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Carissa E Lew

from Santa Clara, CA
Age ~49
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Also known as:
Calvin D Lew, Calvin Daniel Lew, Calvin O Lew, Calvin Lau
Related to:
Raymond C NgZee N GanDebbie Marquez, 58
Connected to:
Ai Y Lew, 63Alfred K Lew, 49Angela L Lew, 40Audra L Lew, 53Austin T Lew, 28Bridget Y Lew, 29Carolyn W Lew, 74Chung M Lew, 101Dov Lew, 51Edmond Lew, 74

Carissa Lew Phones & Addresses

  • Santa Clara, CA
  • San Jose, CA
  • Sunnyvale, CA
  • San Mateo, CA
  • San Francisco, CA
  • Stanford, CA
  • Alhambra, CA

Work

Company: Apple Jan 2020 Position: Technical lead and architect

Education

Degree: Doctorates, Doctor of Philosophy School / High School: Stanford University 2015 to 2017

Skills

Algorithms • Signal Processing • C++ • C • Embedded Systems • Matlab • Digital Signal Processors • Software Engineering • Python • Unix • Physics • Image Processing • Mri • C# • Perl • Vhdl • Simulations • Deep Learning • Tensorflow

Languages

English

Interests

Skiing • Poker • Movie Goer • Running • Tennis • Beginning Piano

Industries

Computer Software

Resumes

Resumes

Carissa Lew Photo 1

Technical Lead And Architect

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Location:
2667 Villa Cortona Way, San Jose, CA 95125
Industry:
Computer Software
Work:
Apple
Technical Lead and Architect
Waveone, Inc.
Engineer
Magic Leap Dec 2015 - Oct 2017
Senior Embedded Algorithms - Lead Software Engineer
Waterbit, Inc. Dec 2015 - May 2016
Consultant
Lucid Vr Jul 2015 - Nov 2015
Firmware Engineer
Education:
Stanford University 2015 - 2017
Doctorates, Doctor of Philosophy Stanford University 2001 - 2007
Doctorates, Doctor of Philosophy, Electrical Engineering Stanford University 1998 - 2001
Master of Science, Masters, Electrical Engineering University of California, Berkeley 1994 - 1998
Bachelors, Bachelor of Science, Electrical Engineering
Skills:
Algorithms
Signal Processing
C++
C
Embedded Systems
Matlab
Digital Signal Processors
Software Engineering
Python
Unix
Physics
Image Processing
Mri
C#
Perl
Vhdl
Simulations
Deep Learning
Tensorflow
Interests:
Skiing
Poker
Movie Goer
Running
Tennis
Beginning Piano
Languages:
English

Publications

Us Patents

Methods And Systems For Threat Engagement Management

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US Patent:
8563908, Oct 22, 2013
Filed:
Dec 19, 2008
Appl. No.:
12/340495
Inventors:
Carissa E. Lew - Sunnyvale CA, US
Moses W. Chan - San Carlos CA, US
Paul-Andre Monney - Ely IA, US
Paul M. Romberg - San Jose CA, US
Leo J. Laux - Half Moon Bay CA, US
Assignee:
Lockheed Martin Corporation - Bethesda MD
International Classification:
G06F 17/10
F41G 7/20
F41G 9/00
G06F 17/00
F41G 7/00
US Classification:
244 31, 244 311, 244 315, 89 111, 702127, 702150, 702152
Abstract:
Sensor(s) may be used to detect threat data. A processing system and/or a method may be used to fuse the detected threat data over time. Threat data may comprise information on a munition, missile, rocket, or nuclear/biological/chemical (NBC) projectile or delivery system. Detected threat data may be processed to create a target track-lethality list comprising the locations of any target(s) and a ranking of their lethality in comparison to decoys or chaff. The target track-lethality list may be used to create a target engagement-track list that matches available threat elimination resources (e. g. interceptors) to targets with a weapon-to-target assignment module.

Machine-Learning Based Video Compression

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US Patent:
20200272903, Aug 27, 2020
Filed:
May 11, 2020
Appl. No.:
16/871418
Inventors:
- Mountain View CA, US
Sanjay Nair - Fremont CA, US
Carissa Lew - San Jose CA, US
Steve Branson - Woodside CA, US
Alexander Anderson - Mountain View CA, US
Lubomir Bourdev - Mountain View CA, US
International Classification:
G06N 3/08
H04N 19/182
H04N 19/517
G06K 9/00
G06K 9/62
Abstract:
An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.

