Xinshuo Weng
Xinshuo Weng is a research scientist at NVIDIA Research working with Marco Pavone. She received a Ph.D. in Robotics (2018-2022) and a Master in Computer Vision (2016-17) working with Kris Kitani at Carnegie Mellon University. She's also worked with Yaser Sheikh at Facebook Reality Lab as a research engineer to help build “Photorealistic Telepresence”. Her bachelor was received from Wuhan University. Her research interest lies in generative models and 3D computer vision for autonomous systems. She has developed 3D multi-object tracking systems such as AB3DMOT that received >1,300 stars on GitHub. Also, she is leading a few autonomous driving workshops at top conferences such as NeurIPS, IJCAI, ICCV, ICML and IROS. She was awarded a number of fellowships and nominations such as the Qualcomm Innovation Fellowship for 2020 and Facebook Fellowship Finalist for 2021.
Specifically, Xinshuo's research spans the tasks of object detection, multi-object tracking, re-identification, trajectory prediction, and motion planning, with an ultimate goal of building an autonomous system such as self-driving cars that can safely interact with others in multi-agent dynamic environments. Towards this goal, she develops computational models to solve each individual task using machine learning techniques such as graph neural networks, transformers, generative adversarial networks and variational auto-encoders. To make the entire robot system robust and safe, Xinshuo's research also aims to seamlessly integrate models across tasks by building differentiable pipelines, propagating uncertainties from the upstream to downstream models, and exploring the most effective structure of the differentiable pipelines.
Fields: Computer Vision, Robotics, Machine Learning
Topics: 3D Computer Vision, Autonomous Driving, Generative Models, Perception, Imitation Learning
Specifically, Xinshuo's research spans the tasks of object detection, multi-object tracking, re-identification, trajectory prediction, and motion planning, with an ultimate goal of building an autonomous system such as self-driving cars that can safely interact with others in multi-agent dynamic environments. Towards this goal, she develops computational models to solve each individual task using machine learning techniques such as graph neural networks, transformers, generative adversarial networks and variational auto-encoders. To make the entire robot system robust and safe, Xinshuo's research also aims to seamlessly integrate models across tasks by building differentiable pipelines, propagating uncertainties from the upstream to downstream models, and exploring the most effective structure of the differentiable pipelines.
Fields: Computer Vision, Robotics, Machine Learning
Topics: 3D Computer Vision, Autonomous Driving, Generative Models, Perception, Imitation Learning
