# Chenyun Wu

> Software Engineer at Waymo

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

I'm a Ph.D. student advised by Subhransu Maji in the computer vision lab of UMass Amherst. I’m interested in broad topics in computer vision, especially the combination of vision and natural language. I study the joint modeling of visual and language signals and leverage the supervision of language to further understand various visual domains including fine-grained categories, objects/stuff in images, visual textures, and videos.
I’m expected to graduate in the summer 2021. I’m looking for research positions in the industry.

## Work Experience
### Software Engineer @ Waymo
Jan 2021 – Present

### Research And Teaching Assistant @ University of Massachusetts Amherst
Jan 2015 – Jan 2021
I gained rich experience of teaching and communication through being a  teaching assistant for courses including "Computer Vision", "Neural Networks",  "Reasoning Under Uncertainty",   "Introduction to Problem Solving with Computers", etc.

### Research Intern @ ByteDance
Jan 2020 – Jan 2020 | California, United States
I worked with Xiaohui Shen, Xiaojie Jin, and Longyin Wen on localizing clips in videos with natural language descriptions.
- Reproduced and visualized results from state-of-the-art models (DRN, LGI, CMINS). Analyzed and compared datasets (ActivityNet-Captions, Charades-STA, TACoS).
- Implemented a graph convolutional net to reason the target clip with language syntactics.
- Designed an attention mechanism to leverage object detection results to associate frames with nouns in sentences.

### Computer Vision Research Intern @ Adobe
Jan 2018 – Jan 2019 | San Jose, CA, United States
I worked with Zhe Lin, Scott Cohen, and Trung Bui on large-scale visual grounding. Our work was published on CVPR 2020 as “PhraseCut: Language-based Image Segmentation in the Wild”.

### Software Engineer Internship @ Google
Jan 2017 – Jan 2017 | Mountview, CA, United States
I worked with Nick Johnston, George Toderici, David Minnen, and Michele Covell on deep image compression.
- Implemented a U-Net image compression model with quantizers and binarizers on skip connections at different levels.
- Designed and tuned the training procedure to analyze the effectiveness of each skip connection.
- Enabled different trade-offs between compression size and quality by turning on and off some skip connections.


## Education
### MS/PhD in Computer Science
University of Massachusetts Amherst

### Bachelor of Science (B.S.) in Physics & Computer software
Peking University


## Contact & Social
- LinkedIn: https://linkedin.com/in/chenyun-wu
- Portfolio: https://people.cs.umass.edu/~chenyun/

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