* Developed and deployed deep learning and CV algorithms for ADAS applications, including object detection and lane segmentation. Worked on custom and unbalanced datasets using transfer learning techniques.

* Optimized neural networks for Qualcomm, achieving a 50% increase in FPS speed without sacrificing accuracy. This was accomplished by optimizing more than 15 models of various kinds using AIMET tools and TensorRT.

* Designed and optimized machine learning–based signal processing solutions to improve radar beamforming efficiency and lobe generation speed.

* Devised and implemented SLAM on autonomous robots with ROS, deftly integrating IMU, Lidar, Camera, and encoders. Seamlessly mapping, localizing, and navigating while performing precise sensor fusion for environment-aware robot tracking.

* Researched and implemented multi-integrated systems, such as navigation with speech, robotic arm with vision capabilities, and published 4 research papers in top conferences.

* Designed and created a home service robot as an consolidated system that performs auto-navigation, speech recognition, detections and recognitions, and pick and place with a robotic arm. Took the robot to participate in RoboCup@Home Germany, 2019 and was selected for World Robot Summit Finals at Japan in 2020.