Yang Song

Yang Song has worked as a Data Scientist at Onto Innovation since February 2022. In this role, they have collaborated with research scientists, software developers, and application scientists to develop and test advanced data analysis and machine learning algorithms. They successfully evaluated a new machine learning algorithm for a product and introduced innovative evaluation techniques using Python modules. They also conducted literature reviews, analyzed existing indicator methods, and explored novel techniques to improve accuracy.

Prior to Onto Innovation, Yang Song worked at Micron Technology. From November 2019 to January 2022, they served as an Operation Improvement Engineer. They developed data analytics web reports and provided solutions to enhance decision-making and improve productivity. They built a rule-based decision support system to identify non-performing workstations and save 70 man-hours per week. They also built a desktop application and implemented statistical methods to detect equipment performance mismatch.

Before that, from August 2018 to November 2019, Yang Song worked as a Process and Equipment Engineer I at Micron Technology. They applied statistical metrics, conducted Design of Experiment (DOE), and built a convolutional neural network model for defect pattern classification. Additionally, they created a web application to automate line yield prediction, reducing process time significantly.

Overall, Yang Song has extensive experience in data analysis, machine learning, automation, and improving operational efficiency in the semiconductor industry.

Yang Song earned a Bachelor's degree in Mechanical Engineering from Beijing Institute of Technology in 2017. Yang then went on to complete a Bachelor's degree in Mechanical Engineering from Technische Universität Darmstadt in 2017-2018. Yang Song furthered their education by obtaining a Master's degree in Mechanical Engineering from Nanyang Technological University Singapore in 2016-2017. In terms of certifications, Yang Song has completed several courses in data science, machine learning, and programming from Coursera, including "RPA Developer Foundation" from UiPath in January 2020, "Scalable Machine Learning on Big Data using Apache Spark" in January 2020, "Applied Data Science Capstone" in June 2019, "Databases and SQL for Data Science" in May 2019, "Machine Learning with Python" in April 2019, "Data Analysis with Python" in March 2019, "Data Visualization with Python" in March 2019, "Data Science Methodology" in February 2019, "Python for Data Science" in February 2019, "Convolutional Neural Networks" in March 2018, "Using Python to Access Web Data" in November 2017, "Using Databases with Python" in November 2017, "Python Data Structures" in October 2017, "Programming for Everybody (Getting Started with Python)" in September 2017, "Structuring Machine Learning Projects" in September 2017, "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization" in September 2017, and "Neural Networks and Deep Learning" in September 2017.