Jiliang Tang, Department of Computer Science and Engineering
Michigan State University
Andrea Tagliasacchi, Research Scientist
Google Brain
It is not uncommon to think of computer graphics and computer vision as loosely disconnected disciplines; the former dealing with the synthesis of visual phenomena and the latter with analysis. However, recent advances in deep learning have blurred the boundary between the two. As a consequence, the research path to develop algorithms that effectively interpret the 3D scene “behind” an image has never seemed so well within reach.
Bryce Sandlund, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor J. Ian Munro
This thesis considers the study of data structures from the perspective of the theoretician, with a focus on simplicity and practicality. We consider both the time complexity as well as space usage of proposed solutions. Topics discussed fall in three main categories: partial order representation, range modes, and graph cuts.
Andrew Beach, Master’s candidate
David R. Cheriton School of Computer Science
Nafisa Anzum, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Semih Salihoglu
Laurie Williams
Distinguished University Professor
Department of Computer Science, North Carolina State University
MohammadReza Karegar, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Lukasz Golab
Danfei Xu, Computer Science Department
Stanford University
Sadjad Fouladi, Computer Science Department
Stanford University
Clara Kang, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Craig Kaplan
Symmetric patterns are used widely in clothing manufacture. However, the discontinuity of patterns at seams can disrupt the visual appeal of clothing. While it is possible to align patterns to conceal such pattern breaks, it is hard to create a completely seamless garment in terms of pattern continuity.