Accenture
Principal Research Scientist, Artificial Intelligence
Syracuse University Sep 2014 - May 2016
Graduate Research Assistant - School of Information Studies
Disney Abc Television Group Jun 2015 - Aug 2015
Data Engineering Intern
Ups Oct 2012 - Aug 2014
Operations Supervisor
Tutor Doctor Sep 2010 - Jun 2014
Homeschool Teacher and Private Tutor
Education:
Syracuse University 2014 - 2016
Master of Science, Masters, Management
University of North Carolina at Chapel Hill 2006 - 2010
Bachelors, Bachelor of Science, Applied Science, Biomedical Engineering
Lorenzo De' Medici School 2008
Skills:
Sql Python Machine Learning Data Analysis R Hadoop Scikit Learn Microsoft Office Natural Language Processing Leadership Apache Spark Git Powerpoint Bash Flask Microsoft Excel Microsoft Word Team Leadership Microsoft Powerpoint Hive Data Visualization Tableau Visio Access Talend Illustrator Microsoft Sql Server Solidworks Ni Labview Comsol
Interests:
Health
Certifications:
The Data Incubator, License 23476 License 23476 Program Graduate
- Dublin, IE Guruprasad Dasappa - Bangalore, IN Krishna Kummamuru - Bangalore, IN Colin Connors - Campbell CA, US Guanglei Xiong - Pleasanton CA, US Christopher Banschbach - Wilmington DE, US Thomas Michael Fahey - Wynnewood PA, US
Examples of cognitive procurement and proactive continuous sourcing are defined. In an example, the system receives a procurement request. The system implements an artificial intelligence component to sort the supplier data into a plurality of domains. The system modifies a domain from the plurality of data domains based on new supplier data being received. The system generates user procurement behavior data based on the procurement interaction and a domain from the plurality of data domains. The system establishes a user procurement behavior model corresponding to a guideline associated with the procurement interaction. The system determines whether the user procurement behavior model should be updated based on modification in the plurality of data domains and updates the same. The system notifies the user regarding change in the user procurement behavior model due to change in a domain of the received supplier data selected by the user.
- Dublin 4, IE Emmanuel Munguia Tapia - San Jose CA, US Jingyun Fan - Berkeley CA, US Danielle Moffat - Highlands Ranch CO, US Colin Connors - Campbell CA, US Kayhan Moharreri - San Jose CA, US
Assignee:
Accenture Global Solutions Limited - Dublin 4
International Classification:
G06Q 30/06 G06Q 10/08 G06Q 10/06 G06F 15/18
Abstract:
Examples of automated order troubleshooting are described. In an example embodiment, sales-specific data sources associated with at least one of a process, an organization, and an industry relevant for sales operations are monitored. From the monitored sales-specific data, an operation behavioral pattern is identified, based on predefined rules. Subsequently, a behavior model capturing the operation behavioral pattern is constructed using a pre-existing behavior model library. Using the behavior model, a potential event relating to an order received to be fulfilled using the sales operation is predicted, the potential event being indicative of an issue affecting the order. Accordingly, the issue affecting the order is proactively remediated to automatically troubleshoot the order.
Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.
- Dublinn 4, IE Benjamin Nathan Grosof - Mercer Island WA, US Madhura Shivaram - Bangalore, IN Guanglei Xiong - Pleasanton CA, US Colin Connors - Campbell CA, US Kyle Patrick Johnson - San Francisco CA, US Emmanuel Munguia Tapia - San Jose CA, US Mingzhu Lu - San Jose CA, US Tsunghan Wu - Seattle WA, US Neeru Narang - San Jose CA, US Sukryool Kang - Sunnyvale CA, US Kayhan Moharreri - San Jose CA, US
Assignee:
ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4
International Classification:
G06F 17/30 G06F 17/27
Abstract:
A system for determining a response to a query includes a receiver to receive a query along with a plurality of potential responses to the query. A detector detects a topic and a type of the query based on information extracted from text and structure. Further, a selector selects at least one of a plurality of techniques for processing the query and the plurality of potential responses, based on the topic and the type of the query. An obtainer obtains an answer by execution of each of the selected techniques for processing the query and the plurality of potential responses along with an associated confidence score. A determinator determines one of obtained answers as a correct response to the query, based on a comparison between confidence scores associated with the answers.
