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

Experience

 
 
 
 
 
Inria
Ph.D. Position
November 2022 – Present Lyon, France
 
 
 
 
 
Inria
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.
 
 
 
 
 
LIRIS
Healthcare Data Analyst
LIRIS
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.

Projects

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MatriXMIX
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.

Contact