Anirudh Patel
About Anirudh Patel
Anirudh Patel is a Machine Learning Researcher at Sandia National Laboratories, specializing in Deep Learning and contributing to various advanced research projects.
Current Role at Sandia National Laboratories
Anirudh Patel is currently a Machine Learning Researcher at Sandia National Laboratories, where he has been working since 2018. He serves as a Deep Learning Subject Matter Expert, contributing to various projects that focus on advanced techniques such as Deep Reinforcement Learning, Meta-Learning, Semantic Segmentation, and Object Detection. His role also includes organizing workshops and training sessions to promote the adoption of Deep Learning methodologies.
Previous Experience at Karlsruhe Institute of Technology
In 2017, Anirudh Patel worked as an Ultrasound Computer Tomography Research Assistant at the Karlsruhe Institute of Technology (KIT) for a duration of two months. During his time in the Karlsruhe Area, Germany, he focused on the application of ultrasound technology in computer tomography, acquiring practical experience in this specialized field.
Teaching Assistant at Stanford University
From 2017 to 2018, Anirudh Patel was a Digital Signal Processing Teaching Assistant at Stanford University. For one year, he assisted in teaching and mentoring students in Digital Signal Processing, reinforcing his own knowledge and expertise while contributing to the academic growth of his students.
Internships at Texas Instruments and DRN Data
Anirudh Patel gained early professional exposure through internships at Texas Instruments and DRN Data. In 2016, he worked as a Product Engineering Intern at Texas Instruments in the Dallas/Fort Worth Area for two months. Prior to this, he interned at DRN Data in 2014, also in the Dallas/Fort Worth Area, where he spent two months acquiring initial industry insights and skills.
Educational Background from Stanford University
Anirudh Patel holds a Master of Science (MS) degree in Electrical and Electronics Engineering from Stanford University, which he completed in 2018. He also earned a Bachelor of Science (BS) degree in Electrical, Electronics, and Communications Engineering from Stanford University, between 2013 and 2017. His academic background provided a strong foundation for his career in machine learning and deep learning research.