Hi, I am Yu Feng, a fifth-year PhD student in Computer Science department, U of R. I am interested in computer system in general and programming for fun. Currently, I am working with Prof. Zhu. My research interest is about designing algorithms and frameworks for Mobile Vision Computing, including but not limited to in-sensor computing, point cloud-based applications, stereo vision.
Before I came to U of R, I attended Carnegie Mellon University as Master’s student in Material Science. I worked with Prof. Rollett as a researcher on scientific computing.
Yu Feng
3002 Wegmans Hall
Rochester, NY 14620 US
(412)706-4492
yfeng28@ur.rochester.edu
PhD in Computer Science. Rochester, NY. Until now.
Master of Material Science. Pittsburgh, PA. 2015-2017.
Bachelor of Material Science. Tianjin, CHINA. 2011-2015.
July 2021 - February 2022
Point cloud has become an important modality in many computer vision applications. However, the main operations in point cloud algorithms are memory-inefficient due to its irregularity and redundancy. We proposed a algorithm-hardware co-design approach to tame the memory irregularity without compromising the overall accuracy.
Match 2021 - July 2021
Pixel flow is an important cue for many video enhancement tasks. However, accurate pixel flow is hard to obtain for many monocular vision tasks. This work proposes a lightweight and fast algorithm to leverage the synchronized LiDAR to obtain a sparse pixel flow. Using the sparse pixel flow, we propose an unified framework to improve the performance of many video enhancement tasks.
September 2020 - February 2021
Eye tracking is an key application to enable many AR/VR applications. This project is inspired by the novel event-camera and design a mechanism to emulate the events in software. The software-emulated event allows us to effectively predict the eye region and only process the useful pixels to speedup the eye tracking pipeline.
January 2020 - April 2020
Point cloud becomes a common modality in deep learning. We found some common computation patterns that are unique to point cloud, and designed a framework for point cloud deep learning system.
October 2019 - February 2020
Point cloud is a key modality in many visual applications, such as autonomous driving. To enable real-time communications and data transfer, efficient real-time point cloud compression is important. We leverage the temporal and spatial redundancies in and across point cloud and develop an efficient compression technique.
October 2018 - April 2019
The key for Stereo Vision applications is the ability to obtain theambient information and estimate the depth of their surround-ings. We designed a framework, Stereo Engine, that leverages the unique characteristics in stereo vision and supports a wide range of algorithms in this domain.
March 2018 - November 2018
Web applications are mainly user-driven/user-oriented. When and what tasks will be triggered and executed largely depends on user behaviors. It is a open issue for mobile developers to design a scheduler that can fulfill user experience (to speedup) meanwhile decrease the amount of energy consumption. We proposed a proactive event scheduler. Instead of optimizing based on current system state, we expand our optimization scope to speculate future system state. By increasing our optimization scope, we expect to enhance the user experience and lower the energy consumption.
Color VR Passthrough. May 2022 - October 2022
Event-based Eye Tracking. September 2020 - December 2020
Smart Code Instruction Prefetching. May 2019 - September 2019