I am skilled at solving analytical problems using a combination of statistical and computational models. I draw on my expertise in statistics, machine learning, and domain-specific knowledge to deliver targeted solutions to complex challenges.
I am a graduate of McGill University, where I earned a PhD in Neuroscience. During my time in this program, I gained extensive experience in software development, and had the opportunity to create and test novel hypotheses in a fast-paced scientific field.
In my role as a scientific software developer, I enhance the workflows of researchers and clinical staff at the Child Mind Institute. I specialize in bringing AI tooling to the end-user.
As a software engineer at Ordina, I am dedicated to delivering high-quality Python solutions that bring value to our customers. In my role, I work closely with clients to understand their needs and develop custom solutions that meet their unique challenges and goals.
During my PhD, I focused on the application of statistical methods and machine learning to improve our understanding of the human brain. This work resulted in scientific advances in the field, including new insights into the connections of the temporal lobe, as well as the development of open-source toolboxes that enable other researchers to use advanced statistical and manifold learning methods.
Skills: Python • FastAPI • PyTorch • Docker • mySQL • ElasticSearch • S3 • RabbitMQ
The Dutch police have a substantial amount of audio data from criminal investigations. However, identifying unknown speakers in these files requires significant manual effort. To tackle this issue, I am collaborating with the Vox team at Ordina to develop a Minimum Viable Product AI solution that can automatically identify speakers in these files. Our work involves creating a scalable web application that enables non-experts to generate and compare vocal "fingerprints" extracted from recordings.
Skills: Typescript • HTML • CSS • React
Well... You're looking at it :-).
Skills: NumPy • Git • Manifold Learning • Non-Parametric Statistics
As part of my PhD studies I developed and maintained BrainSpace, a toolbox for manifold learning of brain connectivity data and the statistical analaysis of these manifolds. This toolbox allowed researchers without significant programming experience to perform advanced statistical procedures. This toolbox has been used in hundreds of scientific papers by researchers around the world.
I was enrolled in the C-track (cognition) of this Master's program. Part of my training was in the Hyder lab at Yale University. This work led to a scientific publication on the effects of various statistical corrections on MRI signals.
My studies at the University of Utrecht focussed on neuroscience and molecular cell biology. My Bachelor's thesis was written on which nerves in the human eye process cues from which areas of the visual field.
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If you would like to get in touch, I am happy to have a quick chat about anything and everything. You can reach me via LinkedIn and email (see the links in the footer), but you will likely receive the quickest response via email. For inquiries related to my work at Child Mind Institute, please contact me at my Child Mind Institute e-mail address