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Vy T Vo

from Portland, OR
Age ~32
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Also known as:
Thuyvy Vo, Y Vo
Related to:
Jasmine NguyenHoan V Nguyen, 49Randy Le, 29Tu Nguyen, 53Jessica L Pham, 32Thuy L Tran, 46Quoc P Le
Connected to:
Alice T Vo, 47Alisha L Vo, 34Amy Vo, 43Anh Vo, 50Anhthu T Vo, 60Anita N Vo, 54Ann Vo, 65Annie Vo, 48Bau T Vo, 71Bond V Vo, 42

Vy Vo Phones & Addresses

  • Portland, OR

Resumes

Resumes

Vy Vo Photo 1

Design Director At Vu Vy Co.,Ltd

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Location:
Viet Nam
Industry:
Apparel & Fashion
Vy Vo Photo 2

Research Scientist

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Location:
12240 southwest Edgewood St, Portland, OR 97225
Industry:
Research
Work:
Intel Labs
Research Scientist
University of California, San Diego
Phd Researcher
University of Rochester Jul 2011 - Jul 2013
Research Assistant For Kid Neurolab With Jessica F Cantlon
Swarthmore College Aug 2009 - Jun 2011
Student Researcher
Usc Viterbi School of Engineering Jun 2009 - Aug 2009
Student Researcher
Education:
Uc San Diego 2013 - 2019
Doctorates, Doctor of Philosophy, Philosophy, Neuroscience Swarthmore College 2007 - 2011
Bachelors, Bachelor of Arts, Biology, Cognitive Science Oxford Academy 2007
Skills:
Research
Statistics
Data Analysis
Matlab
Python
R
Languages:
English
Spanish
Vietnamese
Vy Vo Photo 3

Medical Nail Technician

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Work:
Love Nails
Medical Nail Technician
Vy Vo Photo 4

Vy Vo

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Publications

Us Patents

Technology To Generalize Safe Driving Experiences For Automated Vehicle Behavior Prediction

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US Patent:
20210001884, Jan 7, 2021
Filed:
Jun 27, 2020
Appl. No.:
16/914298
Inventors:
- Santa Clara CA, US
Vy Vo - Portland OR, US
Javier Felip Leon - Hillsboro OR, US
Javier Perez-Ramirez - North Plains OR, US
Javier Sebastian Turek - Beaverton OR, US
Mariano Tepper - Portland OR, US
David Israel Gonzalez Aguirre - Hillsboro OR, US
International Classification:
B60W 60/00
G06N 3/08
G06N 5/02
G01C 21/34
B60W 30/095
B60W 40/06
B60W 30/09
Abstract:
Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.

Self-Supervised Learning System For Anomaly Detection With Natural Language Processing And Automatic Remediation

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US Patent:
20200364107, Nov 19, 2020
Filed:
Jun 27, 2020
Appl. No.:
16/914300
Inventors:
- Santa Clara CA, US
Vy Vo - Portland OR, US
Javier Perez-Ramirez - North Plains OR, US
Marocs Carranza - Portland OR, US
Mateo Guzman - Beaverton OR, US
Dario Oliver - Hillsboro OR, US
International Classification:
G06F 11/07
G06F 40/279
G06N 3/02
Abstract:
Systems, apparatuses and methods may provide for technology that identifies a sequence of events associated with a computer architecture, categorizes, with a natural language processing system, the sequence of events into a sequence of words, identifying an anomaly based on the sequence of words and triggering an automatic remediation process in response to an identification of the anomaly.

Technology To Handle Ambiguity In Automated Control Systems

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US Patent:
20200326696, Oct 15, 2020
Filed:
Jun 26, 2020
Appl. No.:
16/913845
Inventors:
- Santa Clara CA, US
Todd Anderson - Hillsboro OR, US
Vy Vo - Portland OR, US
Javier Felip Leon - Hillsboro OR, US
Javier Perez-Ramirez - North Plains OR, US
International Classification:
G05B 23/02
G05B 13/02
Abstract:
Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.

System To Analyze And Enhance Software Based On Graph Attention Networks

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US Patent:
20200326934, Oct 15, 2020
Filed:
Jun 26, 2020
Appl. No.:
16/913756
Inventors:
- Santa Clara CA, US
Bryn Keller - Vancouver WA, US
Mihai Capota - Portland OR, US
Vy Vo - Portland OR, US
Nesreen Ahmed - Santa Clara CA, US
Theodore Willke - Portland OR, US
International Classification:
G06F 8/75
G06N 3/04
G06N 3/08
Abstract:
Systems, apparatuses and methods may provide for technology that generates a dependence graph based on a plurality of intermediate representation (IR) code instructions associated with a compiled program code, generates a set of graph embedding vectors based on the plurality of IR code instructions, and determines, via a neural network, one of an analysis of the compiled program code or an enhancement of the program code based on the dependence graph and the set of graph embedding vectors. The technology may provide a graph attention neural network that includes a recurrent block and at least one task-specific neural network layer, the recurrent block including a graph attention layer and a transition function. The technology may also apply dynamic per-position recurrence-halting to determine a number of recurring steps for each position in the recurrent block based on adaptive computation time.
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Vy T Vo from Portland, OR, age ~32 Get Report