Soham Chatterjee

Soham Chatterjee

AI And Ml Lead @ Sleek

About Soham Chatterjee

Soham Chatterjee serves as the AI and ML Lead at Sleek in Singapore, where he has worked since 2022. With a background in electrical and electronics engineering, he has held various roles in machine learning and research across multiple organizations.

Work at Sleek

Soham Chatterjee has been serving as the AI and ML Lead at Sleek since 2022. In this role, he has mentored and coached a team of machine learning engineers, emphasizing skill development and goal achievement. Prior to his current position, he worked as a Machine Learning Engineer at Sleek from 2020 to 2022, contributing to various projects in the field of artificial intelligence and machine learning.

Education and Expertise

Soham Chatterjee holds a Bachelor of Technology in Electrical and Electronics Engineering from SRM University. He further advanced his education by obtaining a Master of Engineering in Electrical and Electronics Engineering from Nanyang Technological University in Singapore. His academic focus included research on edge computing and neuromorphic hardware, particularly in designing chips for deep learning models that prioritize low power consumption and high performance.

Background

Before joining Sleek, Soham Chatterjee gained diverse experience in various roles. He worked at Saama as an Associate and later as an Independent Consultant. His earlier roles included positions as a Course Instructor at Udacity, where he taught optimization tools and techniques, and as a Research Intern at Texas Instruments, CESC Ltd., and the Indian Statistical Institute. He also served as a Researcher and Board Member at Next Tech Lab.

Achievements

During his tenure at Udacity, Soham Chatterjee impacted over 1,200 students by teaching optimization tools and techniques as part of the AI for Edge IoT nanodegree program. Additionally, he developed techniques to protect neuromorphic hardware chips from side channel attacks, showcasing his contributions to the field of hardware security and machine learning.

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