Jaswanth Bandlamudi

Jaswanth Bandlamudi has been working in the field of deep learning since 2015. Jaswanth began their career as a Senior Engineer at General Motors, where they were responsible for building system-level test benches with actual vehicle parts for HVAC system and software validation. In 2014, they joined Team Unblockabulls as an Electrical Engineer. In 2019, they joined Akka Technologies as a Work Student, where they assisted the team to develop automated test cases for the Verification and Validation of various software systems in EVO Bus using the PROVEtech TA tool. Jaswanth also worked in development along with client-side issue resolution in the Lane detection part of HMI software for Electric vehicles using C++ and Qt-based on Jetson TK1. In 2020, they joined Fraunhofer FKIE as a Student Research Assistant, where they implemented path planning algorithms for the disaster response robot based on ROSBOT2 and developed software for a web interface to control and display the perceived environment using Java and ihmc-communication libraries. In 2021, they joined the University of Bremen as a Studentische Hilfskraft, where they were responsible for the development of a deep learning-based hand pose estimation model using an RGB image. Jaswanth also implemented a network that predicts 2D key points and another network that regresses rotation and translation matrices to lift these key points into 3D space. Currently, they are working at RIIICO as a Deep Learning Engineer and is extending their work to implement an Inverse-kinematics model to predict the joint rotations based on the joint locations.

Jaswanth Bandlamudi obtained a Bachelor's degree in Electrical and Electronics Engineering from KL University between 2011 and 2015. Jaswanth then obtained a Black Belt Certification in Design for Six Sigma from General Motors in November 2017. Jaswanth is currently pursuing a Master's degree in Autonomous Systems at Bonn-Rhein-Sieg University of Applied Sciences, which they began in 2018 and is expected to complete in 2021.