IR Engineer at Spock Networks
San Francisco Bay Area
- Current
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- Everything at Soup Dog Software LLC
- Past
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- IR Engineer at Spock Networks
- Research Intern/Engineer at IBM
- Graduate Student at UC Santa Cruz
- Education
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- University of California, Santa Cruz
- Cornell University
- Recommended
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1 person has recommended Philip - Connections
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63
connections
- Industry
- Computer Software
- Websites
Philip Zigoris’s Summary
Research revolved around statistical modeling for information retrieval and fault detection. Industry experience in large scale data mining and information retrieval (both from a modeling and architecture standpoint).
Philip Zigoris’s Specialties:
Machine Learning, Statistics, Information Retrieval, Network Services, Search/Index Architecture
Philip Zigoris’s Experience
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Everything
Soup Dog Software LLC
(Computer Software industry)
October 2008 — Present (10 months)
Independent consulting and software development with a focus on iPhone development and data mining.
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IR Engineer
Spock Networks
(Privately Held; Internet industry)
February 2007 — October 2008 (1 year 9 months)
Frontend search services:
- autocomplete
- search aggregation
- spellchecker
Backend data-mining projects include mining web references, crawling/indexing, conflation, mining architecture and API.
Technologies: Python, Perl, C, MySQL, SQL, Xapian, threads, OOP, regex, amazon web services (S3, EC2, SimpleDB) -
Research Intern/Engineer
IBM
(Public Company; IBM; Information Technology and Services industry)
May 2006 — December 2006 (8 months)
Significantly improved core Content Protection technology using methods from statistical machine learning.
Developed extensible Bayesian Network Toolkit (Java) -
Graduate Student
UC Santa Cruz
(Educational Institution; Higher Education industry)
September 2004 — December 2006 (2 years 4 months)
TA'd graduate algorithms course (maintained office hours, graded homework & tests)
Research centered on methods from machine learning as applied to:
- software verification
- image segmentation
- personalized document ranking models
- information extraction using tree alignment models
