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David L Elkind, 671700 N Huntington St, Arlington, VA 22205

Known as:
David S Elkind Dave L Elkind
Related to:
Alicia Elkind

David Elkind Phones & Addresses

1700 Huntington St, Arlington, VA 22205    703-5323668    703-5360705   

2800 Yucatan St, Arlington, VA 22213    703-5360705   

1401 Taft St, Arlington, VA 22201    703-5273475   

3701 Harrison St, Arlington, VA 22207    703-5360705   

109 Highwood Dr, Mchenry, MD 21541    301-3878501    301-3878531   

Mc Henry, MD   

2104 Rockingham St, McLean, VA 22101    703-5362152    703-5360705   

Mc Lean, VA   

Alexandria, VA   

1700 N Huntington St, Arlington, VA 22205    703-5323668   

Work

Position: Homemaker

Education

Degree: High school graduate or higher

Emails

b***[email protected]
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Lawyers & Attorneys

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David L. Elkind, Washington DC - Lawyer

Address:
Dickstein Shapiro LLP
1825 Eye Street Nw, Washington, DC 20006
202-4203603 (Office)
Licenses:
Dist. of Columbia - Active 1989
Education:
Georgetown University Law CenterDegree JD - Juris Doctor - LawGraduated 1983
George Washington UniversityDegree BA - Bachelor of Arts - Journalism and Mass CommunicationGraduated 1979
Specialties:
Insurance - 100%
Associations:
American Bar Association - Member
David Elkind Photo 2

David L. Elkind - Lawyer

Licenses:
New York - Currently registered 1984
Education:
Georgetown University Law Center

David Elkind resumes & CV records

Resumes

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Partner

Location:
1320 north Veitch St, Arlington, VA 22201
Industry:
Law Practice
Work:
Dickstein Shapiro LLP
Partner
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David Elkind

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David Elkind

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David L Elkind

Publications & IP owners

Wikipedia

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David Elkind

Professor David Elkind (born March 11, 1931) is an American child psychologist and author. His groundbreaking books The Hurried Child,[4] The Power of ...

Us Patents

Validation-Based Determination Of Computational Models

US Patent:
2021007, Mar 11, 2021
Filed:
Nov 2, 2020
Appl. No.:
17/087194
Inventors:
- Irvine CA, US
David Elkind - Arlington VA, US
Brett Meyer - Alpharetta GA, US
Patrick Crenshaw - Atlanta GA, US
International Classification:
H04L 29/06
G06N 20/00
G06F 21/56
Abstract:
Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Malware Detection Using Local Computational Models

US Patent:
2019002, Jan 24, 2019
Filed:
Jul 24, 2017
Appl. No.:
15/657379
Inventors:
- Irvine CA, US
David Elkind - Arlington VA, US
Patrick Crenshaw - Atlanta GA, US
Kirby James Koster - Lino Lakes MN, US
International Classification:
G06F 21/56
G06F 21/55
Abstract:
Example techniques herein determine that a trial data stream is associated with malware (“dirty”) using a local computational model (CM). The data stream can be represented by a feature vector. A control unit can receive a first, dirty feature vector (e.g., a false miss) and determine the local CM based on the first feature vector. The control unit can receive a trial feature vector representing the trial data stream. The control unit can determine that the trial data stream is dirty if a broad CM or the local CM determines that the trial feature vector is dirty. In some examples, the local CM can define a dirty region in a feature space. The control unit can determine the local CM based on the first feature vector and other clean or dirty feature vectors, e.g., a clean feature vector nearest to the first feature vector.

Computational Modeling And Classification Of Data Streams

US Patent:
2018019, Jul 12, 2018
Filed:
Jan 10, 2017
Appl. No.:
15/402524
Inventors:
- Irvine CA, US
David Elkind - Arlington VA, US
Patrick Crenshaw - Atlanta GA, US
Brett Meyer - Alpharetta GA, US
International Classification:
G06N 5/04
G06N 99/00
H04L 12/24
H04L 29/06
Abstract:
Example techniques described herein determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processor can locate training analysis regions of training data streams based on predetermined structure data, and determining training model inputs based on the training analysis regions. The processor can determine a computational model based on the training model inputs. The computational model can receive an input vector and provide a corresponding feature vector. The processor can then locate a trial analysis region of a trial data stream based on the predetermined structure data and determine a trial model input. The processor can operate the computational model based on the trial model input to provide a trial feature vector, e.g., a signature. The processor can operate a second computational model to provide a classification based on the signature.

Validation-Based Determination Of Computational Models

US Patent:
2018019, Jul 12, 2018
Filed:
Jan 10, 2017
Appl. No.:
15/402503
Inventors:
- Irvine CA, US
David Elkind - Arlington VA, US
Brett Meyer - Alpharetta GA, US
Patrick Crenshaw - Atlanta GA, US
International Classification:
H04L 29/06
G06N 99/00
Abstract:
Example techniques described herein determine a validation dataset, determine a computational model using the validation dataset, or determine a signature or classification of a data stream such as a file. The classification can indicate whether the data stream is associated with malware. A processing unit can determine signatures of individual training data streams. The processing unit can determine, based at least in part on the signatures and a predetermined difference criterion, a training set and a validation set of the training data streams. The processing unit can determine a computational model based at least in part on the training set. The processing unit can then operate the computational model based at least in part on a trial data stream to provide a trial model output. Some examples include determining the validation set based at least in part on the training set and the predetermined criterion for difference between data streams.

Isbn (Books And Publications)

Readings In Human Development: Contemporary Perspectives

Author:
David Elkind
ISBN #:
0060470550

Studies In Cognitive Development: Essays In Honor Of Jean Piaget

Author:
David Elkind
ISBN #:
0195008782

Children And Adolescents; Interpretive Essays On Jean Piaget

Author:
David Elkind
ISBN #:
0195017803

Child Development And Education: A Piagetian Perspective

Author:
David Elkind
ISBN #:
0195020693

Child Development And Education: A Piagetian Perspective

Author:
David Elkind
ISBN #:
0195020685

The Child And Society: Essays In Applied Child Development

Author:
David Elkind
ISBN #:
0195023714

The Child And Society: Essays In Applied Child Development

Author:
David Elkind
ISBN #:
0195023722

Children And Adolescents: Interpretive Essays On Jean Piaget

Author:
David Elkind
ISBN #:
0195028201

Possible Relatives

Darren J Elkind  James D Elkind  Michael S Elkind  Morton D Elkind  Rachel V Elkind  Samuel S Elkind  Alexander Elkind  David Last  David W Johnson  David Stamps 

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