David Peleg
About David Peleg
David Peleg is a Deep Learning Engineer at AUI™ in Tel Aviv District, Israel, specializing in Graph Neural Networks, Reinforcement Learning, and Time Series analysis. He has a background in Computer Science and Physics from Bar-Ilan University and has held various roles in data science and engineering at notable companies including Intel and IBM.
Work at Augmented Intelligence
David Peleg has been employed as a Deep Learning Engineer at AUI™ (Augmented Intelligence) since 2023. He works in the Tel Aviv District of Israel, contributing to projects that leverage advanced machine learning techniques. His role involves applying his expertise in deep learning to develop innovative solutions within the organization.
Education and Expertise
David Peleg holds a Bachelor of Science in Computer Science and a Bachelor of Science in Physics, both obtained from Bar-Ilan University between 2016 and 2019. He further pursued a Master of Science in Theoretical and Mathematical Physics from the same institution, completing it in 2021. His academic background is complemented by extensive expertise in Graph Neural Networks, Reinforcement Learning, and Time Series analysis.
Background in Data Science
Prior to his current role, David Peleg worked at Intel Corporation as a Data Science Researcher from 2022 to 2023 and as a Data Science Intern from 2021 to 2022. His experience at Intel involved applying data science methodologies to real-world problems. Additionally, he worked at IBM as a QA Automation Engineer from 2018 to 2019, where he gained experience in quality assurance processes.
Teaching Experience
David Peleg served as a Teaching Assistant at Bar-Ilan University from 2020 to 2021. In this role, he supported faculty in delivering course material and assisted students in understanding complex concepts. His experience in academia has contributed to his ability to communicate technical information effectively.
Professional Attributes
David Peleg is recognized for his social skills, curiosity, and ability to learn quickly. These attributes enhance his contributions to machine learning projects, allowing him to develop innovative solutions and collaborate effectively with colleagues.