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Nikita I Lytkin

from Sunnyvale, CA
Age ~42
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Also known as:
Nikita Lytkina
Related to:
Elena Lytkina, 53Imrankhan Shajahan, 45Carlos Botelho, 51
Connected to:
Bradford P Lytle, 69Brittney N Lytle, 31Cassandra Lytle, 39Celestine E Lytle, 67Christine M Lytle, 61Corrine A Lytle, 52Dalton W Lytle, 56Dwain E Lytle, 70Fabio Lytle, 42Fran N Lytle, 70

Nikita Lytkin Phones & Addresses

  • Sunnyvale, CA
  • Menlo Park, CA
  • Walnut Creek, CA
  • Jersey City, NJ
  • New Brunswick, NJ
  • Piscataway, NJ
  • Arlington, VA
  • New York, NY

Work

Company: Rutgers university Sep 2003 to Sep 2009 Position: Graduate research assistant in the department of computer science

Education

Degree: Ph.D. School / High School: Rutgers, The State University of New Jersey-New Brunswick 2003 to 2009 Specialities: Computer Science

Skills

Machine Learning • Algorithms • R • Statistics • Data Mining • Java • C++ • Mapreduce • Statistical Modeling • Computer Science • Matlab • Predictive Modeling • Perl • Hadoop • Sql • Optimization • Data Science • Bioinformatics • Team Leadership • A/B Testing • Hypothesis Testing

Languages

English • Russian

Industries

Internet

Resumes

Resumes

Nikita Lytkin Photo 1

Computer Vision And Machine Learning Leader In Accounts Receivable And Vr

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Location:
932 Fontana Ter, Sunnyvale, CA 94085
Industry:
Internet
Work:
Rutgers University Sep 2003 - Sep 2009
Graduate Research Assistant in the Department of Computer Science
Siemens May 2009 - Aug 2009
Research Intern in the Imaging and Visualization Department
Intel Jun 2006 - Aug 2006
Research Intern in the Analysis & Control Technology group
NEC Jun 2005 - Aug 2005
Research Intern in the Machine Learning department
Rutgers University Sep 2001 - Sep 2003
Applications Developer and Web Designer
Education:
Rutgers, The State University of New Jersey-New Brunswick 2003 - 2009
Ph.D., Computer Science Rutgers, The State University of New Jersey-New Brunswick 1999 - 2002
B.S., Computer Science, with minor in Economics
Skills:
Machine Learning
Algorithms
R
Statistics
Data Mining
Java
C++
Mapreduce
Statistical Modeling
Computer Science
Matlab
Predictive Modeling
Perl
Hadoop
Sql
Optimization
Data Science
Bioinformatics
Team Leadership
A/B Testing
Hypothesis Testing
Languages:
English
Russian

Publications

Us Patents

Systems And Methods For Data Transformation Using Higher Order Learning

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US Patent:
20100312727, Dec 9, 2010
Filed:
Jun 9, 2010
Appl. No.:
12/797366
Inventors:
William M. Pottenger - Hellertown PA, US
Nikita Lytkin - Jersey City NJ, US
Murat C. Ganiz - Uskudar, TR
International Classification:
G06F 15/18
US Classification:
706 12
Abstract:
Disclosed is a method and apparatus for transforming data in vector form. Each vector is composed of a set of attributes that are either boolean or have been mapped to boolean form. The vectors may or may not fall into categories assigned by a subject matter expert (SME). If categories exist, the categorical labels divide the vectors into subsets. The first transformation calculates a prior probability for each attribute based on the links between attributes in each subset of the vectors. The second transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors. The third transformation operates on vectors that have not been categorized. Based on the automatic selection of categories from the attributes, this transformation computes a new numeric value for each attribute based on the links between attributes in each subset of the vectors.

Generating Viewer Affinity Score In An On-Line Social Network

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US Patent:
20180020066, Jan 18, 2018
Filed:
Jul 18, 2016
Appl. No.:
15/212498
Inventors:
- Mountain View CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
International Classification:
H04L 29/08
G06F 17/30
G06F 17/22
G06Q 50/00
G06F 17/21
Abstract:
A relevance model is used to process an inventory of updates for a member of an on-line social network in order to select a subset of updates for presentation to the member. One of the features used as input to the relevance model is viewer affinity. The viewer affinity indicates preference of a member for a particular type or source of information and is determined using the estimated probability of the member clicking on the impression of an update and also based on a correction variable. The correction variable is generated based on information regarding previously-observed interactions of the member with the updates.

First Pass Ranker Calibration For News Feed Ranking

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US Patent:
20170351679, Dec 7, 2017
Filed:
Jun 3, 2016
Appl. No.:
15/172649
Inventors:
- Mountain View CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
Yanen Li - Foster City CA, US
Guy Lebanon - Menlo Park CA, US
International Classification:
G06F 17/30
G06N 99/00
H04L 12/58
Abstract:
An on-line social network system is configured to generate a news feed for a member by processing updates originating from different sources using different first pass ranker models. The first pass ranker models generate respective sets of raw scores, which are calibrated based on a consistent scale of feed engagement metrics of interest, such as a click through rate. The calibrated scores are then used as training data to train a second pass ranker and/or as input into the second pass ranker at the time when the second pass ranker is to generate respective ranks for items in an inventory of updates identified as potentially of interest to a focus member and to select a subset of items from the inventory based on the generated respective ranks.

