“Mohammad is an excellent visionary leader and a bridge to the future of analytics. He has been pushing the machine learning frontier and helped others do so. He built a world-class Data Science team at Workday from scratch. He has unique ability to explain complex problems with simple words and connect the dots between data science, business effectiveness and actionable results. He is constantly at the leading edge of analytics tools and approaches, and will carry any organization he is a part of forward with him.”
About
I am a visionary technologist with over twenty years of Engineering, AI and Data…
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The Claims Organization is undergoing a major business and tech transformation, and my team is hiring Software Engineers across multiple levels, with…
The Claims Organization is undergoing a major business and tech transformation, and my team is hiring Software Engineers across multiple levels, with…
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KDD 2024 (https://kdd2024.kdd.org/) is set to begin this Sunday, August 25. The best minds in AI, Machine Learning, and Data Science from across…
KDD 2024 (https://kdd2024.kdd.org/) is set to begin this Sunday, August 25. The best minds in AI, Machine Learning, and Data Science from across…
Liked by Mohammad Sabah
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Join and be a part of the transformation at GEICO engineering.
Join and be a part of the transformation at GEICO engineering.
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Experience
Education
Patents
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Smart Model Selection for Personalization
Issued US 11876869
Embodiments of the present disclosure provide for improved management, selection, and provision of network asset data objects. For example, embodiments described throughout are configured to score various network assets corresponding to real-world assets, items, products, and/or the like, for selection and provision associated with one or more particular user profile identifiers. Embodiments are configured to train network asset scoring model(s) based on specific data, such as prioritized…
Embodiments of the present disclosure provide for improved management, selection, and provision of network asset data objects. For example, embodiments described throughout are configured to score various network assets corresponding to real-world assets, items, products, and/or the like, for selection and provision associated with one or more particular user profile identifiers. Embodiments are configured to train network asset scoring model(s) based on specific data, such as prioritized network asset data set(s), such that the trained network asset scoring model(s) efficiently generate more accurate, improved scores for network asset data objects. Additional or alternative embodiments are configured to provide data tagged network asset set(s) utilizing specially trained model(s), such as one or more multi-armed bandit models and/or one or more network asset scoring model(s).
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Guided Shopping Quiz-driven Personalization
Issued US 11631106
Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and…
Embodiments of the present disclosure provide mechanisms for selection of a user survey data object from a set of user data objects, and processing of survey engagement data associated with a selected user survey data object. The user survey data object selected is appropriate for providing associated with a particular user data object, and the survey engagement data received associated therewith enables programmatic selection and use of particular ranking model(s) for use in generating and providing an output ranked item data object set. Example embodiments utilize selected ranking model(s) of a set of ranking models to programmatically generate and output an output ranked item data object set for a particular user profile.
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Multi-armed bandit approach to product personalization
Issued US 11290564
Embodiments are provided that facilitate training and utilization of specially configured multi-armed bandit model(s). Such embodiments configure multi-armed bandit model(s) to provide selected network asset scoring model(s) from a network asset scoring model set and/or a selected data tagged network asset set from a set of data tagged network asset sets. Such selections are performable for a particular user profile, for example to perform accurate and/or efficient process(es) for provision of…
Embodiments are provided that facilitate training and utilization of specially configured multi-armed bandit model(s). Such embodiments configure multi-armed bandit model(s) to provide selected network asset scoring model(s) from a network asset scoring model set and/or a selected data tagged network asset set from a set of data tagged network asset sets. Such selections are performable for a particular user profile, for example to perform accurate and/or efficient process(es) for provision of network asset data object(s) to the user profile(s). Some embodiments access enterprise network interaction data, access a set of data tagged network asset sets, train a multi-armed bandit model to provide a selected network asset scoring model and a selected data tagged network asset set, and provides one or more network asset data objects based on the selected
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Retention risk determiner
Issued US 10521748
A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset…
A system for determining retention risk comprises a grouper, a filter, a normalizer, a feature vector extractor, a model builder, and a predictor. The grouper is for determining a set of time series of transactions where each is associated with one employee. The filter is for filtering the set of time series of transactions based on an employee transition characteristic to determine a subset of time series. The normalizer is for determining a model set of time series by normalizing the subset of time series. The feature vector extractor is for determining a set of feature vectors determined from a time series of the model set of time series. The model builder is for determining one or more models based at least in part on the set of feature vectors. The predictor is for predicting retention risk for a given employee using the one or more models.
