Omid Jafari

Omid Jafari
New Mexico State University | NMSU · Department of Computer Science

PhD

About

20
Publications
1,482
Reads
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22
Citations
Introduction
Ph.D. Student of Computer Science at New Mexico State University researching in the area of Big Data Management. My work involves optimizing the queries and indexing in high-dimensional data and optimizing these high-dimensional data structures for various applications such as image and audio retrieval. Main research and work areas include Big Data Management, Data Sciences, Machine Learning, and Distributed Computing.
Additional affiliations
August 2018 - present
New Mexico State University
Position
  • Research Assistant
Description
  • CS 482/502 - Database Management Systems I
January 2018 - present
New Mexico State University
Position
  • Research Assistant
Description
  • Supervisor: Parth Nagarkar
November 2016 - November 2016
Islamic Azad University Mashhad Branch
Position
  • Instructor
Description
  • Taught Matlab Simulink in a workshop held at Azad University of Mashhad.
Education
January 2018 - January 2022
New Mexico State University
Field of study
  • Computer Science
September 2014 - December 2017
Islamic Azad University Mashhad Branch
Field of study
  • Computer Engineering

Publications

Publications (20)
Conference Paper
Similarity search in high-dimensional spaces is an important task for many multimedia applications. Due to the notorious curse of dimensionality, approximate nearest neighbor techniques are preferred over exact searching techniques since they can return good enough results at a much better speed. Locality Sensitive Hashing (LSH) is a very popular r...
Conference Paper
Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Existing Locality Sensitive Hashing (LSH) techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the...
Preprint
Full-text available
Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guaran...
Article
Full-text available
Named Entity Recognition (NER) is a task that involves the keen detection of specific entities in a given text. Existing NER techniques can detect various types of entities in a given text. However, these techniques are not customized to recognize the entities specific to the space domain. Moreover, since the number of satellite being launched are...
Chapter
Similarity search in high-dimensional spaces is an important primitive operation in many diverse application domains. Locality Sensitive Hashing (LSH) is a popular technique for solving the Approximate Nearest Neighbor (ANN) problem in high-dimensional spaces. Along with creating fair machine learning models, there is also a need for creating data...
Preprint
Machine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a res...
Preprint
Video analytics systems perform automatic events, movements, and actions recognition in a video and make it possible to execute queries on the video. As a result of a large number of video data that need to be processed, optimizing the performance of video analytics systems has become an important research topic. Neural networks are the state-of-th...
Chapter
Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many multimedia retrieval applications. Exact tree-based approaches are known to suffer from the notorious curse of dimensionality for high-dimensional data. Approximate searching techniques sacrifice some accuracy while returning good enough results for faster perfo...
Preprint
Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many multimedia retrieval applications. Exact tree-based indexing approaches are known to suffer from the notorious curse of dimensionality for high-dimensional data. Approximate searching techniques sacrifice some accuracy while returning good enough results for fas...
Preprint
Similarity search in high-dimensional spaces is an important task for many multimedia applications. Due to the notorious curse of dimensionality, approximate nearest neighbor techniques are preferred over exact searching techniques since they can return good enough results at a much better speed. Locality Sensitive Hashing (LSH) is a very popular r...
Preprint
Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Locality Sensitive Hashing (LSH) is a very popular approximate technique for finding nearest neighbors in high-dimensional spaces. In order to find top-k s...
Preprint
Full-text available
Nearest-neighbor query processing is a fundamental operation for many image retrieval applications. Often, images are stored and represented by high-dimensional vectors that are generated by feature-extraction algorithms. Since tree-based index structures are shown to be ineffective for high dimensional processing due to the well-known "Curse of Di...
Preprint
Full-text available
Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for finding similar points in high-...
Preprint
Similarity search queries in high-dimensional spaces are an important type of queries in many domains such as image processing, machine learning, etc. Since exact similarity search indexing techniques suffer from the well-known curse of dimensionality in high-dimensional spaces, approximate search techniques are often utilized instead. Locality Sen...
Conference Paper
Similarity search queries in high-dimensional spaces are an important type of queries in many domains such as image processing, machine learning, etc. Since exact similarity search indexing techniques suffer from the well-knowncurse of dimensionality in high-dimensional spaces, approximate search techniques are often utilized instead. Locality Sens...
Conference Paper
امروزه سیستم های صف کاربرد بسیاری در مکان هایی مانند سازمان ها و ادارات دارند و فرآیندهای مارکوف مدلسازی این نوع سیستم ها را بسیار آسان نموده اند. مدلسازی این سیستم ها به این دلیل برای ما اهمیت دارد که با داشتن یک مدل برای سیستم مورد نظر، مدیران و تصمیم گیرندگان در آن سیستم قادر خواهند بود تا یک پیش بینی از وضعیت سیستم در زمان آینده را داشته باشند...

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Cited By

Projects

Projects (2)
Project
Part of the team developing pfLSH, a parameter free method for finding approximate nearest neighbors in high-dimensional spaces. Engineering and scientific applications are always struggling with finding the best input parameters for the algorithms provided by computer programmers. In this project, our focus is on finding a method to intelligently auto-tune the parameters of LSH, so that it can be used easily in many scientific applications.
Project
An index structure for efficiently processing similarity search query workloads in high-dimensional spaces. Based on the important observation that the ratio of index access time to data access time varies based on the cardinality and dimensionality of the dataset, we intelligently divide a given cache during processing of a query workload by using novel cost models. qwLSH will be submitted to VLDB 2019.