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Zhicheng Yan

from Belmont, CA
Age ~40
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Also known as:
Zhi Cheng Yan, Yan Zhicheng
Related to:
Xueming Yan
Connected to:
Andrew H Yan, 45Beizhan Yan, 54Camellia J Yan, 45Cecile S Yan, 73Chang Q Yan, 63Demas W Yan, 61Demi X Yan, 28Elena Yan, 51Elsa W Yan, 41Emy K Yan, 75

Zhicheng Yan Phones & Addresses

  • Belmont, CA
  • Fremont, CA
  • Menlo Park, CA
  • Champaign, IL
  • Urbana, IL
  • Palo Alto, CA
  • Mountain View, CA

Work

Company: University of illinois at urbana-champaign Aug 2010 Address: Computer Science Department Position: Ph.d candidate

Education

Degree: Ph.D School / High School: University of Illinois at Urbana-Champaign 2010 to 2015 Specialities: Computer Graphics, Visualization

Skills

Computer Vision • Machine Learning • Computer Graphics • Algorithms • Computer Science • Software Development • Image Processing • Deep Learning • Opengl • Latex • 3D Visualization • Modeling • Gpu Programming

Languages

Mandarin • English

Industries

Computer Software

Resumes

Resumes

Zhicheng Yan Photo 1

Staff Research Scientist

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Location:
1304 west Green St, Urbana, IL 61802
Industry:
Computer Software
Work:
University of Illinois at Urbana-Champaign - Computer Science Department since Aug 2010
Ph.D Candidate
Google NYC office - NYC May 2012 - Aug 2012
Software Development Engineer
Bosch Jul 2009 - Jun 2010
3D Graphics Intern
CAD&CG State Key Lab Sep 2007 - Jun 2009
Research Assistant
Education:
University of Illinois at Urbana-Champaign 2010 - 2015
Ph.D, Computer Graphics, Visualization Zhejiang University 2007 - 2010
Master, computer science
Skills:
Computer Vision
Machine Learning
Computer Graphics
Algorithms
Computer Science
Software Development
Image Processing
Deep Learning
Opengl
Latex
3D Visualization
Modeling
Gpu Programming
Languages:
Mandarin
English

Publications

Us Patents

Stylized Procedural Modeling For 3D Navigation

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US Patent:
20110166783, Jul 7, 2011
Filed:
Jan 5, 2010
Appl. No.:
12/652594
Inventors:
Liu Ren - Cupertino CA, US
Zhicheng Yan - Palo Alto CA, US
Assignee:
ROBERT BOSCH GMBH - STUTTGART
International Classification:
G01C 21/26
US Classification:
701212
Abstract:
A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.

Stylized Procedural Modeling For 3D Navigation

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US Patent:
20130317738, Nov 28, 2013
Filed:
Jul 29, 2013
Appl. No.:
13/953138
Inventors:
- Stuttgart, DE
Zhicheng Yan - Palo Alto CA, US
Assignee:
Robert Bosch GmbH - Stuttgart
International Classification:
G01C 21/36
US Classification:
701410, 701533
Abstract:
A method of displaying a navigation map includes determining a route of a vehicle. A two-dimensional footprint of a building disposed within a geographic area is received within the vehicle. The geographic area includes the route of the vehicle. Attribute information associated with the building is received. The attribute information is received within the vehicle. A visual representation of the building is rendered based upon the two-dimensional footprint and the attribute information.

Hierarchical Deep Convolutional Neural Network For Image Classification

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US Patent:
20160117587, Apr 28, 2016
Filed:
Dec 23, 2014
Appl. No.:
14/582059
Inventors:
Zhicheng Yan - Champaign IL, US
Robinson Piramuthu - Oakland CA, US
Vignesh Jagadeesh - Santa Clara CA, US
Wei Di - San Jose CA, US
Dennis Decoste - Palo Alto CA, US
International Classification:
G06N 3/08
G06N 3/04
Abstract:
Hierarchical branching deep convolutional neural networks (HD-CNNs) improve existing convolutional neural network (CNN) technology. In a HD-CNN, classes that can be easily distinguished are classified in a higher layer coarse category CNN, while the most difficult classifications are done on lower layer fine category CNNs. Multinomial logistic loss and a novel temporal sparsity penalty may be used in HD-CNN training. The use of multinomial logistic loss and a temporal sparsity penalty causes each branching component to deal with distinct subsets of categories.
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Zhicheng Yan from Belmont, CA, age ~40 Get Report