Capital One
Branch Ambassador
Douglas Elliman Real Estate Oct 2015 - Oct 2016
Real Estate Agent
Charles Rutenberg Realty Inc. Aug 2015 - Apr 2016
Real Estate Agent
Urban Realty Jan 2015 - Aug 2015
Real Estate Agent
Specs New York Dec 2013 - Jul 2014
Manager
Education:
St. Francis College 2016 - 2018
Bachelors, Bachelor of Business Administration, Business Administration, Management, Business Administration and Management
Long Island Business Institute 2012 - 2014
Associates, Accounting
Skills:
Sales Inventory Management Management Merchandising Retail Sales Management Leadership Retail Sales Real Estate Transactions Investment Properties Microsoft Excel Customer Service Powerpoint Microsoft Office Inventory Control Social Media Microsoft Word
Professor Of Electrical Engineering And Computer Science
Case Western Reserve University
Professor of Electrical Engineering and Computer Science
Case Western Reserve University Aug 2008 - Jul 2009
Visiting Associate Professor
Wissenschaftskolleg Berlin, Institute For Advanced Study Aug 2008 - Jul 2009
Fellow
Carnegie Mellon University Jul 1999 - Jul 2008
Associate Professor of Computer Science and Center For the Neural Basis of Cognition
Carnegie Mellon University Jan 1999 - Jun 1999
Research Scientist, Center For the Neural Basis of Cognition
Education:
Caltech 1989 - 1996
Doctorates, Doctor of Philosophy, Philosophy
Carnegie Mellon University 1985 - 1989
Bachelors, Mathematics, Cognitive Science
Jan 2012 to 2000 CLIENT SERVICE SUPERVISORo ML Property Services (LANDSCAPER)
Apr 2009 to 2000 Owner/OperatorPaychex Pittsburgh, PA Aug 2010 to Jan 2012 SENIOR PAYROLL SPECIALSTo ML Property Services (LANDSCAPER) McKees Rocks, PA Jul 2009 to Feb 2010 TELLER/SUPERVISOR
Education:
o Robert Morris University Sep 2005 to Jul 2009 Bachelor's in Business Management
Michael S. Lewicki - Pittsburgh PA Terrence J. Sejnowski - Solana Beach CA
Assignee:
The Salk Institute for Biological Studies - La Jolla CA
International Classification:
G06N 302
US Classification:
706 20, 706 22, 706 25
Abstract:
A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i. e. characteristics) are initially unknown. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.
A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i.e. characteristics) are initially unknown. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.
Unsupervised Adaptation And Classification Of Multiple Classes And Sources In Blind Signal Separation
Michael S. Lewicki - Pittsburgh PA Terrence J. Sejnowski - Solana Beach CA
Assignee:
The Salk Institute for Biological Studies - La Jolla CA
International Classification:
G06N 302
US Classification:
706 20, 600310, 600515
Abstract:
A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters (i. e. characteristics) are initially unknown. The data set may be generated in a dynamic environment where the sources provide signals that are mixed, and the mixing parameters change without notice and in an unknown manner. A mixture model is used in which the observed data is categorized into two or more mutually exclusive classes. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices and bias vectors are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. In some embodiments the class parameters may have been previously learned, and the system is used to classify the data and if desired to separate the sources. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.
Isbn (Books And Publications)
Probabilistic Models of the Brain: Perception and Neural Function