Satyen Abrol

Experience

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  University of Texas at Dallas

  Aug 2008 - May 2013

  Research Assistant

  Predicting location of a user in an Online Social Network (OSN):* Understanding the relationship geospatial proximity and friendship* Instance based classification using k-nearest neighbors with variable depth* Application of semi-supervised machine learning algorithms such as label propagation for predicting labels (locations).* Identifying social cliques to predict most recent location of user.Other Projects:* Developed TWinner, for predicting the news intent of the user and extract n words to enhance the location based user query for better search results.* Developed MapIt, a GIS web application tool for identification and disambiguation of location of the apartment from unstructured ads.Software/Prototypes Developed:* Vedanta: A powerful security analysis tool that builds a parallel profile (actual and psychological) for Twitter users by mining/predicting information such as location, age, interests, ethnicity, etc. and integrating several social networks such as LinkedIn, Google+, Foursquare.* MapIt: A tool which displays the Craigslist apartment listings on Google maps. MapIt then integrates this functionality with the information collected from location based extraction of various web sources such as the city police blotter which makes apartment searching simpler and faster, helping the user to make a better decision. Show less

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  VMware

  May 2012 - Aug 2012

  Member Technical Staff-Intern Performance Group

  Designing and implementation of modules for storage, indexing and querying of performance data. Also built pattern recognition modules for detecting performance regression, outlier detection, step detection and correlation calculation.

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  VMware

  Jun 2013 - Jul 2016

  - Designing machine learning algorithms for predicting customer behavior/usage of product using telemetry data.- Implementation of distributed machine learning algorithms using Apache Spark for identifying frequent log pattern, co-relations between metrics and automated regression detection.- Categorization of customer usage of product, based on storing and mining of terabytes of logs using an ElasticSearch cluster Performance Group (R&D) - vCenter Server Performance

  • Senior Member of Technical Staff

    Jan 2015 - Jul 2016

  • Member of Technical Staff

    Jun 2013 - Dec 2014
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  Samsung Electronics

  Jul 2016 - Jan 2020

  User Profiling and Personalization Platform- Developing scalable ML algorithms and production pipelines to predict smartphone users’ demographics and interests based on app usage, geo location, browsing behavior, music/video usage.- Using Profiles for personalization of Samsung apps such as MyGalaxy.- Using look alike modeling for Customer Driven Marketing to drive Incremental smartphone sales. Building User Profile Based on Smartphone App Usage- Creating spatio-temporal profiles (home, work, frequent routes, etc.) for individual users using agglomerative clustering, sequence mining techniques.- Predicting demographics related attributes such as income group, based on user's geo-location, app usage behavior, etc.- Porting of algorithms to Apache Spark to achieve scalability(process millions of users).

  • Senior Staff Data Scientist

    Feb 2018 - Jan 2020

  • Staff Data Scientist

    Jul 2016 - Feb 2018
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  Rakuten

  Jan 2020 - now

  At Rakuten, I lead two teams across two departments (Research and Data Science):- Customer Social Graph is a Rakuten research (RIT) project focused on leveraging graph embedding methods to predict relationships between users in the absence of ground truth. Social Graph is used to improve Rakuten's customer understanding and for downstream tasks like marketing campaigns and recommendations across businesses such as Ichiba (ecommerce), Mobile and Fintech.- Audience Sciences team is building a sciences framework for marketers for understanding business objectives and recommending which users to target, how to target (channel and Rakuten service) and when to target. Our current focus is on new user acquisition, reactivation, retention and churn prevention. Show less

  • Senior Director - Research (Data Science)

    Apr 2024 - now

  • Director - Research (Data Science)

    Jan 2022 - Apr 2024

  • Principal Research Scientist

    Jan 2020 - Dec 2021
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  Glance

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  Vice President - Machine Learning