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Sofus A Macskassy

from Palo Alto, CA
Age ~54

Sofus Macskassy Phones & Addresses

  • 1107 Trinity Ln, Palo Alto, CA 94303 (650) 391-6840
  • 1044 7Th St, Hermosa Beach, CA 90254
  • 1222 8Th St, Hermosa Beach, CA 90254
  • 201 Concord Pl, North Brunswick, NJ 08902 (732) 951-1556
  • East Brunswick, NJ
  • Los Angeles, CA
  • Edison, NJ
  • Piscataway, NJ
  • N Brunswick, NJ
  • 3607 Evergreen Dr, Palo Alto, CA 94303

Work

Position: Professional/Technical

Education

Degree: Graduate or professional degree

Resumes

Resumes

Sofus Macskassy Photo 1

Director, Fetch Labs At Fetch Technologies Assistant Adjunct Professor At University Of Southern California

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Location:
Greater Los Angeles Area
Industry:
Research
Sofus Macskassy Photo 2

Sofus Macskassy

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Business Records

Name / Title
Company / Classification
Phones & Addresses
Sofus Macskassy
Director
Fetch Technologies
Nonclassifiable Establishments
120 Albany St, New Brunswick, NJ 08901
Sofus Macskassy
Director Of Labs
Fetch Technologies
Computer Software · Custom Computer Programing · Custom Computer Programming Services
841 Apollo St SUITE 400, El Segundo, CA 90245
(310) 414-9849, (310) 640-0434

Publications

Us Patents

Generating Custom Application Links

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US Patent:
20180357215, Dec 13, 2018
Filed:
Jun 8, 2018
Appl. No.:
16/003643
Inventors:
- Redwood City CA, US
William Lindemann - Palo Alto CA, US
Cheng-chao Yang - Palo Alto CA, US
Eric J. Glover - Palo Alto CA, US
Dmitri Gaskin - Albany CA, US
Kan Yu - San Mateo CA, US
Sofus Macskassy - Palo Alto CA, US
Assignee:
Branch Metrics, Inc. - Redwood City CA
International Classification:
G06F 17/24
G06F 17/22
Abstract:
A method includes receiving a request from a user device accessing a webpage, the request including a webpage uniform resource locator (URL) and a user device identifier. The method includes retrieving a list of events associated with the user device based on the device identifier. The method further includes retrieving sets of rules. Each set of rules indicates events and URLs that satisfy the set of rules. Each set of rules is associated with a template that includes link rendering data for rendering a link on the user device. The method includes identifying a set of rules that is satisfied by the received URL and events, transmitting link rendering data associated with the identified set of rules to the user device, and transmitting link routing data to the user device. The link routing data is configured to route the user device to an application state corresponding to the webpage.

Systems And Methods For Incremental Character Recognition To Recognize Characters In Images

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US Patent:
20170372163, Dec 28, 2017
Filed:
Jun 27, 2016
Appl. No.:
15/194021
Inventors:
- Menlo Park CA, US
Ahmad Abdulmageed Mohammed Abdulkader - Palo Alto CA, US
Sofus Attila Macskassy - Palo Alto CA, US
International Classification:
G06K 9/46
G06K 9/20
Abstract:
Systems, methods, and non-transitory computer-readable media can acquire an image that depicts at least one character. A set of pixels, within the image, through which the at least one character is depicted can be identified. At least one linear portion, within the image, can be identified based on the set of pixels. For each sub-portion within the at least one linear portion, a respective first confidence score representing a respective first likelihood that a respective sub-portion depicts the at least one character can be determined.

Systems And Methods For Churn Prediction

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US Patent:
20170220933, Aug 3, 2017
Filed:
Jan 28, 2016
Appl. No.:
15/009603
Inventors:
- Menlo Park CA, US
Aude Hofleitner - San Francisco CA, US
Sofus Attila Macskassy - Palo Alto CA, US
Steven James Jarrett - Alameda CA, US
Aruna Bharathi - San Jose CA, US
Zhiliang Ma - San Jose CA, US
International Classification:
G06N 5/04
G06N 7/00
G06N 99/00
Abstract:
Systems, methods, and non-transitory computer-readable media can collect past user information and churn data for a plurality of users. A churn prediction model is trained using the past user information and churn data. A churn propensity score is calculated for a present user based on the churn prediction model, the churn propensity score indicative of the likelihood of the present user to churn.

Label Inference In A Social Network

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US Patent:
20170124467, May 4, 2017
Filed:
Dec 9, 2016
Appl. No.:
15/375050
Inventors:
- Menlo Park CA, US
Sofus Attila Macskassy - Palo Alto CA, US
Stanislav Funiak - Lawrence KS, US
Jonathan Chang - San Francisco CA, US
International Classification:
G06N 5/04
G06Q 50/00
Abstract:
At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.

Label Inference In A Social Network

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US Patent:
20150213370, Jul 30, 2015
Filed:
May 7, 2014
Appl. No.:
14/272176
Inventors:
- Menlo Park CA, US
Sofus Attila Macskassy - Palo Alto CA, US
Stanislav Funiak - Lawrence KS, US
Jonathan Chang - San Francisco CA, US
Assignee:
Facebook, Inc. - Menlo Park CA
International Classification:
G06N 5/04
G06Q 50/00
G06N 7/00
Abstract:
At least one embodiment of this disclosure includes a method of inferring attribute labels for a user in a social networking system based on the user's social connections and user-specified attribute labels in the social networking system. The method can include: establishing variational equations based on attribute labels of nodes in an ego network in a social graph of a social networking system; determining likelihood scores for at least a portion of the attribute labels of neighboring nodes from a focal user node in the ego network based on user-specified attribute labels from the social networking system; and calculating probability distributions of possible attribute labels for the focal user node of the ego network based on the variational equations and the likelihood scores.
Sofus A Macskassy from Palo Alto, CA, age ~54 Get Report