Keyi Tang
About Keyi Tang
Keyi Tang is a Senior Research Engineer at Borealis AI in Vancouver, Canada, specializing in biomechanics modeling and causal machine learning. With a background in data science and research, he has contributed to optimizing deeper transformers and has held various positions in academia and industry.
Work at Borealis AI
Keyi Tang has been employed at Borealis AI as a Senior Research Engineer since 2021. He is based in Vancouver, British Columbia, Canada. Prior to his current role, he worked at Borealis AI as a Research Engineer from 2020 to 2021 in Toronto, Ontario, Canada. His work at Borealis AI focuses on advancing research in artificial intelligence and machine learning.
Education and Expertise
Keyi Tang holds a Master of Applied Science (MASc) in Electrical and Computer Engineering from The University of British Columbia, which he completed from 2014 to 2017. He also earned a Bachelor of Engineering (BE) in Electrical and Electronics Engineering from the University of Electronic Science and Technology of China, graduating in 2014. His expertise includes biomechanics modeling and simulation, as well as causal machine learning for data-driven decision making.
Professional Background
Keyi Tang has a diverse professional background in data science and engineering. He worked as a Data Scientist and NLP Engineer at ForeSee from 2017 to 2019. He also served as an Applied Scientist at Amazon for seven months in 2019-2020. Additionally, he has experience as a Research Assistant in the Human Communication Technology Lab at the University of British Columbia from 2014 to 2017, and as a Teaching Assistant in 2015 and 2016.
Research Contributions
Keyi Tang has contributed to research focused on optimizing deeper transformers for small datasets. He co-authored a paper on this topic alongside several colleagues, including Peng Xu, Dhruv Kumar, and others. His research efforts aim to enhance the efficiency and effectiveness of machine learning models.
Skills and Specializations
Keyi Tang possesses strong skills in numerical simulation, which supports his work in biomechanics and machine learning. His specialization in causal machine learning enables him to apply data-driven decision-making techniques in various research and engineering contexts.