About

Jyothirmayee Donthineni is a highly skilled software engineer with a strong background in machine learning. She is currently interning at NASA's Jet Propulsion Laboratory, where she is applying machine learning algorithms to ground-penetrating radar images taken from a network of autonomously cooperating robots. Her work involves autonomously identifying and characterizing significant identifiers or discriminators in model data, real-world data from controlled field experiments, and finally real-world data obtained from the surface of the moon. Jyothirmayee has 2.28 years of relevant experience, having previously worked as a software system designer at AMD. She is proficient in data structures and algorithms, image and signal processing concepts and applications, and has expertise using MATLAB and Python libraries such as OpenCV, Python Imaging Library, and Matplotlib. She is also experienced in working with machine learning models and deep neural networks, with a strong foundation in linear algebra, probability, and statistics. Jyothirmayee is currently seeking software internships for Summer 2023. She is a graduate teaching assistant at the Paul G. Allen School of Computer Science & Engineering, where she teaches the course EEP 522: Embedded and Real-time Systems. She has also worked as a grader for the Digital Imaging Systems course (EE 440) by Prof. Ming-Ting Sun. Jyothirmayee holds a Bachelor of Technology in Electronics and Communications Engineering from the National Institute of Technology Karnataka and is currently pursuing a Master of Science in Computer Engineering from the University of Washington, which she will complete in December 2023. Her tech stack includes ML, designing, Python, OpenCV, and neural networks.