered me a PhD position to work on single-cell proteomics (SCP), effectively combining my passions for single-cell and MS-based technologies.
During my PhD, we quickly realized that the emerging field of SCP was
lacking computational standards. We, therefore, developed the
scp
software that provides standardized SCP data storage and manipulation
and the
scpdata
software that provides curated SCP data sets. With our tools, we could
replicate current workflows, offering a bird’s-eye view on current
practices and highlighting bottlenecks of SCP data analysis. These
efforts positioned us as experts in the field, which we marked by
contributing to a pioneering paper on initial recommendations for SCP
experiments, lead by Prof. Nikolai Slavov. Finally, we developed a
novel approach that lays the foundation to the analysis of SCP data.
I foresee a prosperous future for SCP and aspire to play a pivotal role in the adoption of SCP in biomedical research. However, the current computational developments need to be extended beyond proof-of-concept experiments to address the challenges associated with biological replication. Therefore, I turned to Prof. Lieven Clement and his statOmics lab who is internationally renowned for developing comprehensive statistical methods for single-cell transcriptomics data and proteomics data. Moreover, the lab has a close connection with the VIB-UGent proteomics core facility, a Belgian leader in proteomic-based biomedical and clinical research. This represents an ideal work environment for building upon my skills in setting up data analysis infrastructure for SCP and expanding these towards developing novel statistical methods and tools that exploit SCP data to their full potential, as well as to further develop myself towards an independent academic researcher in proteomics research.
I hope to ultimately apply the skills and knowledge I will have developed to solve real-life problems and concretely contribute to improving patient’s life, either through developing novel therapeutics or boosting prevention.
Resume
