As a former machine learning intern at Form Energy, I applied my skills and knowledge in time series data mining, classification, and visualization to support the development of long-duration energy storage systems. I worked with a multidisciplinary team of engineers, scientists, and analysts to design and implement novel algorithms and solutions that can enhance the performance and efficiency of the company's products and services.I have completed my PhD in Computer Science at University of California, Riverside, where I focused on discovering and reasoning about conserved structures or time series motifs in large and complex datasets. My research has resulted in multiple publications and patents, including a new time series primitive called Novelets, which enables the first emerging time series behavior discovery algorithm in the literature. I am motivated by the potential of machine learning and data mining to uncover hidden patterns and insights that can benefit various domains and applications, such as seismology, bioinformatics, transportation, and biology.