I am a Research Engineer at Google DeepMind. I received my PhD in Computer Science from UCLA in 2018, advised by Professor Song-Chun Zhu.


Paleo: A Performance Model for Deep Neural Networks

Joint work with Evan R. Sparks and Ameet Talwalkar

Paleo is an analytical model to estimate the scalability and performance of deep learning systems. It can be used for efficiently exploring the space of scalable deep learning systems and quickly diagnosing their effectiveness for a given problem instances. Paleo is robust to the choice of network architecture, hardware, software, communication schemes, and parallelization strategies.

Runner-up prize for best real-world application at Southern California Machine Learning Symposium 2016.

Paper Web View on Github
Restricted Visual Turing Test

Joint work with Tianfu Wu, Mun-Wai Lee, and Song-Chun Zhu

This project features a restricted visual Turing test (VTT) which evaluates computer vision systems' understanding of scenes and events in videos by story-line based queries. We collected a long-term and multi-camera captured video dataset. To perform the test, we built an integrated system consisting of a well-designed architecture, various vision modules, a knowledge base, and a query engine.

Project Website
Topic Discovery and Story Segmentation for Broadcast News

Joint work with Weixin Li, Jungseock Joo, and Song-Chun Zhu

Topic discovery and story segmentation provides fundamental methods for automatically organizing, analyzing, searching, and visualizing the vast amount of news videos available online. In this project, we present a topic discovery and story segmentation framework based on Swendsen-Wang Cuts, aiming at dividing news videos into stories and generating a topic hierarchy to organize these stories.



Federated Visual Classification with Real-World Data Distribution.
Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown.
arXiv:2003.08082. 2020. [pdf]

Measuring the effects of non-identical data distribution for federated visual classification.
Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown.
Workshop on Federated Learning. NeurIPS 2019. [pdf]

Low-Shot Learning with Imprinted Weights.
Hang Qi, Matthew Brown, and David G. Lowe.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [pdf]

Scene-centric Joint Parsing of Cross-view Videos.
Hang Qi*, Yuanlu Xu*, Tao Yuan*, Tianfu Wu, and Song-Chun Zhu.
AAAI Conference on Artificial Intelligence (AAAI), 2018 (Oral). [pdf]

Paleo: A Performance Model for Deep Neural Networks.
Hang Qi, Evan R. Sparks, and Ameet Talwalkar.
International Conference on Learning Representations (ICLR), 2017. [pdf]

Joint Image-Text News Topic Detection and Tracking by Multimodal Topic And-Or Graph.
Weixin Li, Jungseock Joo, Hang Qi, and Song-Chun Zhu.
IEEE Transactions on Multimedia (TMM), Volume 19 Issue 2, February 2017. [pdf]

A Restricted Visual Turing Test for Deep Scene and Event Understanding.
Hang Qi*, Tianfu Wu*, Mun Wai Lee, and Song-Chun Zhu.
arXiv:1512.01715. 2015. [pdf]