Yuke Zhu received his master’s and Ph.D. degrees from Stanford. His Ph.D. thesis centers around closing the perception-action loop to make robot intelligence more generalized and applicable to less-controlled environments. His research lies at the intersection of robotics, machine learning, and computer vision. He develops computational methods of perception and control that give rise to intelligent robot behaviors. Through his work, he aspires to teach robots to understand and interact with the visual world around them. His expertise has gained attention from a variety of news outlets, leading tech institutions, and award organizations. His publications have won several awards and nominations, including the Best Conference Paper Award in ICRA 2019. His work has been covered by media, such as MIT Technology Review and Stanford News.