Parv Parkhiya
About Parv Parkhiya
Parv Parkhiya is a Senior Perception Engineer at ISEE in Boston, Massachusetts, where he has worked since 2022. He has a background in robotics and engineering, with experience in simultaneous localization and mapping, and holds a Master of Science in Robotics System Development from Carnegie Mellon University.
Work at ISEE
Parv Parkhiya has been employed at ISEE as a Senior Perception Engineer since 2022. In this role, he focuses on developing advanced perception algorithms for autonomous systems. His work involves utilizing Lidar technology and other sensors to enhance the capabilities of robotic systems. Prior to his current position, he served as a Robotics Engineer at ISEE from 2020 to 2022, where he contributed to various projects aimed at improving robotic perception and navigation.
Previous Experience in Robotics
Before joining ISEE, Parv Parkhiya worked at ZENUITY for a brief period in 2019, specializing in Simultaneous Localization and Mapping (SLAM). His role involved developing algorithms that enable robots to understand their position in an environment. Additionally, he served as a Teaching Assistant at IIIT Hyderabad from 2016 to 2018, where he supported students in their learning of robotics and related subjects.
Education and Expertise
Parv Parkhiya holds a Master of Science degree in Robotics System Development from Carnegie Mellon University, which he completed from 2018 to 2020. He also earned a Bachelor's degree in Electronics and Communications Engineering from the International Institute of Information Technology, studying from 2014 to 2018. His academic background provides a strong foundation in robotics, Lidar simulation, and perception technologies.
Technical Contributions
In his professional career, Parv Parkhiya has developed significant technical solutions. He created a real-time Lidar-based mapping system on GPU as part of a 3D Occupancy Grid module. Additionally, he implemented a pointcloud auto labeling pipeline that utilizes self-supervised learning for detection and reconstruction tasks. His expertise includes ground segmentation techniques, optimization, and mesh fitting, which are critical for enhancing the performance of autonomous robotic systems.