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Steven M Dimaria

from Charlotte, NC
Age ~33
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Also known as:
Steven Mark Dimaria, Steve Dimaria, Steve Demaria
Related to:
Robert Pete Chin, 78Stephanie Schmieg, 45Eric Besserer, 41Tze G Chin, 56Pamela Chin, 34
Connected to:
Alan M Dimaria, 64Albert R Dimaria, 62Amanda P Dimaria, 36Beth A Dimaria, 73Janet L Dimaria, 58Jessie Dimariano, 38Laureen H Dimaria, 64Loriann Dimaria, 43Raymond A Dimaria, 59Sal N Dimaria, 92

Steven Dimaria Phones & Addresses

  • Charlotte, NC
  • Elmira, NY
  • New York, NY
  • Ann Arbor, MI
  • 9037 W 24Th St, Los Angeles, CA 90034 (310) 838-4509

Work

Company: Purewow Feb 2017 Position: Data scientist

Education

Degree: Bachelors, Bachelor of Arts School / High School: University of Michigan 2009 to 2013 Specialities: English Language, Linguistics, Literature, English Language and Literature, Philosophy

Skills

Python • Sql • Postgresql • Natural Language Processing • Pandas • Numpy • Scipy • Django • Statistical Modeling • Hadoop • Spark • Hive • Pig • Microsoft Excel • Editorial • New Media • Google Analytics • Ppc Bid Management • Multivariate Testing • Digital Marketing • Web Analytics • Growth Hacking • Digital Strategy

Languages

French • Latin

Ranks

Certificate: License 4979831613161472

Interests

Event Planning • Internet Culture • Digital Marketing • Digital Strategy • Media Planning • New Media • Pop Culture

Industries

Internet

Resumes

Resumes

Steven Dimaria Photo 1

Data Scientist

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Location:
New York, NY
Industry:
Internet
Work:
Purewow
Data Scientist
Chief Media Sep 2015 - Jan 2016
Digital Analyst
Vaynermedia Sep 2015 - Jan 2016
Growth Marketing and User Retention
Bank of America Sep 2015 - Jan 2016
Data Scientist
Thrillist May 2012 - Jan 2013
Summer Intern
Education:
University of Michigan 2009 - 2013
Bachelors, Bachelor of Arts, English Language, Linguistics, Literature, English Language and Literature, Philosophy
Skills:
Python
Sql
Postgresql
Natural Language Processing
Pandas
Numpy
Scipy
Django
Statistical Modeling
Hadoop
Spark
Hive
Pig
Microsoft Excel
Editorial
New Media
Google Analytics
Ppc Bid Management
Multivariate Testing
Digital Marketing
Web Analytics
Growth Hacking
Digital Strategy
Interests:
Event Planning
Internet Culture
Digital Marketing
Digital Strategy
Media Planning
New Media
Pop Culture
Languages:
French
Latin
Certifications:
License 4979831613161472
Google Analytics Individual Qualifier (Gaiq)
Google, License 4979831613161472

Publications

Us Patents

Known-Deployed File Metadata Repository And Analysis Engine

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US Patent:
20220366038, Nov 17, 2022
Filed:
May 13, 2021
Appl. No.:
17/319299
Inventors:
- Charlotte NC, US
Jeffrey Texada - Carrollton TX, US
Matthew E. Kelly - Chicago IL, US
Steven Dimaria - Charlotte NC, US
International Classification:
G06F 21/55
Abstract:
A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and/or identified artifacts of known-deployed files.

Known-Deployed File Metadata Repository And Analysis Engine

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US Patent:
20220366042, Nov 17, 2022
Filed:
May 13, 2021
Appl. No.:
17/319831
Inventors:
- Charlotte NC, US
Jeffrey Texada - Carrollton TX, US
Matthew E. Kelly - Chicago IL, US
Steven Dimaria - Charlotte NC, US
International Classification:
G06F 21/56
G06F 21/53
G06F 8/60
Abstract:
A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and/or identified artifacts of known-deployed files.

Known-Deployed File Metadata Repository And Analysis Engine

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US Patent:
20220366045, Nov 17, 2022
Filed:
May 13, 2021
Appl. No.:
17/319638
Inventors:
- Charlotte NC, US
Jeffrey Texada - Carrollton TX, US
Matthew E. Kelly - Chicago IL, US
Steven Dimaria - Charlotte NC, US
International Classification:
G06F 21/56
G06F 21/55
G06F 21/53
G06F 8/60
Abstract:
A known-deployed file metadata repository (KDFMR) and analysis engine enumerates reference lists of files stored on a software delivery point (SDP) and compares the enumerated list of files and associated metadata to previously stored values in the KDFMR. If newly stored or modified files are identified, the analysis engine acquires the files from the SDP. Each file is analyzed to determine whether the file is an atomic file or a container file and metadata is generated or extracted. Each file stored in a container file is recursively extracted and analyzed, where metadata is generated for each extracted file and each container file. The KDFMR periodically analyzes the files stored on the SDP for differences to maintain the currency of the KDFMR data with respect to files stored on the SDP. Storage or modification of files on the SDP triggers analysis of the associated file. KDFMR data is updated with metadata determined based on sandbox detonation of files and/or identified artifacts of known-deployed files.

System And Method For Single-Speaker Identification In A Multi-Speaker Environment On A Low-Frequency Audio Recording

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US Patent:
20220223157, Jul 14, 2022
Filed:
Jan 11, 2021
Appl. No.:
17/145474
Inventors:
- Charlotte NC, US
Steven Mark DiMaria - Charlotte NC, US
International Classification:
G10L 17/06
G10L 15/02
Abstract:
A system for identifying a speaker in a multi-speaker environment comprises a processor operably coupled with a memory. The system receives a request to identify a first speaker in an audio file. The system splits the audio file into audio snippets based on a probability of each audio snippet comprising one or more utterances being above a threshold percentage. For each audio snippet, the system generates a frequency representation of the audio snippet in a time domain. The system generates a feature vector of numerical values representing voice features associated with one or both of the first speaker and the second speaker. The system determines whether the feature vector corresponds to the target vector labeled with the first speaker. In response to determining that the feature vector corresponds to the target vector, the system determines that one or more utterances in the audio snippet are spoken by the first speaker.

Protection Against Voice Misappropriation In A Voice Interaction System

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US Patent:
20230012259, Jan 12, 2023
Filed:
Jul 12, 2021
Appl. No.:
17/372994
Inventors:
- Charlotte NC, US
Steven Mark DiMaria - Charlotte NC, US
Assignee:
BANK OF AMERICA CORPORATION - Charlotte NC
International Classification:
G10L 15/22
H04Q 9/00
G10L 15/08
Abstract:
Prevention of voice misappropriation in voice interaction/response systems. The system relies on telemetry data, including thermal data of components to determine whether a received voice command was made by actual voice. If the voice command is determined to have been made by an actual voice, a response to the command is generated and transmitted, otherwise if the voice command is determined to have likely not been made by an actual voice (e.g., artificial means replicating a voice, such as a laser or the like), no response to the command is transmitted or action taken with respect to the command.
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Steven M Dimaria from Charlotte, NC, age ~33 Get Report