Xiaofeng Ren

Chief Scientist, Amap (AutoNavi) @ Alibaba
Bellevue, WA, USA

x dot ren at alibaba-inc dot com

I am currently chief scientist of Amap (AutoNavi), a subsidiary of Alibaba, China's leading mapping, navigation, and location-based service provider. I joined Alibaba in 2017, initially as chief scientist and associate dean of the Institute of Data Science and Technology (iDST), Alibaba's AI R&D Division. Alibaba has a fast growing site in Bellevue (yes I am still in sunny Seattle, love it) and I am hiring in computer vision (Seattle, Bay Area as well as Beijing and Hangzhou)! (CV,Google Scholar).

Prior to Alibaba, I was a senior principal scientist at Amazon. For 2013-17, I was the lead scientist at Amazon Go, using computer vision and machine learning to re-invent retail. We have launched our first Just-Walk-Out store that automatically figures out purchases without customer effort, completely eliminating check-out (that "unnecessary" and annoying wait).

I am interested in all aspects of computer vision, as I believe all are needed to solve it. Prior to Amazon, I worked on using RGB-D (color+depth, a.k.a. Kinect style) cameras, ranging from 3D mapping and modeling to everyday object recognition. I worked on many other vision problems, including image descriptors, boundary detection, image segmentation, figure-ground grouping, object and pose recognition, human body detection and pose estimation, object segmentation and tracking, and optical flow. I had opportunities to work on vision-related problems in robotics and human-computer interaction.

I was a research scientist at Intel Labs during 2008-2013, working closely with faculty and students at University of Washington. For 2006-2008, I was a research assistant professor at the Toyota Technological Institute at Chicago (TTI-C). I received my Ph.D. from U.C. Berkeley in 2006, under the supervision of Jitendra Malik.

(Some) Recent Updates

(Old) Updates


(Old) Research Projects

Discriminative Viewpoint Classification
RGB-D Mapping
Egocentric Object Recognition
Multi-Scale Improves Boundary Detection
Local Grouping for Optical Flow
Finding and Tracking People in Archive Films
Tracking as Repeated Figure/Ground Segmentation
Line-based Aspect Learning and Matching
Figure-ground organization in natural images
Cue Integration in Figure/Ground Labeling
Scale-Invariant Contour Completion using Conditional Random Fields
Using Shapemes for Mid-level Vision
A Scale-Invariant Image Representation: the CDT Graph
Pairwise Constraints between Human Body Parts
Learning Discriminative Models for Image Segmentation
Human Body Configuration from Bottom-Up: a Segmentation-based Approach
Contours in Natural Images and Scale Invariance
Superpixel: Empirical Studies and Applications