# Rufeng Meng

> Uber

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/rufeng

Knowledge and hands-on experiences on vehicle/human localization, driving analytics, machine learning, computer vision, platform development (ADAS/AD, mobile), simulation, wireless/sensor network. An energetic and very adaptive team player, quick learner and enthusiastic problem solver.

https://sites.google.com/view/rufeng/

## Work Experience
### Software Engineer @ Uber
Jan 2019 – Present | San Francisco Bay Area

### Sr. Software Engineer  (Automated Driving Group & Mobileye) @ Intel Corporation
Jan 2017 – Jan 2019
Designed and developed safety applications and AD/ADAS platform; supported teams and optimized development process;  demonstrated and invented technologies/solutions.

a) Safety Applications:
    Designed V2V safety applications (including software architecture, algorithm, GUI, etc); oversaw the development; designed and carried out data collection/validation and KPI measurement. 

b) AD/ADAS Platform:
    Played a key role in the design and development of AD/ADAS platform (including sensor simulation, sensor data logging, image processing/visualization and common libraries/APIs).

c) Team/Development Support:
    Actively optimized code architectures, automated and simplified the development and test process for the teams to improve efficiency and quality;  educated team members through technical presentations;  shared knowledge and provided feedbacks to external team(s) to facilitate their product definition and development.

d) Technology Demonstration:
     Played a key role (including designed and developed PoC, coordinated teams from different groups/sites for development/integration/test/logistics) in the successful demonstration of Intel’s computing platforms and wireless technologies for ADAS/AD in various venues, including MWC.

e) Innovation:
    Filed 3 patents in the fields of autonomous technologies and wireless connectivity solutions.

### Research Intern @ Zendrive
Jan 2014 – Jan 2015
Conducted research on smartphone-based driving analytics

a) Driver Behavior Analysis:
    Design and developed a motion sensor and GPS based machine learning model for identifying aggressive driving patterns (such as hard brake, sharp turn, harsh acceleration) and also identifying the positions of a phone inside a vehicle and how a driver uses his phone during driving.

b) Vehicle Speed Estimation:
    Designed and developed a sensor based system (with map knowledge) for estimating vehicle speed in an overwhelming majority of road/traffic situations.


## Education
### Doctor of Philosophy (PhD) in Computer Science (Mobile Sensing/Computing)
University of South Carolina


## Contact & Social
- LinkedIn: https://linkedin.com/in/rufengmeng

---
Source: https://flows.cv/rufeng
JSON Resume: https://flows.cv/rufeng/resume.json
Last updated: 2026-03-29