Ya Zhu
About Ya Zhu
Ya Zhu is a Data Scientist with extensive experience in machine learning and data analysis. He has worked at Enigma Technologies, Inc. since 2018 and previously held positions at NYU, HTC, and Zhejiang University.
Work at Enigma
Ya Zhu has been employed at Enigma Technologies, Inc. as a Data Scientist since 2018. In this role, Zhu applies expertise in machine learning and data analysis to develop solutions that enhance data-driven decision-making. The position involves working with complex datasets and implementing advanced algorithms to extract insights relevant to business operations.
Education and Expertise
Ya Zhu holds a Master of Science (MS) degree in Computer Science from New York University, which was completed in 2017. Prior to this, Zhu earned a Bachelor's degree in Computer Science and Technology from Shanghai Jiao Tong University in 2012. Additionally, Zhu has a Master's degree in Computer Science and Engineering from National Taiwan University, obtained in 2016. Zhu's educational background provides a strong foundation in programming, data analysis, and machine learning.
Background
Ya Zhu's professional journey includes various internships and research positions. Zhu worked as a Data Science Intern at NYU in 2017, where machine learning models and APIs were developed. Prior to that, Zhu was a Machine Learning Intern at HTC from 2014 to 2015. Zhu also served as a Research Assistant at Zhejiang University from 2012 to 2013, contributing to the development of LIBLINEAR version 2.11.
Technical Skills
Ya Zhu possesses strong programming skills in Python, C++, and Java. Zhu is familiar with both SQL and NoSQL database systems, including MySQL, Hive, HBase, and ElasticSearch. Additionally, Zhu has experience with distributed computing platforms such as Hadoop and Spark. Expertise in deep learning frameworks like Tensorflow and Keras further enhances Zhu's capabilities in implementing advanced machine learning methods.
Achievements
During the tenure at Zhejiang University, Ya Zhu contributed to a conference paper presented at ACML 2017, which was a result of work on LIBLINEAR version 2.11. Zhu's ability to develop machine learning models and classifiers, including a model for predicting industry codes from company descriptions, demonstrates a commitment to advancing data science methodologies.