报告人： Prof. QiXing Huang, University of Texas at Austin
报告题目：Five principles for picking research problems
个人简介：Qixing Huang is an associate professor with tenure at the computer science department of the University of Texas at Austin. His research sits at the intersection of graphics, geometry, optimization, vision, and machine learning. He has published more than 100 papers at leading venues across these areas. His research has received several awards, including multiple best paper awards, the best dataset award at Symposium on Geometry Processing 2018, IJCAI 2019 early career spotlight, 2021 NSF Career award, and awards from Adobe, Intel and Google. He has also served as area chairs of CVPR and ICCV and technical papers committees of SIGGRAPH and SIGGRAPH Asia, and co-chaired Symposium on Geometry Processing 2020.
报告简介：Picking good research problems to work on is a fundamental skill to learn during Ph.D. study. In this talk, I will highlight five principles for picking research problems, ranging from solving old problems using new techniques to opening a new problem to work on to establish the skeleton of a field to interdisciplinary research to dataset-driven research. I will emphasize both examples from the general AI field and my personal experience. The talk targets a general audience, and no technical background is required.