- Dublin 4, IE Guanglei Xiong - Pleasanton CA, US Mohammad Ghorbani - Foster City CA, US Emmanuel Munguia Tapia - San Jose CA, US Sukryool Kang - Sunnyvale CA, US Benjamin Nathan Grosof - Mercer Island WA, US Ashish Jain - Chennai, IN Colin Connors - Campbell CA, US
Assignee:
ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin 4
International Classification:
G06N 5/04
Abstract:
In an example, an ontology analyzer may generate an ontology, based on a claim adjudication request. The claim adjudication request may be processed, based on the ontology to provide an ontology based inference. A rule based analyzer may identify a predefined rule corresponding to the claim adjudication request and process the request, based on the predefined rule. A conflict resolver may resolve a conflict which may occur between the ontology based inference and the rule based inference. When a conflict is detected, a predefined criteria may be selected for resolving the conflict, the predefined criteria comprising rules to select one of the ontology based inference and the rule based inference to maximize a probability of accurately processing the claim adjudication request in case of a conflict.
- Dublin, IE Guanglei XIONG - Pleasanton CA, US Sukryool KANG - Sunnyvale CA, US Ashish JAIN - Chennai, IN Colin CONNORS - Campbell CA, US Benjamin Nathan GROSOF - Mercer Island WA, US Neeru NARANG - San Jose CA, US
Assignee:
ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin
International Classification:
G06Q 40/08 G06F 17/30 G06N 99/00
Abstract:
A classifier receives policy data corresponding to a new policy. Further, the classifier processes the policy data to classify the policy data into an obligation class and an informational class. An information extractor then extracts metadata from the policy data that is classified into the obligation class. Subsequently, a data translator determines if there is an incremental change in the policy data based on a comparison of the policy data with policy data corresponding to existing policies. On determining the incremental change in the policy data, the data translator translates the policy data that is classified into the obligation class into a rule based on the metadata. A rules engine then receives the rule from the data translator for claims adjudication.
- Dublin, IE Suraj Govind JADHAV - Mumbai IN, US Saurabh MAHADIK - Mumbai, IN Prakash GHATAGE - Bangalore, IN Guanglei XIONG - Fremont CA, US Emmanuel MUNGUIA TAPIA - Newark CA, US Mohammad Jawad GHORBANI - Foster City CA, US Kyle JOHNSON - San Francsico CA, US Colin Patrick CONNORS - Campbell CA, US Benjamin Nathan GROSOF - Mercer Island WA, US
International Classification:
G06Q 40/00
Abstract:
A system for orchestrating an operation is disclosed. The system includes an case orchestration engine to identify a discrepancy in the operation, and to generate a plurality of hypotheses for resolving the discrepancy. The case orchestration engine further collects evidence pertaining to the discrepancy in the operation, evaluates each of the plurality of hypotheses based on a dialogue-driven feedback received from a user, and selects one of the plurality of hypotheses for resolving the discrepancy based on the evidence and an expected outcome of the operation. The case orchestration engine provides reasons for the discrepancy along with remedial measures for resolving the discrepancy based on the selected hypothesis, and then generates a plan for performing the operation to achieve the expected outcome based on the remedial measures.
- Dublin, IE Suraj Govind JADHAV - Mumbai, IN Saurabh MAHADIK - Mumbai, IN Prakash GHATAGE - Bangalore, IN Guanglei XIONG - Fremont CA, US Emmanuel Munguia TAPIA - Newark CA, US Mohammad Jawad GHORBANI - Foster City CA, US Kyle JOHNSON - San Francsico CA, US Colin Patrick CONNORS - Campbell CA, US Benjamin Nathan GROSOF - Mercer Island CA, US
Assignee:
ACCENTURE GLOBAL SOLUTIONS LIMITED - Dublin
International Classification:
G06Q 10/10 G06Q 10/06
Abstract:
Systems and methods for orchestrating a process are disclosed. In an implementation, a system is configured to extract process information associated with the process. Based on the process information, the system is configured to determine a current model of performing the process based on the process information. The system is further configured to retrieve regulatory information associated with the process, wherein the regulatory information is indicative of at least one of a predefined policy, a predefined rule, and a predefined regulation associated with the process. Further, the system is configured to update the current model based on at least one of the process information and the regulatory information for obtaining a predefined outcome of the process.
One that does is the Miami Heat. Colin Connors at Hot Hot Hoops pitched this idea a few days ago (though in his version Melo plays for Miami rather than wanting a buyout) and the idea has really grown on me. Essentially, OKC flips Melo for two of: Dion Waiters (!!), Kelly Olynyk or James Johnson. Th