Aditya Peshin
About Aditya Peshin
Aditya Peshin is a Machine Learning Engineer at Klaviyo, specializing in recommendation systems. He has a background in data science and engineering, with experience from various institutions including the University of California, Berkeley, and IIT Roorkee.
Work at Klaviyo
Aditya Peshin serves as a Machine Learning Engineer at Klaviyo since 2021. In this role, he specializes in developing recommendation systems, leveraging his expertise in machine learning to enhance customer experiences. His work contributes to Klaviyo's mission of providing powerful marketing automation solutions.
Education and Expertise
Aditya Peshin holds a Master's of Engineering in Industrial Engineering and Operations Research from the University of California, Berkeley, which he completed in 2020. He also earned a Bachelor of Technology (BTech) in Production and Industrial Engineering from the Indian Institute of Technology, Roorkee, graduating in 2019. His academic background provides a strong foundation in data analysis and operational efficiency.
Background
Aditya Peshin has a diverse background in machine learning and data science. He began his career as an Undergraduate Student at the Indian Institute of Technology, Roorkee, where he also engaged in research as an Undergraduate Researcher. He gained practical experience through various internships, including roles at Schneider Electric and the Industrial Design Centre at IIT Bombay.
Previous Experience
Before joining Klaviyo, Aditya worked as a Data Science Intern at Skillful.ly for eight months, where he applied data science techniques to real-world problems. He also participated in a capstone project at the University of California, Berkeley, focusing on building machine learning models with limited data. His editorial experience includes serving as Chief Editor and Editor at Watch Out, IIT Roorkee.
Research and Interests
Aditya Peshin has conducted research in the field of active learning, contributing to advancements in machine learning methodologies. He is open to discussing machine learning research and technology, indicating a commitment to collaboration and knowledge sharing within the field.