Ahmed Abbasi

Ahmed's research interests include data mining, text mining, machine learning, and information visualization. Specifically, his research focuses on the development and evaluation of predictive technologies for improved online security, fraud detection, and social media analytics.

Ahmed's work has been featured in various media outlets, including the Wall Street Journal, Fox News, the Associated Press, the United Press International, Science Daily, and Phys Org. His work on fraud detection, security, and social media analytics has been funded through multiple grants from the National Science Foundation. 

Social Media Analytics

  • Analysis and Visualization of Computer-mediated Communication 
  • Sentiment and Affect Analysis of Online User-generated Content
  • Detecting Adverse Events using Social Media


Online Security and Fraud Detection

  • Stylometric Identification in Cyberspace
  • Detection of Fraudulent and Phishing Websites
  • Detection of Fraudulent Traders in Online Auctions
  • Detection of Financial Fraud from Public Statements

7. DIBBs for Intelligence and Security Informatics Research Community, (Co-PI), funded by the National Science Foundation (#ACI-1443019), $1,500,000 total, UVA Share: $150,068.

6. Big Data in Business Curriculum – Educate, Explore, Engage, and Execute,” (PI), funded by IBM Faculty Award, $20,000.

5. Large-scale Sentiment Analysis of Social Media, (PI), funded by AWS Educator Research Grant, $5,000.

4. Computational Public Drug Surveillance, (Co-PI), funded by the National Science Foundation (# IIS-1236970 and IIS-1236983), $156,057.

3. A User-Centric Approach to the Design of Intelligent Fake Website Detection Systems, (Co-PI), funded by the National Science Foundation (# CNS-1049497), $280,173.

2. Online Stylometric Authorship Identification: An Exploratory Study, (Project Lead), funded by the National Science Foundation (# IIS-0646942), $75,000.

1. Explosives and IEDs in the Dark Web: Discovery, Categorization, and Analysis, (Project Lead and Subcontractor), funded by the National Science Foundation (# CBET-0730908), $797,447.

Research Grants