Unlock the true potential of your data

At ScienceAble, we provide services and tools to extract the most value from your data. We engage to work with our customers to best fit their needs (personalized AI) while providing high level of accuracy and reproductibility (pipeline standardization).

OUR EXPERTISE

You have questions? We provide you with answers driven from evidence in data.

From data collection to data visualization, our services and expertise cover the entire spectrum of data science tailored and to your needs.

We help you formulate your questions, elaborate your experimental design, propose adequate methodology, build the tools you want to have, and write up all the communication you need.

Automated pipelines are good but can't answer all scientific questions.

Doing cutting edge science and making transformative discoveries always involve innovative experimental designs and specific analyses that will answer your scientific questions.

THE TEAM

Discover our Linked In and GitHub profiles

Sandrine Muller, Msc. Eng., PhD

ScienceAble Founder

Sandrine Muller is a highly accomplished data scientist with an comprehensive background in artificial intelligence (AI), genomics, and neuroimaging. With a Ph.D. in neurosciences (statistical framework for high dimensional neuroimaging data) and several years of postdoctoral research experience in bioinformatics, Sandrine has worked at world-renowned institutions such as Ecole Polytechnique Federale de Lausanne, Harvard Medical School/Masachusetts General Hospital, and the Broad Institute of MIT and Harvard. Her research has focused on a wide range of topics related to genomics, such as gene expression analysis, and biomarker discovery. Her expertise extends beyond genomics to various fields of AI, including signal processing, Bayesian statistics, predictive analytics, and experimental design.

Sandrine has also made significant contributions to the field of neuroimaging and neuroengineering, where she has developed feature selection techniques based on joint mutual information and Bayesian machine learning models to analyze highly dimensional datasets and extract meaningful insights to predict language recovery after stroke.

Moreover, Sandrine has practical experience in the industry (L'Oréal), having worked on projects related to cheminformatics and data science.