Sunita Gopal
About Sunita Gopal
Sunita Gopal is a Deep Learning Engineer currently employed at Flux Auto in Bengaluru, India. She has a background in machine learning and data science, with previous roles at aiConomix, Linde, and the Technical University of Munich.
Work at Flux Auto
Sunita Gopal currently holds the position of Deep Learning Engineer at Flux Auto. She has been with the company since 2022, contributing her expertise in deep learning technologies. Her role involves developing and implementing machine learning algorithms to enhance the company's automotive solutions. The work at Flux Auto aligns with her background in data science and machine learning.
Previous Experience in Data Science
Before joining Flux Auto, Sunita Gopal worked as a Data Scientist at aiConomix for five months in 2021. In this role, she focused on data analysis and model development, applying her skills to solve complex problems. Her experience at aiConomix provided her with valuable insights into the application of data science in various industries.
Education and Expertise
Sunita Gopal studied at Sir M Visvesvaraya Institute of Technology from 2013 to 2017, where she completed her undergraduate degree. She is currently pursuing a Master's degree in Informatics at the Technical University of Munich, a program she has been enrolled in since 2017. This educational background has equipped her with a solid foundation in machine learning and data analysis.
Background in Machine Learning
Sunita Gopal has a diverse background in machine learning, having worked in various roles related to this field. She served as a Working Student in Machine Learning at Linde for four months in 2019 and participated in research at the Technical University of Munich, where she completed her Master Thesis from 2019 to 2020. Additionally, she was involved with ROBOY at the Technical University of Munich as a student for four months in 2018, further enhancing her practical experience in robotics and machine learning.
Deep Learning Experience
Sunita Gopal gained significant experience in deep learning while working as a Deep Learning Working Student at Tarsier from 2019 to 2020 for eleven months. This role allowed her to apply deep learning techniques in real-world applications, solidifying her expertise in the field. Her cumulative experience in various roles has contributed to her current position as a Deep Learning Engineer.