Yanan Jia

Yanan Jia has a diverse work experience in the field of machine learning and data science. Yanan began their career as a Teaching Assistant and Research Assistant at The Ohio State University, where they gained experience in grading, leading recitations, and conducting research. Yanan then interned as a Data Science and Machine Learning Intern at Glassdoor.

Yanan's most recent role was at Businessolver, where they worked as a Machine Learning Scientist and later as a Machine Learning Engineer. In these roles, they built a speech recognition system and a multimodal sentiment analysis system for the company's chatbot and call center. Yanan was responsible for collecting and cleaning training data, annotating the data, and building acoustic, language, and punctuation models. Yanan also implemented decision-level fusion of text and audio sentiment analysis. Additionally, they worked on health insurance recommendations using machine learning technology and identified the intent and entity behind user inputs for a retrieval-based chatbot.

Yanan's early work experience includes a role as a Teaching Assistant at The University of Toledo, where they lectured and led recitations for various math courses.

Overall, Yanan has a strong background in machine learning, data science, and teaching, with experience in developing speech recognition and sentiment analysis systems, conducting research, and providing instruction in mathematics.

Yanan Jia began their education at China University of Mining and Technology, where they earned a Bachelor's Degree in Mathematics from 2004 to 2008. Following this, they pursued a Master's Degree in Statistics from The University of Toledo from 2008 to 2010. Yanan then furthered their education at The Ohio State University, where they obtained a Doctor of Philosophy (Ph.D.) in Statistics from 2010 to 2016.

In addition to their formal education, Yanan Jia has received several certifications from Coursera. In 2018, they obtained certifications in various topics, including Sequence Models, Convolutional Neural Networks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, and Structuring Machine Learning Projects.