Yassir Bendou
About Yassir Bendou
Yassir Bendou is a Research Intern at Inria in Grenoble, France, specializing in unsupervised cross-domain adaptation techniques for medical localization tasks. He has previous experience as a Data Scientist at Amazon and has studied Computer Science and Engineering at IMT Atlantique.
Work at Inria
Yassir Bendou has been working at Inria as a Research Intern since 2021. His role involves focusing on advanced research in the field of computer vision and deep learning. Located in Grenoble, Auvergne-Rhône-Alpes, France, he has dedicated three years to exploring innovative solutions in machine learning. His current research emphasizes unsupervised cross-domain adaptation techniques, particularly for medical localization tasks.
Previous Experience at Amazon
Yassir Bendou worked at Amazon as a Data Scientist for six months in 2020, based in Luxembourg. Prior to this, he served as a Data Scientist Intern at Amazon from 2019 to 2020 for five months. His experience at Amazon involved applying data science methodologies to solve complex problems, contributing to the company's data-driven decision-making processes.
Education and Expertise
Yassir Bendou has a solid educational background in computer science and engineering. He studied at IMT Atlantique, where he completed a Graduate program in Computer Science and Engineering from 2017 to 2021. He also participated in an exchange semester at the National University of Singapore in 2018-2019. His early education includes two years at CPGE IBN TIMYA Marrakech, focusing on Maths, Physics, and Engineering Science.
Research Experience
Prior to his current position, Yassir Bendou held a Research Assistant position at IMT Atlantique in 2018, where he worked on human pose estimation for one month. During his research internship, he explored various approaches to localize both disc and vertebrate tasks across different imaging modalities, enhancing his expertise in medical imaging and machine learning.
Specialization in Machine Learning
Yassir Bendou specializes in unsupervised cross-domain adaptation techniques, particularly in the context of medical localization tasks. His research focuses on transferring knowledge between MRI and CT modalities, showcasing his commitment to advancing machine learning applications in healthcare. He has a strong interest in crafting innovative solutions for complex problems in computer vision and deep learning.