Augmented News Feed In An On-Line Social Network

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US Patent:
20170324799, Nov 9, 2017
Filed:
May 9, 2016
Appl. No.:
15/149418
Inventors:
- Mountain View CA, US
Zheng Li - San Jose CA, US
Ying Xuan - Sunnyvale CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
International Classification:
H04L 29/08
G06F 17/30
G06F 17/30
G06F 17/30
H04L 29/08
H04L 12/26
Abstract:
A news feed system is provided to generate augmented news feed for a member of an on-line social network. When a request is detected to construct a news feed for a member in an on-line social network system, the news feed system determines a cohort of the member and, from the updates that are being presented in their respective news feeds to members of the cohort, selects a leaderboard set of updates. The leaderboard set of updates is used in constructing a news feed web page for the member.

Profile Personalization Based On Viewer Of Profile

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US Patent:
20160292280, Oct 6, 2016
Filed:
Mar 31, 2015
Appl. No.:
14/674755
Inventors:
- Mountain View CA, US
Sachit Kamat - San Carlos CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
Vibha Rathi - Cupertino CA, US
Jibran Kutik - Mountain View CA, US
Mathieu Bastian - San Francisco CA, US
Matthieu F. Monsch - San Francisco CA, US
Xin Hu - Mountain View CA, US
International Classification:
G06F 17/30
Abstract:
Techniques for presenting a personalized member profile page to a viewer are described. A highlight module can receive a request to view a profile page of a member in a social network. The highlight module can access viewer data of a viewer associated with the request, and access member data of the member. Additionally, the highlight module can determine a plurality of member attributes relevant to the viewer based on the viewer data, the plurality of member attributes being derived from the member data. Furthermore, the highlight module can calculate an overall score for a member attribute in the plurality of member attributes based on the viewer data and the member data. Subsequently, a profile generation module can cause a presentation, on a display of a device, of the member attribute on the profile page, when the overall score of the member attribute is higher than a predetermined threshold value.

Algorithm For Selecting And Scoring Suggested Action

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US Patent:
20160292284, Oct 6, 2016
Filed:
Sep 2, 2015
Appl. No.:
14/843731
Inventors:
- Mountain View CA, US
Nipun Dave - Mountain View CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
Sachit Kamat - San Carlos CA, US
International Classification:
G06F 17/30
Abstract:
Techniques for presenting a personalized member profile page to a viewer are described. The online social network service system can access a sender score of a sender requesting to view a profile page in an online social network service, and access a receiver score of a receiver associated with the profile page. Additionally, a suggested action can be determined based on the sender score and the receiver score, a sender confirmation to perform the suggested action can be received, and a communication associated with the suggested action can be transmitted in response to the received sender confirmation. Subsequently, the online social network service system can classify an interaction between the sender and the receiver, and update the receiver score and the sender score based on the classified interaction.

Determining A Preferred List Length For School Ranking

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US Patent:
20160217139, Jul 28, 2016
Filed:
Jan 27, 2015
Appl. No.:
14/606944
Inventors:
- Mountain View CA, US
Ryan Wade Sandler - San Francisco CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
Bee-Chung Chen - San Jose CA, US
Deepak Agarwal - Sunnyvale CA, US
International Classification:
G06F 17/30
G06Q 50/20
G06Q 10/10
Abstract:
A school ranking system may be configured to determine a rank of a school based on career outcomes data. Career outcomes data is obtained, at least in part, from member profile data stored by an on-line social network system. The school ranking system uses a list of the top-ranked companies for generating ranking data and also determines how many companies are to be included in the list of the top-ranked companies.

Guided Edit Optimization

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US Patent:
20160055010, Feb 25, 2016
Filed:
Aug 25, 2015
Appl. No.:
14/835605
Inventors:
Alexis Blevins Baird - San Francisco CA, US
Lokesh P. Bajaj - Fremont CA, US
Jason Schissel - Mountain View CA, US
Xin Fu - Sunnyvale CA, US
Nikita Igorevych Lytkin - Sunnyvale CA, US
Yin Lou - Mountain View CA, US
International Classification:
G06F 9/44
G06F 3/0482
G06F 17/30
G06F 17/24
Abstract:
Techniques for optimizing a guided edit process for editing a member profile page are described. According to various embodiments, incomplete member profile fields in a member profile associated with member of an online social networking service are identified. Profile completion score weight values associated with the incomplete member profile fields in the member profile are then determined. Thereafter, the incomplete member profile fields in the member profile are ranked, based on the profile completion score weight values associated with each of the incomplete member profile fields. A list of one or more of the highest ranked incomplete member profile fields are then displayed, together with a prompt recommending the member to complete the identified incomplete member profile fields.
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Nikita I Lytkin from Sunnyvale, CA, age ~42 Get Report