Mai Said
About Mai Said
Mai Said serves as the Machine Learning Lead at Si-Ware Systems since 2021, bringing extensive experience in machine learning and engineering. She holds a PhD in Machine Learning - Chemometrics from Ain Shams University and has held various roles in academia and industry, including positions at IBM and Mentor Graphics.
Current Role at Si-Ware Systems
Mai Said serves as the Machine Learning Lead at Si-Ware Systems, a position she has held since 2021. In this role, she oversees machine learning projects and contributes to the development of innovative solutions. Her leadership is integral to advancing the company's capabilities in machine learning.
Previous Experience in Machine Learning
Prior to her current role, Mai Said worked at Si-Ware Systems as a Senior Machine Learning Engineer from 2019 to 2020 and as a Staff Machine Learning Engineer from 2020 to 2021. Her experience in these positions involved the application of machine learning techniques to various projects, enhancing the company's technological offerings.
Educational Background
Mai Said earned her Doctor of Philosophy (PhD) in Machine Learning - Chemometrics from Ain Shams University, completing her studies from 2018 to 2023. She also holds a Master's degree in Computer and Systems Engineering, specializing in GPU Acceleration of Bioinformatics Algorithms, which she obtained from 2013 to 2017. Her foundational education includes a Bachelor's degree in Computer and Systems Engineering from Ain Shams University, completed from 2007 to 2011.
Teaching and Research Experience
Since 2012, Mai Said has been a Research and Teaching Assistant at the Faculty of Engineering, Ain Shams University. In this capacity, she supports academic research and assists in teaching engineering courses, contributing to the educational development of students in her field.
Internship Experience
Mai Said gained early industry experience through internships at IBM and Mentor Graphics in 2009 and 2010, respectively. These internships provided her with practical exposure to engineering and technology, laying the groundwork for her future career in machine learning and engineering.