About

Chirag Choudhary is a highly skilled Machine Learning Engineer with 3.86 years of experience in the field. He is currently working at Qualcomm, where he is responsible for designing and implementing machine learning models to improve the performance of various systems. Chirag has a Master of Science in Computer Science from the University of California, Irvine and a Bachelor of Technology in Computer Science and Engineering from the Indian Institute of Technology, Roorkee. Chirag has a strong background in software engineering and has worked on a variety of projects throughout his career. He has experience working with Elastic Search, Javascript, Python, and Java, among other technologies. Chirag is also highly skilled in UI design and has built several web applications throughout his career. In his previous role at Oracle, Chirag worked on a machine learning project to proactively analyze cluster-wide log files and detect anomalies. He used clustering and pattern matching to identify unique patterns in logs for individual database components and reduced the possible anomalous lines in database logs by approximately 86%. Chirag also built an anomaly detection timeline to visualize important events across multiple database components in chronological order, which greatly improved the time needed to identify underlying errors and resolve bugs. Chirag's past experience also includes working on his MS thesis at UC Irvine, where he experimented with various knowledge graph completion models on synthetic datasets to validate their suitability for learning different relational patterns. He evaluated the relation learning capabilities of five state-of-the-art KB completion methods and performance variation based on amounts of data and embedding dimensionality of the learned embeddings. Overall, Chirag is a highly skilled and experienced Machine Learning Engineer with a strong background in software engineering and UI design. He is passionate about using his skills to develop innovative solutions that improve the performance of various systems.