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Vishal S Vaingankar

from Kensington, CA
Age ~46
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Also known as:
Vishal B Vaingankar, Vishal R
Related to:
Puneet Vaingankar
Connected to:
Leon M Vainikos, 66Tuukka T Vainio, 53Ana R Vainisi, 73James Vainisi, 57Aldona A Vainius, 47Jennifer R Vaino, 44Alan F Vainrib, 43Alexander Vainstein, 51Natalia Vainstein, 56Tony Vainuku, 50

Vishal Vaingankar Phones & Addresses

  • Kensington, CA
  • San Francisco, CA
  • Belmont, CA
  • South Pasadena, CA
  • Hicksville, NY
  • Alhambra, CA
  • Los Angeles, CA
  • Redondo Beach, CA
  • Menlo Park, CA

Work

Company: Cruise automation 2017 to May 2020 Position: Engineering manager, data science

Education

Degree: Doctorates, Doctor of Philosophy School / High School: University of Southern California 2004 to 2011 Specialities: Neuroscience

Skills

Machine Learning • Data Mining • Predictive Modeling • Statistical Modeling • Mapreduce • Algorithms • Big Data • Information Retrieval • R • Hadoop • Data Visualization • Artificial Intelligence • Web Analytics • Pattern Recognition • Natural Language Processing

Industries

Internet

Resumes

Resumes

Vishal Vaingankar Photo 1

Vishal Vaingankar

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Location:
Berkeley, CA
Industry:
Internet
Work:
Cruise Automation 2017 - May 2020
Engineering Manager, Data Science
Zendrive Sep 1, 2015 - May 2017
Senior Data Scientist
Google Dec 2013 - Mar 2015
Advanced Technology and Projects
Stumbleupon Jun 2011 - Dec 2013
Senior Data Scientist
University of Southern California May 2005 - May 2011
Phd Student
Education:
University of Southern California 2004 - 2011
Doctorates, Doctor of Philosophy, Neuroscience Rochester Institute of Technology 2001 - 2004
Master of Science, Masters, Computer Science University of Mumbai
Bachelor of Engineering, Bachelors, Computer Science
Skills:
Machine Learning
Data Mining
Predictive Modeling
Statistical Modeling
Mapreduce
Algorithms
Big Data
Information Retrieval
R
Hadoop
Data Visualization
Artificial Intelligence
Web Analytics
Pattern Recognition
Natural Language Processing

Publications

Us Patents

Identifying A Route For An Autonomous Vehicle Between An Origin And Destination Location

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US Patent:
20210325883, Oct 21, 2021
Filed:
Jun 30, 2021
Appl. No.:
17/364761
Inventors:
- San Francisco CA, US
Nimish Patil - Pleasanton CA, US
Vishal Suresh Vaingankar - Kensington CA, US
Laura Athena Freeman - San Francisco CA, US
International Classification:
G05D 1/00
G05D 1/02
Abstract:
Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.

Identifying A Route For An Autonomous Vehicle Between An Origin And Destination Location

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US Patent:
20200301419, Sep 24, 2020
Filed:
Mar 19, 2019
Appl. No.:
16/358206
Inventors:
- San Francisco CA, US
Nimish Patil - Pleasanton CA, US
Vishal Suresh Vaingankar - Kensington CA, US
Laura Athena Freeman - San Francisco CA, US
International Classification:
G05D 1/00
G05D 1/02
Abstract:
Described herein are technologies relating to computing a likelihood of an operation-influencing event with respect to an autonomous vehicle at a geographic location. The likelihood of the operation-influencing event is computed based upon a prediction of a value that indicates whether, through a causal process, the operation-influencing event is expected to occur. The causal process is identified by means of a model, which relates spatiotemporal factors and the operation-influencing events.

Autonomous Vehicle Routing Based Upon Risk Of Autonomous Vehicle Takeover

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US Patent:
20200264605, Aug 20, 2020
Filed:
Feb 20, 2019
Appl. No.:
16/280599
Inventors:
- San Francisco CA, US
Vishal Suresh Vaingankar - Kensington CA, US
Antony Joseph - San Francisco CA, US
Sean Gregory Skwerer - San Francisco CA, US
Lucio Otavio Marchioro Rech - San Mateo CA, US
Nitin Kumar Passa - San Francisco CA, US
Laura Athena Freeman - San Francisco CA, US
George Herbert Hines - Kensington CA, US
International Classification:
G05D 1/00
G05D 1/02
G01C 21/20
G01C 21/34
Abstract:
Various technologies described herein pertain to routing an autonomous vehicle based upon risk of takeover of the autonomous vehicle by a human operator. A computing system receives an origin location and a destination location of the autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a computer-implemented model. The computer-implemented model is generated based upon labeled data indicative of instances in which autonomous vehicles are observed to transition from operating autonomously to operating based upon conduction by human operators while the autonomous vehicles are executing predefined maneuvers. The computer-implemented model takes, as input, an indication of a maneuver in the predefined maneuvers that is performed by the autonomous vehicle when the autonomous vehicle follows a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.

Autonomous Vehicle Routing Based Upon Spatiotemporal Factors

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US Patent:
20200264619, Aug 20, 2020
Filed:
Feb 20, 2019
Appl. No.:
16/280415
Inventors:
- San Francisco CA, US
Vishal Suresh Vaingankar - Kensington CA, US
Laura Athena Freeman - San Francisco CA, US
International Classification:
G05D 1/02
G01C 21/20
G01C 21/34
G05D 1/00
G01C 21/36
Abstract:
Various technologies described herein pertain to routing autonomous vehicles based upon spatiotemporal factors. A computing system receives an origin location and a destination location of an autonomous vehicle. The computing system identifies a route for the autonomous vehicle to follow from the origin location to the destination location based upon output of a spatiotemporal statistical model. The spatiotemporal statistical model is generated based upon historical data from autonomous vehicles when the autonomous vehicles undergo operation-influencing events. The spatiotemporal statistical model takes, as input, a location, a time, and a direction of travel of the autonomous vehicle. The spatiotemporal statistical model outputs a score that is indicative of a likelihood that the autonomous vehicle will undergo an operation-influencing event due to the autonomous vehicle encountering a spatiotemporal factor along a candidate route. The autonomous vehicle then follows the route from the origin location to the destination location.
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Vishal S Vaingankar from Kensington, CA, age ~46 Get Report