Hana Sebia ☕️

Hana Sebia

PhD Candidate in Machine Learning

Inria, AIStroSight, Centre de Lyon, France

Ph.D. candidate at the French research institute Inria. I am working on machine learning for high-dimensional healthcare data analytics. My research interests include computational phenotyping and predictive analysis for large-scale electronic health records (EHR) data. I obtained the master degree in Data Science from the computer science department of Claude Bernard Lyon 1 University.

Research Topics

Machine Learning
Deep Learning
Tensor Decomposition
Healthcare Data Analytics
Computational Phenotyping
Predictive Analysis


Ph.D. Position
November 2022 – Present Lyon, France
Research Internship
March 2022 – August 2022 Lyon, France
The state-of-the-art tensor decomposition methods are limited to the extraction of phenotypes that only describe a combination of correlated features occurring the same day. During my internship, I introduced temporal phenotyping to extract phenotypes describing expressive sequential patterns.
Healthcare Data Analyst
June 2020 – September 2021 Lyon, France

KANOPEE: An application offering clinical identification and advice by a virtual companion to limit sleep problems and addictive behaviors, early markers of anxiety, stress and depression linked to the COVID-19 crisis.

QUALITOP: European project aiming to develop a smart digital platform using big data analysis to monitor health status and quality of life of cancer patients given immunotherapy.


An application to perform basic and advanced matrix calculations as well as operations related to matrix decomposition (under Linux and OSX).
The Fatser Scrabble Move generation Algorithm
A simulation of a scrabble party based on the faster algorithm for generating every possible move given a set of tiles and a position
The Top Down Query Evaluation Engine for DataLog
A Top down recursive query processing method for DataLog, implemented in Java.