Machine Learning Engineer
Teradyne
North Reading, MA, United States
Laura Rojas is a Machine Learning Engineer at Teradyne, where she develops AI-driven solutions for semiconductor test challenges. She is dedicated to developing specialized approaches that combine expert systems with neural architectures to improve electronic design automation for semiconductor test, while addressing limitations such as small data and delivering the performance and efficiency required by the industry.
Alongside her work at Teradyne, Laura is pursuing a Ph.D. in Computer Engineering at Northeastern University. Her research interests span electronic design automation (EDA), efficient AI, model optimization, and machine learning for small-data environments. By combining academic research with industry application, she aims to advance methods that not only improve performance but also address the practical constraints of semiconductor testing.
She earned her Master of Science in Electrical and Computer Engineering from Northeastern University, with a concentration in Machine Learning, Computer Vision, and Algorithms. Previously, she worked at Intel Corporation as a Product Development Engineer, where she streamlined fuse processes for next-generation CPUs and validated early-stage processors. She also has prior research experience as a visiting researcher at Texas Tech University, contributing to studies on Raman spectroscopy and co-authoring publications in Nature Communications and PNAS.
At the Test Vision Symposium, she will present her work on applying machine learning to automate and optimize Device Interface Board (DIB) design, with the goal of reducing design complexity, improving efficiency, and enabling faster iteration cycles in semiconductor testing.
Optimizing Device Interface Board Design with Machine Learning for Analog and Digital Test Platforms
Wednesday, October 8, 2025
3:10pm - 5:00pm MT