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Ion A Muslea

from El Segundo, CA
Age ~57

Ion Muslea Phones & Addresses

  • 1429 Oak Ave, El Segundo, CA 90245 (310) 615-3124
  • 625 Maryland St, El Segundo, CA 90245 (310) 426-9809
  • 350 Sharon Park Dr, Menlo Park, CA 94025 (650) 854-9977
  • 6150 Canterbury Ave, Culver City, CA 90230 (310) 649-6806
  • Los Angeles, CA
  • Morgantown, WV

Work

Company: Bemoko Mar 2017 to Jan 2019 Position: Senior director of research and product development, scalability and big data

Education

Degree: Doctorates, Doctor of Philosophy School / High School: University of Southern California 1996 to 2002 Specialities: Computer Science

Skills

Natural Language Processing • Machine Learning • Text Mining • Artificial Intelligence • Big Data • Information Retrieval • Computer Science • Computational Linguistics • Algorithms • Pattern Recognition • Scalability • Information Extraction • Hadoop • Data Mining • Software Engineering • Distributed Systems • Python • Machine Translation • Mapreduce • Computer Vision • Language Technology • Perl • Apache Pig • Recommender Systems • Neural Networks • High Performance Computing • Human Computer Interaction • Semantic Web • Pig • Text Classification • Parallel Computing • Text Analytics • Semantic Technologies • Nosql • Web Mining • Sentiment Analysis • Ontologies • Software Development • Modern Saas Architectures

Languages

English • French • Romanian

Ranks

Certificate: Introduction To Operations Management

Industries

Research

Resumes

Resumes

Ion Muslea Photo 1

Software Development Manager

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Location:
Los Angeles, CA
Industry:
Research
Work:
Bemoko Mar 2017 - Jan 2019
Senior Director of Research and Product Development, Scalability and Big Data

Amazon Mar 2017 - Jan 2019
Software Development Manager

University of Southern California Mar 2017 - Jan 2019
Part-Time Faculty

Sdl Documentation Solutions Oct 2012 - Mar 2014
Senior Research Scientist, Manager of Big Data Initiatives

Language Weaver Mar 2005 - Oct 2012
Research Scientist
Education:
University of Southern California 1996 - 2002
Doctorates, Doctor of Philosophy, Computer Science
Skills:
Natural Language Processing
Machine Learning
Text Mining
Artificial Intelligence
Big Data
Information Retrieval
Computer Science
Computational Linguistics
Algorithms
Pattern Recognition
Scalability
Information Extraction
Hadoop
Data Mining
Software Engineering
Distributed Systems
Python
Machine Translation
Mapreduce
Computer Vision
Language Technology
Perl
Apache Pig
Recommender Systems
Neural Networks
High Performance Computing
Human Computer Interaction
Semantic Web
Pig
Text Classification
Parallel Computing
Text Analytics
Semantic Technologies
Nosql
Web Mining
Sentiment Analysis
Ontologies
Software Development
Modern Saas Architectures
Languages:
English
French
Romanian
Certifications:
Introduction To Operations Management
Introduction To Marketing
Neural Networks and Deep Learning
Structuring Machine Learning Projects
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Convolutional Neural Networks
Machine Learning Foundations: A Case Study Approach
Machine Learning: Regression
Machine Learning: Classification
Sequence Models
License Qmvrnvpj49
License Ncbc8Dm8Gz
Coursera Verified Certificates, License Qmvrnvpj49
Coursera Verified Certificates, License Ncbc8Dm8Gz

Publications

Us Patents

Identifying Documents Which Form Translated Pairs, Within A Document Collection

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US Patent:
7813918, Oct 12, 2010
Filed:
Aug 3, 2005
Appl. No.:
11/197744
Inventors:
Ion Muslea - El Segundo CA, US
Kevin Knight - Marina del Rey CA, US
Daniel Marcu - Hermosa Beach CA, US
Assignee:
Language Weaver, Inc. - Los Angeles CA
International Classification:
G06F 17/27
G06F 17/28
G06F 17/20
US Classification:
704 9, 704 1
Abstract:
A training system for text to text application. The training system finds groups of documents, and identifies automatically similar documents in the groups which are similar. The automatically identified documents can then be used for training of the text to text application. The comparison uses reduced size versions of the documents in order to minimize the amount of processing.

Wrapper Induction By Hierarchical Data Analysis

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US Patent:
6606625, Aug 12, 2003
Filed:
Jun 2, 2000
Appl. No.:
09/587528
Inventors:
Ion Muslea - Culver City CA
Steven Minton - El Segundo CA
Craig A. Knoblock - El Segundo CA
Assignee:
University of Southern California - Los Angeles CA
International Classification:
G06F 1730
US Classification:
707 6, 707 10
Abstract:
An inductive algorithm, denominated STALKER, generating high accuracy extraction rules based on user-labeled training examples. With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem. A vital component of any Web-based information agent is a set of wrappers that can extract the relevant data from semistructured information sources. The novel approach to wrapped induction provided herein is based on the idea of hierarchical information extraction, which turns the hard problem of extracting data from an arbitrarily complex document into a series of easier extraction tasks. Labeling the training data represents the major bottleneck in using wrapper induction techniques, and experimental results show that STALKER performs significantly better than other approaches; on one hand, STALKER requires up to two orders of magnitude fewer examples than other algorithms, while on the other hand it can handle information sources that could not be wrapped by prior techniques. STALKER uses an embedded catalog formalism to parse the information source and render a predictable structure from which information may be extracted or by which such information extraction may be facilitated and made easier.
Ion A Muslea from El Segundo, CA, age ~57 Get Report