Peng Chang

Peng Chang has a diverse range of work experience in the field of robotics and software engineering. In 2012, they worked as an Image Processing Intern at Xi'an Research Institute of Intelligent Perception and Image Understanding. From 2014 to 2016, they served as an MCU R&D System Engineer Intern at Hangzhou Silan Microelectronics Co., Ltd. Following that, Peng joined Northeastern University as a Student Ambassador for a brief period in 2016. Peng then took on the role of Teaching Assistant at Northeastern University from 2016 to 2020, assisting with a course that focused on scripting languages, high-level programming, and more. In 2017, they participated in the IAEA Robotics Challenge, where they investigated visual SLAM and object detection algorithms for inspection tasks with unmanned ground vehicles. From 2019 to 2020, Peng worked on the Panasonic Prototype 3D LiDAR Challenge, developing intelligent wheelchairs using 3D LiDAR technology. Peng later became a Robotics Engineer & Researcher at the Robotics and Intelligent Vehicles Research Laboratory, where they developed autonomous systems and designed manipulation methods for various tasks. Currently, Peng is a Senior Software Engineer, specializing in AI Perception, at Black Sesame Technologies Inc.

Peng Chang holds a Doctor of Philosophy (Ph.D.) degree in Electrical Engineering with a specialization in Robotics, which they obtained from Northeastern University. Peng pursued their Ph.D. between 2015 and 2021. Prior to that, Peng completed their Master of Science (M.S.) degree in Electrical and Computer Engineering from the same university from 2013 to 2015. Peng'seducational journey began at Xidian University, where they earned their Bachelor of Science (B.S.) degree in Electrical and Electronics Engineering from 2009 to 2013. In addition to their formal education, Peng Chang has obtained several certifications, including "Machine Learning with Python: Foundations" from LinkedIn in October 2022, "C++: Advanced Topics" from LinkedIn in August 2021, "Introduction to Deep Learning with OpenCV" from LinkedIn in August 2021, and "Machine Learning" from Coursera in July 2021.