Portfolio item number 1
Short description of portfolio item number 1
Posts by Collection
portfolio
Portfolio item number 2
Short description of portfolio item number 2
publications
Paper Title Number 1
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Paper Title Number 2
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Paper Title Number 3
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
talks
Equivariant Learning for Robotic 3D Perception
Published:
In this talk, I introduced equivariant models and their applications in robotic 3D perception.
teaching
NA/EECS 568, ROB 530: Mobile Robotics: Methods and Algorithms
Joint Instructor, Graduate Course, University of Michigan
Offered: Winter 2024
Description: Theory and application of probabilistic and geometric techniques for autonomous mobile robotics. This course presents and critically examines contemporary algorithms for robot perception. Topics include Bayesian filtering; stochastic representations of the environment; motion and sensor models for mobile robots; algorithms for mapping and localization; application to autonomous marine, ground, and air vehicles.
NA 565, ROB 535, ME 599: Self-Driving Cars: Perception and Control
Joint Instructor, Graduate Course, University of Michigan
Offered: Fall 2023, Fall 2024
Description: Self-driving cars are a transformative technology for society. This course covers the underlying technologies in perception and control. Topics include deep learning, computer vision, sensor fusion, localization, trajectory optimization, obstacle avoidance, and vehicle dynamics. The course includes theoretical underpinnings of self-driving car algorithms and practical application of the material in hands-on labs.
NA/ROB/MATH 599, PHY 590: Computational Symmetry in AI and Robotics
Joint Instructor, Graduate Course, University of Michigan
Offered: Fall 2024
Description: Symmetry describes the intrinsic structure and properties of a subject. In this course, we will explore the symmetries in geometry and rigorously express them in mathematics, covering relevant topics in group theory, differential geometry, representation theory, and Lie groups. Then, we will use them as tools to achieve computationally efficient and generalizable algorithms for learning, perception, estimation, and control with applications in many domains, such as AI, computer vision, and robotics. The course will cover novel topics in geometric learning and explores state of the art in symmetry-preserving and geometric learning methods.