Machine-Learning Based Video Compression

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US Patent:
20200036995, Jan 30, 2020
Filed:
Nov 7, 2018
Appl. No.:
16/183469
Inventors:
- Mountain View CA, US
Sanjay Nair - Fremont CA, US
Carissa Lew - San Jose CA, US
Steve Branson - Woodside CA, US
Alexander Anderson - Mountain View CA, US
Lubomir Bourdev - Mountain View CA, US
International Classification:
H04N 19/517
H04N 19/182
G06K 9/00
G06K 9/62
Abstract:
An encoder system trains a compression model that includes an autoencoder model and a frame extractor model. The encoding portion of the autoencoder is coupled to receive a set of target frames and a previous state tensor for the set of target frames and generate compressed code. The decoding portion of the autoencoder is coupled to receive the compressed code and the previous state tensor for the set of frames and generate a next state tensor for the set of target frames. The frame extractor model is coupled to receive the next state tensor and generate a set of reconstructed frames that correspond to the set of target frames by performing one or more operations on the state tensor. The state tensor for the set of frames includes information from frames of the video that can be used by the frame extractor to generate reconstructed frames.

Data Compression For Machine Learning Tasks

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US Patent:
20180174047, Jun 21, 2018
Filed:
Dec 15, 2017
Appl. No.:
15/844447
Inventors:
- Mountain View CA, US
Carissa Lew - San Jose CA, US
Sanjay Nair - Fremont CA, US
Oren Rippel - Mountain View CA, US
International Classification:
G06N 3/08
G06N 3/04
Abstract:
A machine learning (ML) task system trains a neural network model that learns a compressed representation of acquired data and performs a ML task using the compressed representation. The neural network model is trained to generate a compressed representation that balances the objectives of achieving a target codelength and achieving a high accuracy of the output of the performed ML task. During deployment, an encoder portion and a task portion of the neural network model are separately deployed. A first system acquires data, applies the encoder portion to generate a compressed representation, performs an encoding process to generate compressed codes, and transmits the compressed codes. A second system regenerates the compressed representation from the compressed codes and applies the task model to determine the output of a ML task.

Using Generative Adversarial Networks In Compression

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US Patent:
20180174052, Jun 21, 2018
Filed:
Dec 15, 2017
Appl. No.:
15/844449
Inventors:
- Mountain View CA, US
Lubomir Bourdev - Mountain View CA, US
Carissa Lew - San Jose CA, US
Sanjay Nair - Fremont CA, US
International Classification:
G06N 3/08
G06N 3/04
Abstract:
The compression system trains a machine-learned encoder and decoder through an autoencoder architecture. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder is coupled to receive content and output a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder is coupled to receive a tensor representing content and output a reconstructed version of the content. The compression system trains the autoencoder with a discriminator to reduce compression artifacts in the reconstructed content. The discriminator is coupled to receive one or more input content, and output a discrimination prediction that discriminates whether the input content is the original or reconstructed version of the content.

Autoencoding Image Residuals For Improving Upsampled Images

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US Patent:
20180174275, Jun 21, 2018
Filed:
Dec 15, 2017
Appl. No.:
15/844452
Inventors:
- Mountain View CA, US
Carissa Lew - San Jose CA, US
Sanjay Nair - Fremont CA, US
Oren Rippel - Mountain View CA, US
International Classification:
G06T 5/00
H04N 19/44
G06K 9/66
Abstract:
An enhanced encoder system generates residual bitstreams representing additional image information that can be used by an image enhancement system to improve a low quality image. The enhanced encoder system upsamples a low quality image and compares the upsampled image to a true high quality image to determine image inaccuracies that arise due to the upsampling process. The enhanced encoder system encodes the information describing the image inaccuracies using a trained encoder model as the residual bitstream. The image enhancement system upsamples the same low quality image to obtain a prediction of a high quality image that can include image inaccuracies. Given the residual bitstream, the image enhancement system decodes the residual bitstream using a trained decoder model and uses the additional image information to improve the predicted high quality image. The image enhancement system can provide an improved, high quality image for display.

Adaptive Compression Based On Content

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US Patent:
20180176578, Jun 21, 2018
Filed:
Dec 15, 2017
Appl. No.:
15/844424
Inventors:
- Mountain View CA, US
Lubomir Bourdev - Mountain View CA, US
Carissa Lew - San Jose CA, US
Sanjay Nair - Fremont CA, US
International Classification:
H04N 19/167
G06N 3/08
H04N 19/126
H04N 19/172
H04N 19/196
H04N 19/91
G06K 9/62
G06K 9/00
Abstract:
A compression system trains a machine-learned encoder and decoder. The encoder can be deployed by a sender system to encode content for transmission to a receiver system, and the decoder can be deployed by the receiver system to decode the encoded content and reconstruct the original content. The encoder receives content and generates a tensor as a compact representation of the content. The content may be, for example, images, videos, or text. The decoder receives a tensor and generates a reconstructed version of the content. In one embodiment, the compression system trains one or more encoding components such that the encoder can adaptively encode different degrees of information for regions in the content that are associated with characteristic objects, such as human faces, texts, or buildings.
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Carissa E Lew from Santa Clara, CA, age ~49 Get Report