Network Science

Signed Graph Analysis

Signed graphs provide more information than unsigned graph networks. In this project we analyze and characterize signed graphs, propose a novel way to characterize them and discover communities.

We expand the balance theory to signed social network graph analysis and propose a frustration cloud view of the signed graph. We then quantify vertex and edge int erms of frustration cloud statistics, and validate this novel social network graphs analysis approach.

Community Discovery approaches suffer for lack of scalability or reproducability, and high modularity seems to be hard to overcome. We conduct a detailed analysis in terms of efficacy, efficiency, scalability, and reproducability of existing methods, and propose frustration cloud based approach for cluster boosting for high modular signed graphs.

Unsigned Graph Analysis

We mine the ways users interact in the large social media networks to enrich content classification and community discovery.

Scalable end-to-end Twitter network data management pipeline that gathers, stores, and models rich relationships from Twitter networks, and analyzes Twitter data using a combination of graph-clustering and topic modeling techniques at scale using multiple data science methods for graph construction and tweet data processing.

The large number of users, messages, and tags makes it difficult to separate interesting and meaningful conversation threads from the spread of fake news, malicious accounts, background noise, or irrelevant trolling on Twitter. We answer if the content of Tweets be classified based on interactions alone, and when and how well can community classification predict the content class.

Deep Learning in Computer Vision

Analysis of Objects in Overhead Video Streams

Object localization, identification, multi-camera tracking, and activity detection.

Activity Recognition in Overhead Imagery

Robust, adaptable, intuitive implementation for automated early activity warning in maritime scenarios (piracy) or smart city setup. We expand computing with words paradigm to augment training data and identify threating activities where lack of training data prohibits the use of deep learning.

Maritime Object Localization and Identification

We expand the recent advances in deep learning to design and train algorithms to localize, identify, track, and re-identify small maritime objects under varying conditions.

Image Analysis

NewsImages support code for MediaEval 2021.
Deep Learning Framework is sensitive to noisy annotations. This tool extends VIA for COCO JSON compatibility, and allows for labeling on multiple levels of description; it allows Import, Export, and Edit Video Annotations in COCO JSON format.

Iterative computer vision algorithm (based on histogram inference) that in each iteration refines segmentation masks from existing annotation boxes in maritime domain.


A web wrapper for the Boxes2Masks application.

DataLab @ gitHub