Biography
I work on end-to-end data and AI systems, from large-scale data acquisition and processing pipelines to intelligent applications in healthcare and finance.
My work combines machine learning, natural language processing, infrastructure engineering, and product development, with experience spanning clinical data platforms, document intelligence, mobile applications, and research systems. I have developed NLP pipelines for extracting structured information from electronic health records, designed document processing and validation systems for financial workflows, and built applications integrating backend services, data pipelines, and user-facing interfaces.
I am the founder of Leutaz Lab, where I develop applied AI products and systems for healthcare and finance, particularly around document intelligence, workflow automation, and therapeutic follow-up applications.
Previously, I worked at AP-HP / BNDMR, Beth Israel Deaconess Medical Center, and Boston Children’s Hospital on projects involving clinical NLP, neuroimaging, machine learning, and data infrastructure. I studied statistics and data science at the University of Michigan.
Download my resumé.
- Applied AI Systems
- NLP & Document Intelligence
- Healthcare and Financial Data
Master of Science in Biostatistics, 2017
University of Michigan
Bachelor of Art in Biological Sciences, 2014
Connecticut College
Skills
Experience
- Developed OCR and NLP pipelines for automated extraction and validation of financial and wealth management documents.
- Designed reliability scoring and document validation systems for financial workflows.
- Built mobile and backend applications for therapeutic follow-up and rehabilitation support.
- Developed end-to-end data pipelines and infrastructure for AI-driven applications in healthcare and finance.
Responsibilities include:
Building machine learning algorithms for automatic extraction of clinical information from rare diseases patients’ electronic health records.
Conducting epidemiological studies on rare disease patients
Developing tools to anonymize nominative patient data and building a widely accessible desindentified research rare disease registry usable by all researchers.
Responsibilities include:
Analyzed Magnetic Resonance Imaging (MRI) data using statistics, machine learning, and deep learning to understand the effects of brain lesions on brain function.
Planned and performed the statistical analysis for multiple published research projects.
Created a web platform to analyze MRI data enabling the medical doctors and researchers to use state of the art quantitative methods.
Maintained and developed the lab computational infrastructures using sys admin and programming tools.
Responsibilities include:
Assisted research work on identifying the neurological basis of autism using MRIs data using statistics and machine learning.
Created data preprocessing pipelines to build normative datasets from publicly available data using the computational radiology lab infrastructures.
Built Dockers and singularity containers to deploy lab software solutions on BCH, Harvard Medical School, and Harvard Faculty of Art and Science high-performance clusters.
Responsibilities include:
Created an R data package on different cancers’ risks and incidences using data from published literature on patients with genetic risks.
Developed a web-based interface using R shiny and a search engine to navigate the database.
Contributed to an exhaustive literature review of lifetime risk of genetic inheritance to cancer.
Recent Publications
Contact
- louisasoussand@gmail.com
- Paris, France