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Mayank Shrivastava

from Woodinville, WA
Age ~34
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Also known as:
Mayank Srivastava, Mayank A
Related to:
Trusha J ParekhAbhishek Srivastava, 36Samanvaya Srivastava, 39Rahul Shrivastava, 40Anshumali Shrivastava, 39
Connected to:
Apoorva Shrivastava, 45Arpita Shrivastava, 40Chetan Shrivastava, 39Daya S Shrivastava, 49Gajendra P Shrivastav, 69Nita Shrivastava, 61Ravi D Shrivastava, 47Ritesh Shrivastava, 43Ruchir Shrivastava, 43Sangita K Shrivastav, 62

Mayank Shrivastava Phones & Addresses

  • Woodinville, WA
  • Bellevue, WA
  • Kirkland, WA
  • Ithaca, NY

Resumes

Resumes

Mayank Shrivastava Photo 1

Principal Applied Science Manager, Business 360 Ai, Business Applications Group

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Location:
Bellevue, WA
Industry:
Computer Software
Work:
Microsoft
Principal Applied Science Manager, Business 360 Ai, Business Applications Group
Indian Institute of Technology, Kharagpur Jul 2012 - May 2013
Student
Cornell University Apr 2012 - Jul 2012
Intern
Ibm May 2010 - Jul 2010
Intern
Education:
Indian Institute of Technology, Kharagpur 2015 - 2020
Bachelors, Bachelor of Technology Indian Institute of Technology, Kharagpur 2008 - 2013
Bachelors, Bachelor of Technology, Computer Science Indian Institute of Technology
Skills:
C
C++
Algorithms
Programming
Machine Learning
Java
Software Development
Computer Science
Data Structures
Databases
C#
Data Mining
Distributed Systems
Artificial Intelligence
Latex
Eclipse
Mayank Shrivastava Photo 2

Mayank Shrivastava

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Publications

Us Patents

Topic Set Refinement

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US Patent:
20190392078, Dec 26, 2019
Filed:
Jun 22, 2018
Appl. No.:
16/016352
Inventors:
- Redmond WA, US
Mayank SHRIVASTAVA - Bellevue WA, US
Pushpraj SHUKLA - Dublin CA, US
Jonas BARKLUND - Seattle WA, US
Dario VIGNUDELLI - Bellevue WA, US
Ipolitas Clinton DUNARAVICH - Seattle WA, US
International Classification:
G06F 17/30
Abstract:
A computing system including one or more processors generates a topic set for a domain. A taxonomic evaluator is executed by the one or more processors to evaluate a set of category clusters generated from domain-specific textual data against a domain-specific taxonomic tree based on a coherency condition and to identify the category clusters that satisfy the coherency condition. The domain-specific taxonomic tree is generated from hierarchical structures of documents relating to the domain. Each identified category cluster is labeled with a label. A topic set creator is executed by the one or more processors to insert the labels of the set of identified category clusters into the topic set for the domain.

Purchase Analytics Derived From A Consumer Decision Journey Model

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US Patent:
20190005540, Jan 3, 2019
Filed:
Jun 30, 2017
Appl. No.:
15/638880
Inventors:
- REDMOND WA, US
Gunyoung HAN - BELLEVUE WA, US
Supratim Roy CHAUDHURY - SAMMAMISH WA, US
Karthikeyan ASOKKUMAR - BELLEVUE WA, US
Apurv PANT - REDMOND WA, US
Walter SUN - BELLEVUE WA, US
Paul Joseph APODACA - MERCER ISLAND WA, US
Mayank SHRIVASTAVA - KIRKLAND WA, US
International Classification:
G06Q 30/02
Abstract:
Systems, methods, computer storage media, and user interfaces are provided for providing analytics tools derived from a consumer decision journey model. Once the consumer decision journey for a particular good or service is constructed, a series of tools is provided to help a user understand the return on investment for providing different types of multimedia content at different stages in the consumer decision journey for a particular demographic. To do so, an interface is provided to the user that enables the user to select a desired tool and features of the tool the user wishes to exploit. Browser history from a plurality of consumers is transformed into a visual representation that provides insights into the types of multimedia content that can provide the greatest return on investment for a particular demographic at a particular state in the consumer decision journey for the selected category of goods or services.

Predictive Modeling Across Multiple Horizons Combining Time Series & External Data

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US Patent:
20170220939, Aug 3, 2017
Filed:
Jun 9, 2016
Appl. No.:
15/178445
Inventors:
- Redmond WA, US
Amita Surendra Gajewar - Sunnyvale CA, US
Debraj GuhaThakurta - Bellevue WA, US
Konstantin Golyaev - Lake Forest Park WA, US
Mayank Shrivastava - Kirkland WA, US
Vijay Krishna Narayanan - Mountain View CA, US
Walter Sun - Bellevue WA, US
International Classification:
G06N 5/04
G06N 99/00
Abstract:
A multi-horizon predictor system that predicts a future parameter value for multiple horizons based on time-series data of the parameter, external data, and machine-learning. For a given time horizon, a time series data splitter splits the time into training data corresponding to a training time period, and a validation time period corresponding to a validation time period between the training time period and the given horizon. A model tuner tunes the prediction model of the given horizon fitting an initial prediction model to the parameter using the training data thereby using machine learning. The model tuner also tunes the initial prediction model by adjusting an effect of the external data on the prediction to generate a final prediction model for the given horizon using the validation data. A multi-horizon predictor causes the time series data splitter and the model tuner to operate for each of multiple horizons.
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Mayank Shrivastava from Woodinville, WA, age ~34 Get Report