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Masum Hossain Serazi

from Piscataway, NJ
Age ~52

Masum Serazi Phones & Addresses

  • 44 Royal Dr, Piscataway, NJ 08854 (732) 968-2169
  • 6 Moryan Rd, Edison, NJ 08817
  • 1602 Dakota Dr, Fargo, ND 58102
  • 180 University Dr, Fargo, ND 58102
  • 910 Alleghany Dr, Arlington Heights, IL 60004 (847) 392-9435
  • Somerset, NJ
  • Brookings, SD
  • Minneapolis, MN

Work

Position: Clerical/White Collar

Education

Degree: High school graduate or higher

Resumes

Resumes

Masum Serazi Photo 1

Senior Software Engineer

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Location:
Edison, NJ
Industry:
Computer Software
Work:
Bloomberg since Jan 2011
Sr. Software Engineer

Ask.com May 2010 - Feb 2011
Principal Software Engineer

Ask.com Oct 2005 - Apr 2010
Sr. Software Engineer & Relevancy Analyst

ITS, NDSU Sep 2004 - Oct 2005
Project Manager

Computer Science Department, NDSU, Fargo, ND Aug 2001 - Sep 2005
Chief Programmer Analyst
Education:
North Dakota State University 1999 - 2005
PhD, Computer Science
Bangladesh University of Engineering and Technology 1991 - 1996
BSc, Engineering
Govt. P.C. College, Bagerhat 1987 - 1989
H.Sc., Science
Skills:
Mysql
Distributed Systems
Subversion
Linux
Algorithms
C
Perl
Agile Methodologies
Ajax
Tcp/Ip
Search Engine Technology
Eclipse
Apache
Java
Xml
Ipc
Php
C++
Sql
Python
Machine Learning
Unix
Data Mining
Interests:
Social Services
Children
Economic Empowerment
Civil Rights and Social Action
Politics
Education
Environment
Poverty Alleviation
Science and Technology
Disaster and Humanitarian Relief
Human Rights
Health
Masum Serazi Photo 2

Masum Serazi

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Publications

Us Patents

Method And System For Data Mining Of Very Large Spatial Datasets Using Vertical Set Inner Products

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US Patent:
20080109437, May 8, 2008
Filed:
Nov 17, 2005
Appl. No.:
11/791004
Inventors:
William K. Perrizo - Fargo ND, US
Taufik Fuadi Abidin - Piscataway NJ, US
Amal Shehan Perera - Knoxville TN, US
Masum Serazi - Edison NJ, US
International Classification:
G06F 7/08
G06F 17/30
US Classification:
707 7, 7071041, 707E17009, 707E17046
Abstract:
A system and method for performing and accelerating cluster analysis of large data sets is presented. The data set is formatted into binary bit Sequential (bSQ) format and then structured into a Peano Count tree (P-tree) format which represents a lossless tree representation of the original data. A P-tree algebra is defined and used to formulate a vertical set inner product (VSIP) technique that can be used to efficiently and scalably measure the mean value and total variation of a set about a fixed point in the large dataset. The set can be any projected subspace of any vector space, including oblique sub spaces. The VSIPs are used to determine the closeness of a point to a set of points in the large dataset making the VSIPs very useful in classification, clustering and outlier detection. One advantage is that the number of centroids (k) need not be pre-specified but are effectively determined. The high quality of the centroids makes them useful in partitioning clustering methods such as the k-means and the k-medoids clustering. The present invention also identifies the outliers.
Masum Hossain Serazi from Piscataway, NJ, age ~52 Get Report