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Yuhao (Henry) Zhou
I am currently a fourth-year computer engineering student at
University of Toronto.
My focus of study are machine learning, software engineering and system control.
Since January in 2017, I have been working as an undergraduate research assistant under the supervision of
Prof. Sanja Fidler and
Prof. Jimmy Ba
on computer vison and reinforcement learning projects.
Before I worked as a research assistant, I primarily spent my time on various software engineering internships.
During which period, I gained strong coding skills through in-depth experience on large-scale engineering projects.
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Address:
RM1801, 24 Wellesley St. West
Toronto, Canada. M4Y 2X6
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Email:
henryzhou-at-cs-dot-toronto-dot-edu
henry-dot-zhou-at-mail-dot-utoronto-dot-ca
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CV /
LinkedIn /
GitHub /
Google Scholar
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Research
I am broadly interested in machine learning, computer vision, reinforcement learning, mathematics, image processing, and control theory.
(* Denotes equal contribution.)
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Neural Graph Evolution: Automatic Robot Design
Yuhao Zhou*, Tingwu Wang*, Sanja Fidler, Jimmy Ba
International Conference on Learning Representations, 2019
Abstract /
Bibtex /
Open Review
Despite the recent successes in robotic locomotion control, the design of robot relies heavily on human engineering.
Automatic robot design has been a long studied subject, but the recent progress has been slowed due to the large combinatorial search space and the difficulty in evaluating the found candidates.
To address the two challenges, we formulate automatic robot design as a graph search problem and perform evolution search in graph space.
We propose Neural Graph Evolution (NGE), which performs selection on current candidates and evolves new ones iteratively.
Different from previous approaches, NGE uses graph neural networks to parameterize the control policies, which reduces evaluation cost on new candidates with the help of skill transfer from previously evaluated designs.
In addition, NGE applies Graph Mutation with Uncertainty (GM-UC) by incorporating model uncertainty, which reduces the search space by balancing exploration and exploitation.
We show that NGE significantly outperforms previous methods by an order of magnitude.
As shown in experiments, NGE is the first algorithm that can automatically discover kinematically preferred robotic graph structures, such as a fish with two symmetrical flat side-fins and a tail, or a cheetah with athletic front and back legs.
Instead of using thousands of cores for weeks, NGE efficiently solves searching problem within a day on a single 64 CPU-core Amazon EC2.
@inproceedings{
wang2018neural,
title={Neural Graph Evolution: Automatic Robot Design},
author={Tingwu Wang and Yuhao Zhou and Sanja Fidler and Jimmy Ba},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=BkgWHnR5tm},
}
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Now You Shake Me: Towards Automatic 4D Cinema
Yuhao Zhou, Makarand Tapaswi, Sanja Fidler
Computer Vision and Pattern Recognition (CVPR), 2018 (Spotlight)
Abstract /
Bibtex /
Project Web /
PDF /
CVPR Spotlight /
Poster
Media:
UofT News /
CBC Radio News /
Inquisitr News
We are interested in enabling automatic 4D cinema by parsing physical and special effects from untrimmed movies.
These include effects such as physical interactions, water splashing, light, and shaking, and are grounded to either a character in the scene or the camera.
We collect a new dataset referred to as the Movie4D dataset which annotates over 9K effects in 63 movies.
We propose a Conditional Random Field model atop a neural network that brings together visual and audio information, as well as semantics in the form of person tracks. Our model further exploits correlations of effects between different characters in the clip as well as across movie threads.
We propose effect detection and classification as two tasks, and present results along with ablation studies on our dataset, paving the way towards 4D cinema in everyone's homes.
@inproceedings{Zhou2017_Movie4D,
author = {Yuhao Zhou and Makarand Tapaswi and Sanja Fidler},
title = {{Now You Shake Me: Towards Automatic 4D Cinema}},
year = {2018},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {Jun.}, doi = {}}
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Industry Experience
I spent most of my time as a software engineering intern in the first 3 summers during undergradate studies.
Please email me for more details of the experience.
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Nvidia Corporation
Research Intern
Toronto, Canada
January, 2019 -
AI research team.
Under the supervison of Sanja Fidler, working on Computer Vision projects.
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Intel PSG
PEY (Professional Experience Year) Intern
San Jose, CA
May, 2017 - December, 2017
Participated in software development in Quartus high-level synthesis group.
Engaged in large-scale C++ programming projects on software backward compatibility.
Enhanced customers' usability to use pre-compiled products to compile on latest Quartus software. (Perl)
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Oracle Corp.
R&D Intern
Beijing, China
June, 2015 - Aug, 2015
Worked in R&D department cloud computing group.
Exposure to cloud-computing architecture and networking.
Utilized integrated tools to manage cloud-computing resources and services for the entire R&D department.
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Huawei Technologies Co. Ltd
Android Programming Intern
Beijing, China
May, 2015 - June, 2015
Beijing R&D Deartment: automated testing team.
Familiarzed with Android OS and its GUI layout.
Transplanted old testing scripts (Java/Python) to allow automated testing on latest Ascend phone series.
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