Lisha Han
About Lisha Han
Lisha Han is a Test Engineer II at Owlet Baby Care, specializing in deep-learning projects and machine learning implementations. She holds multiple degrees in Industrial Engineering, Computer Science, and Electrical Engineering from Dalian University of Technology and Northeastern University.
Current Role at Owlet Baby Care
Lisha Han currently serves as a Test Engineer II at Owlet Baby Care, a position she has held since 2021. In this role, she is involved in various testing processes to ensure the quality and reliability of the company's products. Her work contributes to the development of innovative baby care solutions, enhancing the user experience for parents and caregivers.
Previous Experience at Owlet Baby Care
Before her current role, Lisha Han worked at Owlet Baby Care as a QA Automation and Test Engineer from 2018 to 2021. During this time, she focused on automating testing processes to improve efficiency and product quality. Her contributions during these three years helped streamline the testing workflow within the organization.
Professional Background at Neusoft
Lisha Han worked at Neusoft from 2012 to 2014 as the Integration Test QAE Team Lead in the Greater Seattle Area. In this capacity, she led a team responsible for ensuring the integration quality of software products. Additionally, she briefly served as an Android Software QAE for four months in 2012 in Dalian, Liaoning, China, where she focused on quality assurance for Android applications.
Educational Qualifications
Lisha Han holds multiple degrees in engineering and computer science. She earned her Bachelor's degree in Electrical, Electronics and Communications Engineering from Dalian University of Technology, completing her studies from 2006 to 2010. She then pursued a Master's degree in Industrial Engineering at the same institution from 2006 to 2012. Later, she obtained a Master's degree in Computer Science from Northeastern University, studying from 2016 to 2018.
Technical Skills and Expertise
Lisha Han possesses expertise in deep learning, particularly in object recognition using Convolutional Neural Networks (CNN) and Single Image Super Resolution. She has experience in data cleaning, exploratory data analysis, and machine learning model selection using Python libraries such as Pandas, Numpy, and Scikit Learn. Her skills also include deploying machine learning projects on cloud platforms and utilizing visualization tools like PowerBI, matplotlib, seaborn, and TensorBoard.