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Somnath Banerjee, 61636 W Remington Dr, Sunnyvale, CA 94087

Known as:
Somnat Banerjee Banerjee Somnath
Related to:
Sheetal C Gavankar 55

Somnath Banerjee Phones & Addresses

636 Remington Ct, Sunnyvale, CA 94087    408-2459131    408-9624877   

636 W Remington Dr, Sunnyvale, CA 94087    408-2459131   

Cupertino, CA   

Miracle, KY   

Santa Clara, CA   

Plano, TX   

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Somnath Banerjee resumes & CV records

Resumes

Somnath Banerjee Photo 34

Chief Technology Officer

Location:
636 west Remington Dr, Sunnyvale, CA 94087
Industry:
Computer Software
Work:
Lodgiq
Chief Technology Officer Nor1 Jul 2014 - Oct 2015
Chief Technology Officer Nor1 Jun 2013 - Jul 2014
Vice President Engineering Sourcebits, Inc. Dec 2011 - Jun 2013
Chief Operations Officer Advanced Software Systems Nov 2008 - Nov 2011
Chief Technology Officer Mec Technologies Aug 2000 - Oct 2008
Principal Architect and Co-Founder Egain Corporation Jan 1999 - Aug 2000
Principal Architect Texas Instruments May 1988 - May 1996
Developer, Architect, Branch Manager
Education:
Southern Methodist University 1987 - 1988
Master of Science, Masters, Computer Science, Engineering, Computer Science and Engineering Indian Institute of Technology, Kharagpur 1981 - 1985
Skills:
Enterprise Software, Cloud Computing, Mobile Applications, Management, Agile Methodologies, Crm, Leadership, Software Project Management, Product Development, Business Intelligence, Software As A Service, Machine Learning, Deep Learning
Languages:
English
Bengali
Certifications:
Edx Verified Certificate For Introduction To Big Data With Apache Spark
Edx Verified Certificate For Scalable Machine Learning
Neural Networks and Deep Learning
Machine Learning Foundations: A Case Study Approach
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Structuring Machine Learning Projects
Image Understanding With Tensorflow on Gcp
Google Cloud Platform Big Data and Machine Learning Fundamentals
Edx
Somnath Banerjee Photo 35

Somnath Banerjee

Somnath Banerjee Photo 36

Somnath Banerjee

Location:
United States
Somnath Banerjee Photo 37

Somnath Banerjee

Location:
United States

Publications & IP owners

Us Patents

Multi-Layered Market Forecast Framework For Hotel Revenue Management By Continuously Learning Market Dynamics

US Patent:
2021012, Apr 29, 2021
Filed:
Feb 12, 2020
Appl. No.:
16/788317
Inventors:
SOMNATH BANERJEE - sunnyvale CA, US
RIMO DAS - sunnyvale CA, US
HARSHINDER CHADHA - hayward CA, US
KURIEN JACOB - new york NY, US
International Classification:
G06Q 30/02
G06Q 50/12
G06Q 10/04
G06F 16/25
G06N 20/00
Abstract:
In one aspect, a computerized method for implementing multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics includes the step of collecting a set of data from various relevant providers, wherein the set of data comprises market data, events information relevant to a market, and market pricing. The method includes the step of implementing an extract, transform, load (ETL) operations on the set of data, wherein the ETL comprises the ingestion of the multi-textured data into big data storage for use on demand basis. The method includes the step of implementing one or more specified data cleaning operations on the set of data. The method includes the step of implementing one or more specified feature engineering operations on the cleaned data. The method includes the step of generating an Average daily rate (ADR) training data set. The method includes the step of generating an occupancy training data set. The method includes the step of building an ADR model using the ADR training data. The method includes the step of building the occupancy model using the occupancy training data. The method includes the step of, with the ADR model and the occupancy model, generating a prediction data set. The method includes the step of, with the prediction data set, generating a forecast for a specified set of rates for a specific hotel. The method includes the step of, with the accuracy trackers, evaluating the multi-layered market forecaster and update the multi-layered market forecaster model to ensure its accuracy.

