I am a first-year PhD student in Computer Science at University of Illinois Urbana-Champaign, advised by Liangyan Gui & Yuxiong Wang. Previously, I graduated from Carnegie Mellon University with MS in Robotics, where I was advised by Kris Kitani, and Peking University with BS in Computer Science, where I was advised by Liwei Wang.
I mainly study the intersection of machine learning and computer vision, in particular efficient neural networks, network compression, neural architecture search, and object detection. My major focus is to improve the (training, inference, searching) efficiency of deep learning models.
I am looking for research internships for Summer 2022. Feel free to contact me if interested!
PhD in Computer Science, 2021 - Present
University of Illinois Urbana-Champaign
MS in Robotics, 2019 - 2021
Carnegie Mellon University
BS in Computer Science, 2015 - 2019
Analyze the slow convergence of DEtection TRansformer (DETR), and propose alternative solutions to improve convergence and performance of DETR.
Propose a novel neural architecture search formulation to encourage flatness in the architecture space, which improves generalization of searched architectures.
Accelerate performance estimation for exploring new model configurations, improving neural achitecture search and hyper-parameter optimization within limited time.