Shadi Naguib
About Shadi Naguib
Shadi Naguib is a Machine Learning Engineer currently employed at Ladder in New York since 2024. He has an extensive academic background in Robotics and Data Science, holding a Master of Science from EPFL and a Master's thesis from Harvard.
Work at Ladder
Shadi Naguib has been employed at Ladder as a Machine Learning Engineer since 2024. In this role, he contributes to the development and implementation of machine learning models and algorithms. His position is based in New York, United States, where he has been working for approximately seven months.
Education and Expertise
Shadi Naguib studied at Harvard John A. Paulson School of Engineering and Applied Sciences, focusing on Robotics. He completed his Master Thesis during his studies from 2022 to 2023. Additionally, he holds a Master of Science in Robotics Engineering and Data Science from EPFL, which he pursued from 2020 to 2023. His academic background also includes a Bachelor's degree in Microtechnical Engineering from EPFL, obtained from 2016 to 2020.
Professional Background
Before joining Ladder, Shadi Naguib worked at a Stealth Startup as a Machine Learning Engineer for three months in 2023. He also served as a Manufacturing Intern at ETML in 2018 and held the position of Undergraduate Research Assistant at École polytechnique fédérale de Lausanne from 2018 to 2019. His experience includes a role as a Visiting Researcher at Harvard from 2022 to 2023 and as an Agile Robots and Machine Learning Intern at ROVENSO in 2022.
Teaching and Leadership Experience
Shadi Naguib has experience in teaching and leadership roles. He worked as a Teaching Assistant at École polytechnique fédérale de Lausanne from 2019 to 2020. Furthermore, he held the position of Vice President of Software Development at TANOS.AI from 2019 to 2022, where he was involved in overseeing software development projects.
Early Education
Shadi Naguib completed his early education at Lycée Français du Caire, where he studied Basic Sciences and achieved the French Baccalaureate from 2013 to 2016. This foundational education provided him with a strong basis in scientific principles.