Prerak Srivastava
About Prerak Srivastava
Prerak Srivastava is a PhD student and researcher based in Nancy, France, specializing in audio signal processing and acoustics. His work involves applying Bayesian statistics and machine learning techniques to enhance audio augmented reality.
Current Role at Inria
Prerak Srivastava is currently working at Inria as a Researcher PhD Student since 2020. His research is based in Nancy, France, where he focuses on advancements in audio augmented reality. He utilizes Bayesian statistics and machine learning techniques in his work, particularly in audio signal processing and acoustics research.
Previous Experience at Inria
Before his current role, Prerak Srivastava worked at Inria as a Student Researcher from 2018 to 2019 for seven months. He also completed a Summer Research Internship at Inria in 2019 for three months. Both positions were located in the Nancy Area, France, where he gained valuable experience in research methodologies and project development.
Internship Experience
Prerak Srivastava has held several internships that contributed to his professional development. He worked as a Research Intern at Orange in 2020 for six months in the Rennes Area, France. Additionally, he served as a Technical Intern at Cadence Design Systems in 2017 for three months in Noida, India. These internships provided him with practical experience in the tech industry.
Educational Background
Prerak Srivastava completed his Bachelor of Technology in Electrical, Electronics and Communications Engineering at Rajasthan Technical University from 2014 to 2018. He furthered his education by obtaining a Master's degree in Computer Science with a specialization in Machine Learning, Deep Learning, and Natural Language Processing from Université de Lorraine from 2018 to 2020.
Research Focus
Prerak Srivastava's research primarily focuses on estimating acoustic parameters using multi-channel audio signals. His work aims to enhance virtual sound rendering in audio augmented reality applications. This research is significant in the context of improving user experiences in immersive audio environments.