Apaar Shanker
About Apaar Shanker
Apaar Shanker is a Machine Learning Research Engineer currently employed at Scale AI in San Francisco, California. He holds a Ph.D. in Computational Science and Engineering from Georgia Institute of Technology and has extensive experience in machine learning and computational modeling.
Current Role at ScaleAI
Apaar Shanker currently serves as a Machine Learning Research Engineer at Scale AI, a position he has held since 2022. He is based in San Francisco, California, United States. His role involves applying machine learning techniques to enhance the capabilities of AI systems, contributing to the company's mission of improving data annotation and machine learning workflows.
Previous Experience at ScaleAI
Before his current role, Apaar Shanker worked at Scale AI as a Machine Learning Research Engineer for three months in 2021. During this time, he focused on developing and implementing machine learning models to support various projects within the organization, leveraging his expertise in computational science.
Education and Expertise
Apaar Shanker completed his Bachelor of Science and Master of Science degrees at the Indian Institute of Science (IISc) from 2011 to 2016. He later pursued a Master of Science in Computational Science and Engineering at the College of Computing at Georgia Tech from 2016 to 2021. He is currently a Ph.D. candidate at Georgia Institute of Technology, specializing in Machine Learning and Computational Modeling.
Professional Background
Apaar Shanker has a diverse professional background, having worked in various research and quantitative roles. He served as an International Research Associate at the National Institute of Standards and Technology (NIST) in 2017 and 2018. He also held a position as a Global Markets Quantitative Strategies Summer Associate at Bank of America Merrill Lynch in 2019. Additionally, he interned at GE Power & Water in 2014.
Skills and Technical Proficiency
Apaar Shanker possesses a strong skill set in statistical data analysis, Linux, Python, and deep learning tools. His interdisciplinary experience as a computational scientist enables him to tackle complex problems in machine learning and data science, making him a valuable asset in his field.