Piyush Karande
About Piyush Karande
Piyush Karande is a Staff Research Engineer at Lawrence Livermore National Laboratory, specializing in signal processing and machine learning techniques for neural data interpretation. He holds a PhD in Biomedical Engineering from Washington University in St. Louis and has extensive experience in developing algorithms for Brain-Computer Interfaces.
Work at Lawrence Livermore National Laboratory
Piyush Karande has been employed at Lawrence Livermore National Laboratory since 2020, serving as a Staff Research Engineer. His role involves applying his expertise in signal processing and machine learning to various research projects. Prior to his current position, he worked at the same laboratory as a Postdoctoral Research Staff Member from 2017 to 2020. His contributions during this time focused on advanced research methodologies and the development of algorithms.
Education and Expertise
Piyush Karande holds a Bachelor's degree in Electronics and Communications Engineering from Birla Institute of Technology, completed between 2005 and 2009. He furthered his education at Washington University in St. Louis, where he earned a PhD in Biomedical Engineering from 2009 to 2016. His studies emphasized signal processing, logic and integrated circuit design, and microprocessors. Additionally, he completed a Nanodegree in Self Driving Car Engineering from Udacity in 2017.
Background
Piyush Karande began his academic journey at Birla Institute of Technology, where he developed foundational knowledge in engineering principles. He transitioned to Washington University in St. Louis, where he not only completed his PhD but also served in various roles, including Graduate Research Assistant and Postdoctoral Researcher. His work in these positions spanned over seven years, during which he gained significant experience in research and teaching.
Achievements
During his PhD research, Piyush Karande developed robust algorithms for Brain-Computer Interfaces, utilizing minimally invasive neural recording modalities. His specialization in signal processing and novel machine learning techniques has enabled him to interpret neural data in real time. This work has implications for advancements in neuroscience and technology, particularly in the field of brain-computer interaction.