About Me

Hi, I am Yu Feng, a third-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 current research is about creating a better system for Vision Computing and optimizing Web application platfrom, such as Chromium.

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.

Contact Details

Yu Feng
3002 Wegmans Hall
Rochester, NY 14620 US



University of Rochester

PhD in Computer Science. Rochester, NY. Until now.

Carnegie Mellon University

Master of Material Science. Pittsburgh, PA. 2015-2017.

Tianjin Polytechnic University

Bachelor of Material Science. Tianjin, CHINA. 2011-2015.


Efficient Deep Learning for Point Cloud

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.

Real-time Point Cloud Compression

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.

Co-Design Stereo Vision System

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.

Task Scheduling in Runtime Application

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.


Conference Articles

Mesorasi: Enabling Efficient Point Cloud Analytics via Delayed-Aggregation, MICRO 2020. [slide]

Yu Feng*, Boyuan Tian*, Tiancheng Xu*, Paul Whatmough, Yuhao Zhu

(*: first three authors are equal-contribution.)
Real-Time Spatio-Temporal LiDAR Point Cloud Compression, IROS 2020. [slide]

Yu Feng, Shaoshan Liu, Yuhao Zhu

ASV: Accelerated Stereo Vision System, MICRO 2019. [slide]

Yu Feng, Paul Whatmough, Yuhao Zhu

PES: Proactive Event Scheduling for Responsive and Energy-Efficient Mobile Web Computing, ISCA 2019. [slide]

Yu Feng, Yuhao Zhu

Journal Articles

Extension of the Mechanical Threshold Stress Model to Static and Dynamic Strain Aging: Application to AA5754-O, 21 August 2017.

Yu Feng, Sudipto Mandal, Brian Gockel & Anthony D. Rollett


Programming Languages

C, C++, Python, Java, Halide, Ruby

Other Toolkits

Matlab, OpenGL(basic)


MySQL, HBase(basic)