Jing Zhao

Jing Zhao has a diverse work experience spanning several companies and roles. From 2011 to 2012, Jing worked as a Research Assistant at the University Medical Center Groningen. In 2012, Jing joined Stockholm University as a PhD Candidate, specializing in data mining, machine learning, health informatics, and data analytics. Here, Jing focused on various aspects such as feature engineering, handling high dimensional and imbalanced data, creating data-driven representations of complex data, and learning predictive models from electronic health records. Jing's research also explored the impact of concept hierarchies of clinical codes on the effectiveness of predictive models and created different types of ensemble models. After completing the PhD studies in 2017, Jing took a break for parental leave from April to December. In 2018, Jing joined Campanja as a Data Scientist, a role that continued until November 2019. Jing then moved to EasyPark Group, where they held the position of Data Scientist from November 2019 to September 2020 before being promoted to the role of Head of Data Insights from September 2020 onwards.

Jing Zhao has a strong educational background in computer science and data analysis. Jing obtained their Bachelor of Science (B.Sc.) in Information Engineering from Xi'an Jiaotong University in 2008. Jing then pursued a Master of Science (M.Sc.) in Engineering and Management of Information Systems from KTH Royal Institute of Technology from 2008 to 2010. After that, they completed a Master of Science (M.Sc.) in Clinical and Psychosocial Epidemiology from the University of Groningen from 2010 to 2012.

Jing Zhao continued their academic journey by pursuing a Doctor of Philosophy (Ph.D.) in Computer and Systems Sciences from Stockholm University from 2012 to 2017.

In addition to their formal education, Jing Zhao has also obtained several certifications. Jing completed various courses and certifications in machine learning and data analysis. These include certifications such as "Convolutional Neural Networks," "Structuring Machine Learning Projects," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Neural Networks and Deep Learning," "Learning From Data," "Statistical Learning," "the Third Lisbon Machine Learning School: Learning with Big Data," "Machine Learning," and "Computing for Data Analysis" from institutions such as Coursera, edX, and Stanford University.