Course Staff on CS559 Computer Graphics - Fall 2026

Alex Peseckis

Mon, 01 Jan 0001 00:00:00 +0000

Alex Peseckis is a third-year PhD student in Computer Sciences working in the Computer Graphics lab under Professor Michael Gleicher. He is interested in computer graphics and robotics. In his free time, he enjoys programming, playing video games, and going on walks.

Isabelle Xue

Mon, 01 Jan 0001 00:00:00 +0000

Isabelle is a sophomore majoring in computer science and mathematics. She is interested in research on human-robot interaction and multi-agent reinforcement learning, as well as applications of computer graphics for game development. In her free time, she enjoys photography, playing video games, listening to music and playing instruments, and doing crafts.

Jae Woo Song

Mon, 01 Jan 0001 00:00:00 +0000

Jae-Woo is a fourth-year undergraduate student at UW-Madison majoring in Computer Science with a certificate in Mathematics. His academic interests are machine learning (particularly reinforcement learning) and its applications in robotics.

Jingyi Gao

Mon, 01 Jan 0001 00:00:00 +0000

Jingyi is a fifth-year PhD student in computer science at the University of Wisconsin-Madison. Her research focuses on learning theory, particularly the design of statistically efficient algorithms that remain reliable under data corruption, missingness, and reproducibility constraints.

Before joining UW-Madison, she earned bachelor’s degrees in mathematics and computer science from the University of Michigan-Ann Arbor.

Young Wu

Mon, 01 Jan 0001 00:00:00 +0000

Young has been teaching CS559 since 2024, and has been the teaching assistant for Professor Gleicher ’s CS559 for three years before that. He has also been teaching CS540 Artificial Intelligence since 2019. He has also taught undergraduate game theory at the University of Toronto from 2015 to 2016.

Young’s research is in adversarial machine learning and multi-agent reinforcement learning, which lie in the intersection between machine learning and game theory. He is also interested in computer graphics and data visualization since they are useful in simulating reinforcement learning environments and visualizing the policies learned by the agents.