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Maria A Kazandjieva

from Menlo Park, CA
Age ~39
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Connected to:
Harout Kazandjian, 35Leon Kazandjian, 49Amirali A Kazani, 76Konstandinos Kazanis, 48Christian J Kazanjian, 28Cindy L Kazanjian, 62Gary C Kazanjian, 70Gregory A Kazanjian, 58Hasmik Kazanjian, 45Michael K Kazanjian, 70

Maria Kazandjieva Phones & Addresses

  • 830 Fremont St APT 2, Menlo Park, CA 94025
  • Stanford, CA
  • Princeton, NJ
  • South Hadley, MA

Resumes

Resumes

Maria Kazandjieva Photo 1

Director Of Engineering

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Location:
San Francisco, CA
Industry:
Computer Software
Work:
Stanford University since Sep 2007
PhD Student
Princeton University Feb 2007 - Aug 2007
Research Staff
Princeton University Jun 2006 - Aug 2006
Intern
Education:
Stanford University 2007 - 2012
PhD, Computer Science Stanford University 2007 - 2009
MS, Computer Science Mount Holyoke College 2003 - 2006
B.A., Computer Science American College of Sofia
Skills:
Computer Science
Wireless Sensor Networks
Latex
Linux
Python
Distributed Systems
Programming
C
Software Engineering
Embedded Systems
Unix
Networking
Energy Efficiency
Measurement Systems
Tinyos
Software Development
Network Protocol Design
Javascript
Interests:
Green Computing
Animal Welfare
Wireless Sensor Networks
Languages:
English
Bulgarian
Maria Kazandjieva Photo 2

Phd Student At Stanford University

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Position:
PhD Student at Stanford University
Location:
San Francisco Bay Area
Industry:
Computer Networking
Work:
Stanford University since Sep 2007
PhD Student
Princeton University Feb 2007 - Aug 2007
Research Staff
Princeton University Jun 2006 - Aug 2006
Intern
Education:
Stanford University 2007 - 2012
PhD, Computer Science Stanford University 2007 - 2009
MS, Computer Science Mount Holyoke College 2003 - 2006
B.A., Computer Science American College of Sofia
Skills:
Python
C
Computer Science
Embedded Systems
LaTeX
Distributed Systems
Software Engineering
Linux
Programming
Unix
Networking
Network Protocol Design
Energy Efficiency
Wireless Sensor Networks
Measurement Systems
TinyOS
Interests:
green computing, wireless sensor networks
Honor & Awards:
Google Anita Borg Scholarship Finalist, 2009 Stanford Computer Forum Fellowship, September 2007 ACM Grand Finals, Undergraduate Category, Second Place, June 2007 SIGCSE ’07 Student Research Competition, Second Place, March 2007 Starr Scholar, C.V. Foundation, February and September 2006 CRA-W Distributed Mentor Project, Summer 2005 and Summer 2006 Howard Hughes Medical Institute Research Fellowship, Summer 2006 Sarah Williston Scholar, October 2005 Mildred L. Sanderson Prize for Excellence in Mathematics, May 2004

Publications

Us Patents

Apparatus And Method For Forming Connections With Unstructured Data Sources

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US Patent:
20220414254, Dec 29, 2022
Filed:
May 3, 2022
Appl. No.:
17/735994
Inventors:
- San Francisco CA, US
Maria KAZANDJIEVA - Menlo Park CA, US
Eric SCHKUFZA - Oakland CA, US
Mher HAKOBYAN - Mountain View CA, US
Irina CALCIU - Palo Alto CA, US
Brian CALVERT - San Francisco CA, US
Deven NAVANI - San Jose CA, US
International Classification:
G06F 21/62
G06F 21/31
G06F 16/33
G06F 16/338
Abstract:
A non-transitory computer readable storage medium with instructions executed by a processor maintains a collection of data access connectors configured to access different sources of unstructured data. A user interface with prompts for designating a selected data access connector from the data access connectors is supplied. Unstructured data is received from the selected data access connector. Numeric vectors characterizing the unstructured data are created from the unstructured data. The numeric vectors are stored and indexed.

Apparatus And Method For Aggregating And Evaluating Multimodal, Time-Varying Entities

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US Patent:
20230069958, Mar 9, 2023
Filed:
Feb 23, 2022
Appl. No.:
17/678942
Inventors:
- San Francisco CA, US
Maria KAZANDJIEVA - Menlo Park CA, US
Eric SCHKUFZA - Oakland CA, US
Mher HAKOBYAN - Mountain View CA, US
Irina CALCIU - Palo Alto CA, US
Brian CALVERT - San Francisco CA, US
Daniel WOOLRIDGE - Los Angeles CA, US
International Classification:
G06F 16/31
G06F 16/33
Abstract:
A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data, where the unstructured data has multiple modes of semantically distinct data types and the unstructured data has time-varying data instances aggregated over time. An entity combining different sources of the unstructured data is formed. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models. These operations are repeated to form an aggregation of multimodal, time-varying entities and a corresponding index of individual entities and corresponding embeddings. Proximity searches are performed on embeddings within the index.

Apparatus And Method For Transforming Unstructured Data Sources Into Both Relational Entities And Machine Learning Models That Support Structured Query Language Queries

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US Patent:
20230072311, Mar 9, 2023
Filed:
Sep 28, 2021
Appl. No.:
17/488043
Inventors:
- San Francisco CA, US
Maria KAZANDJIEVA - Menlo Park CA, US
Eric SCHKUFZA - Oakland CA, US
Mher HAKOBYAN - Mountain View CA, US
Irina CALCIU - Palo Alto CA, US
Brian CALVERT - San Francisco CA, US
International Classification:
G06F 16/242
G06F 16/2458
G06F 16/28
G06N 20/20
G06F 16/33
Abstract:
A non-transitory computer readable storage medium has instructions executed by a processor to receive from a network connection different sources of unstructured data. An entity is formed by combining one or more sources of the unstructured data, where the entity has relational data attributes. A representation for the entity is created, where the representation includes embeddings that are numeric vectors computed using machine learning embedding models, including trunk models, where a trunk model is a machine learning model trained on data in a self-supervised manner. An enrichment model is created to predict a property of the entity. A query is processed to produce a query result, where the query is applied to one or more of the entity, the embeddings, the machine learning embedding models, and the enrichment model.
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Maria A Kazandjieva from Menlo Park, CA, age ~39 Get Report