Litan Li
About Litan Li
Litan Li is a Machine Learning Engineer with extensive experience in developing machine learning pipelines and conducting scientific computations. He has worked at SparkCognition, CognitiveScale, and 3M, and holds both a Master's and Bachelor's degree from the Cockrell School of Engineering at The University of Texas at Austin.
Work at SparkCognition
Litan Li currently holds the position of Machine Learning Engineer at SparkCognition, where he has been employed since 2022. Prior to this role, he worked as a Data Scientist at the same company from 2019 to 2023. During his tenure, he contributed to the development of end-to-end machine learning pipelines tailored for industry applications. His work involved engaging in machine learning experimentation aimed at enhancing product features and performance.
Previous Experience in Machine Learning
Before joining SparkCognition, Litan Li worked at CognitiveScale as an Applied Machine Learning Scientist from 2018 to 2019. His role involved applying machine learning techniques to solve complex problems. Earlier in his career, he served as a Graduate Research Assistant at the Cockrell School of Engineering, The University of Texas at Austin from 2015 to 2017, where he implemented large-scale scientific computations using the map-reduce model for research and development purposes.
Early Career at 3M
Litan Li began his professional journey at 3M, where he worked as a Technical Aide for a duration of seven months in 2013. This early experience provided him with foundational skills in a technical environment, contributing to his later roles in machine learning and engineering.
Education and Expertise
Litan Li earned his Master of Science in Petroleum Engineering from the Cockrell School of Engineering, The University of Texas at Austin, completing his studies from 2015 to 2017. He also holds a Bachelor of Science in Chemical Engineering, graduating magna cum laude from the same institution in 2014. His educational background equips him with a strong foundation in engineering principles and machine learning methodologies.