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A novel Address parsing system
Issued US 10366159
A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components…
A system for identifying address components includes an interface and a processor. The interface is to receive an address for parsing. The processor is to determine a matching model of a set of models based at least in part on a matching probability for each model for a tokenized address, which is based on the address for parsing, and associate each component of the tokenized address with an identifier based at least in part on the matching model, wherein each component of the set of components is associated with an identifier, and wherein probabilities of each component of the set of components are determined using training addresses.
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Method for learning a latent interest taxonomy from multimedia metadata
Issued US 9805098
Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to…
Techniques are disclosed herein for learning latent interests based on metadata of one or more images. An analysis tool associates one or more attributes with each of the objects based on a time and a location described in the metadata of that object. Each of the attributes describes one of a plurality of locations or an event scheduled to occur at one or more of the plurality of locations. The analysis tool identifies one or more concepts from a distribution of the one or more attributes to each of the objects. Each of the one or more concepts includes at least a first attribute in the distribution that co-occurs with a second attribute in the distribution.
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Method for Inferring Latent User Interests using Multimedia
Issued US 9798980
Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept…
Techniques disclosed herein describe inferring user interests based on metadata of a plurality of multimedia objects captured by a plurality of users. An analysis tool receives, for each of the users, metadata describing each multimedia object in the plurality of objects associated with that user. Each multimedia object includes one or more attributes imputed to that object based on the metadata. The analysis tool identifies one or more concepts from the one or more attributes. Each concept includes at least a first attribute that co-occurs with a second attribute imputed to a first multimedia object. The analysis tool associates a first one of the plurality of users with at least one of the concepts based on the attributes imputed to multimedia objects associated with the first one of the plurality of users.
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Retention Risk Mitigation System
Issued US 20160180291
A system for rating job transitions includes a probability determiner for determining a set of probabilities, a grouper for determining a group of job transition histories, a filter for determining a subset of job transition histories from the group of job transition histories by filtering based at least in part on a transition characteristic, a normalizer for determining a model set of job transition histories by normalizing the subset of job transition histories, a feature vector extractor…
A system for rating job transitions includes a probability determiner for determining a set of probabilities, a grouper for determining a group of job transition histories, a filter for determining a subset of job transition histories from the group of job transition histories by filtering based at least in part on a transition characteristic, a normalizer for determining a model set of job transition histories by normalizing the subset of job transition histories, a feature vector extractor for determining a set of feature vectors using the model set of job transition histories, a model builder for determining a model based at least in part on the set of feature vectors, and a rater for rating potential job transitions of a selected employee based on the model using a set of test feature vectors.
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Personalization through Relationship-based searches and recommendations
Issued US 20140101142
Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may…
Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.
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Personalization through relationship-based plays and recommendations
Issued US 20140101192
Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may…
Techniques are described for determining relationships between user activities and determining search results and content recommendations based on the relationships. A plays-related-to-searches application may determine a relationship score between plays of a media title and searches of a query by determining a distance between a projection of the search onto the space of the users and a projection of plays of the media title onto the space of the users. A plays-after-searches application may determine a score for plays of the streaming media title given the search by multiplying a number of times plays of the media title occur after the query is entered by the number of times any play occurs, and dividing by a product of the number of times plays of the media title occur after any query is entered and the number of times plays of any media title occur after the query is entered.
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System and Method for Determining Semantically Related Terms
Issued US 20090198684
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage…
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.
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System and method for determining semantically related terms
Issued US 7548929
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage…
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.
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System and method for determining semantically related terms
Issued US 20070027864
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage…
The present disclosure is directed to systems and methods for determining semantically related terms. Generally, one or more seed terms are received from a user. A system searches a first index comprising a plurality of terms and one or more webpages associated with each term of the plurality of terms to determine a plurality of webpages associated with the seed terms. The system then searches a second index comprising a plurality of webpages and one or more terms associated with each webpage of the plurality of webpages to determine a plurality of potential terms associated with the plurality of webpages associated with the seed terms. At least one term of the plurality of potential terms is suggested to a user.
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Urdu
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Arabic
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We are hiring at all levels for our Claims Engineering org and looking for technical leaders who are passionate to lead technological transformations…
We are hiring at all levels for our Claims Engineering org and looking for technical leaders who are passionate to lead technological transformations…
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