Multi-Layered System For Heterogeneous Pricing Decisions By Continuously Learning Market And Hotel Dynamics

US Patent:
2021011, Apr 15, 2021
Filed:
Sep 23, 2020
Appl. No.:
17/029036
Inventors:
SOMNATH BANERJEE - SUNNYVALE CA, US
RIMO DAS - SUNNYVALE CA, US
KURIEN JACOB - NEW YORK NY, US
HARSHINDER CHADHA - HAYWARD CA, US
International Classification:
G06Q 30/02
G06F 16/25
G06N 20/00
Abstract:
In one aspect, a computerized method for implementing a multi-layered system for heterogeneous pricing decisions by continuously learning market and hotel dynamics includes the step of collecting a set of data from relevant providers. The set of data from relevant providers comprises hotel data, competitor data, market data, and market pricing data. The method includes the step of implementing an ETL operation on the set of data from relevant providers. The ETL operation comprises an ingestion of the multi-textured data into a big data storage for use on an on-demand basis. The method includes the step of implementing one or more specified data cleaning operations on the dataset to generate a cleaned data set. The method includes the step of implementing one or more specified feature engineering operations on the cleaned data set. The method includes the step of generating an evaluation set and prediction set. The method includes the step of generating training and test dataset from the evaluation set. The method includes the step of building a price-sensitive demand model using the training data. The method utilizes a machine-learning gradient boosting framework and Expectation-Maximization algorithm to build the price-sensitive demand model. The method includes the step of measuring the accuracy of the model using the test data. The method includes the step of optimizing a price for a specified set of dates for a specific hotel using the prediction dataset. The method includes the step of providing a price-sensitive demand model.

Systems And Methods Of Advertisement Creatives Optimization

US Patent:
2018005, Feb 22, 2018
Filed:
Aug 17, 2016
Appl. No.:
15/239531
Inventors:
- Bentonville AR, US
Somnath Banerjee - Foster City CA, US
Assignee:
WAL-MART STORES, INC. - Bentonville AR
International Classification:
G06Q 30/02
Abstract:
Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving a plurality of advertisement creatives for an advertisement campaign, generating first predefined frequency weights for the plurality of advertisement creatives, and coordinating a display of the plurality of advertisement creatives within the impression slot of the webpages displayed to the online users based on the first predefined frequency weights. The first predefined frequency weights include a weighted frequency that each advertisement creative of the plurality of advertisement creatives should be displayed to the online users. A first advertisement creative of the can be displayed more frequently than a second advertisement creative because the first advertisement comprises a first frequency weight that is higher than a second frequency weight of the second advertisement creative.

System And Method For Building A Targeted Audience For An Online Advertising Campaign

US Patent:
2016022, Aug 4, 2016
Filed:
Jan 30, 2015
Appl. No.:
14/609652
Inventors:
- Bentonville AR, US
Nikhil Raj - Menlo Park CA, US
Somnath Banerjee - Foster City CA, US
Gary Tang - San Francisco CA, US
Zachary Poley - San Bruno CA, US
Yun Zhang - San Bruno CA, US
Galana Gebisa - Sunnyvale CA, US
Robert Bartoszynski - South San Francisco CA, US
Chung-Wei Yen - Sunnyvale CA, US
International Classification:
G06Q 30/02
Abstract:
A system for building a targeted audience for a present online advertising campaign is disclosed. The system comprises a database for storing data related to each of the plurality of products with each product associated with a previous online advertising campaign and a processor in communication with the database and configured to execute computer-readable instructions causing the processor to utilize the data to identify at least one previous online advertising campaign as being similar to the present online advertising campaign, learn from the identified previous online advertising campaign(s) to predict a probability of conversion of each of a plurality of customers when exposed to an impression of the present online advertising campaign, and build the targeted audience for the present online advertising campaign based on the predicted probability of conversion. A method for building a targeted audience for a present online advertising campaign is also disclosed.

Possible Relatives

Deb K Banerjee  Debasish K Banerjee  Devraj Banerjee  Dilip S Banerjee  Duke Banerjee  Abhijit Banerjee  Abhishek Banerjee  Somnath Roy  Somnath Pal  Somnath Das 

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