Bolong Cheng
About Bolong Cheng
Bolong Cheng is a Research Engineer at SigOpt, specializing in Bayesian optimization and sequential decision-making. He holds a PhD in Operations Research from Princeton University and has a background in Electrical Engineering from The University of Texas at Austin.
Work at SigOpt
Bolong Cheng has been employed at SigOpt as a Research Engineer since 2017. In this role, he focuses on productionizing Bayesian optimization and addressing sequential decision-making problems. His work contributes to the application of advanced optimization techniques in various domains, enhancing the capabilities of SigOpt's offerings. Prior to his current position, he served as a Research Engineer Intern at SigOpt for three months in 2017, where he gained initial experience in the company's research environment.
Education and Expertise
Bolong Cheng holds a Doctor of Philosophy (PhD) in Operations Research from Princeton University, where he studied from 2011 to 2017. His doctoral research concentrated on approximate dynamic programming, stochastic optimization, and optimal learning, particularly in the context of managing grid-level battery storage. He also earned a Master of Arts (M.A.) in Electrical Engineering from Princeton University from 2011 to 2013. Earlier, he completed a Bachelor of Science (BS) in Electrical Engineering at The University of Texas at Austin from 2007 to 2011.
Background
Bolong Cheng's academic journey began at The University of Texas at Austin, where he obtained his Bachelor of Science in Electrical Engineering. He then pursued advanced studies at Princeton University, earning both a Master of Arts and a PhD in Operations Research. During his doctoral studies, he was advised by Professor Warren B. Powell, a notable figure in the field. His educational background provides a strong foundation for his current research interests and professional work.
Research Interests
Bolong Cheng is interested in applying sequential optimization techniques within scientific and engineering domains, particularly in materials simulation and design. His research explores the intersection of optimization methodologies and practical applications, aiming to enhance decision-making processes in various fields. This focus aligns with his work at SigOpt, where he leverages his expertise to tackle complex optimization challenges.