vivek sharma

Vivek
Sharma

Doctoral Lecturer
Phone number
(212)484-1176
Room number
6.65.23

Areas of expertise

Faculty Expertise: topics/keywords

Research: Propaganda Detection, Social Science, Natural language Processing application

Teaching: Computer Architecture, Programming Languages, Algorithms

Education
  • Ph.D., Computer Science, Graduate Center, City University of New York(2025)
  • Master in Technology, Computer Science, National Institute of Technology Jamshedpur(2019)
  • Master in Science, Computer Science, University of Mumbai(2016)
  • Bachelor of Science, Computer Science, University of Mumbai(2014)
Courses Taught

Courses Taught

  • CISC 3310 Computer Architecture(Brooklyn College)
  • CISC 3140 Design and Implementation of Large Scale Web Application(Brooklyn College)
  • CSCI 171 Introduction to Computing(Python Programming)
  • CSCI 271 Introduction to Computer Science(C++ programming)

Currently Teaching

  • CSCI 274 Principles of Computer Architecture

Workshops:

  • Python programming
  • Introduction to Web Development
Languages
English, Hindi
Scholarly Work
  1. Sharma, V., Jain, S., Shokri, M., Levitan, S. I., & Filatova, E. (2026, March). Council of LLMs: Evaluating Capability of Large Language Models to Annotate Propaganda. In The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026) (pp. 1-12).
  2. Shokri, M., Sharma, V., Klapper, E., Jain, S., Filatova, E., & Levitan, S. I. (2026, March). The Impact of Highlighting Subjective Language on Perceived News Trustworthiness. In The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026) (pp. 60-72).
  3. Sharma, V., Shokri, M. M., Jain, S., Levitan, S. I., & Filatova, E. (2025, May). Propasafe: A bert-based offline tool for propaganda detection. In Companion Proceedings of the ACM on Web Conference 2025 (pp. 2903-2906).
  4. Sharma, V., Shokri, M. M., Levitan, S. I., Filatova, E., & Jain, S. (2025). Analysis of Propaganda in Tweets From Politically Biased Sources. arXiv preprint arXiv:2507.08169.
  5. Shokri, M., Sharma, V., Filatova, E., Jain, S., & Levitan, S. (2024, August). Subjectivity detection in english news using large language models. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis (pp. 215-226).
  6. Sharma, V., & Halevi, T. (2022, July). A Survey on Research Directions in Blockchain Applications Usability. In Proceedings of Seventh International Congress on Information and Communication Technology: ICICT 2022, London, Volume 2 (pp. 727-738). Singapore: Springer Nature Singapore.
  7. Sharma, V., & Halevi, T. (2022, June). A survey on phishing website detection using deep neural networks. In International Conference on Human-Computer Interaction (pp. 684-694). Cham: Springer Nature Switzerland.
  8. Dinur, I., Goldfeder, S., Halevi, T., Ishai, Y., Kelkar, M., Sharma, V., & Zaverucha, G. (2021, August). MPC-friendly symmetric cryptography from alternating moduli: Candidates, protocols, and applications. In Annual International Cryptology Conference (pp. 517-547). Cham: Springer International Publishing.
Honors and Awards

Grants

  • National Artificial Intelligence Research Resource (Grant# 240383)
  • PSC CUNY Grant (Cycle 55)
Research Summary

Vivek Sharma’s work focuses on the detection and analysis of propaganda and subjectivity within news and social media text. His research spans the full pipeline of computational analysis, beginning with the systematic collection and curation of multi‑platform textual datasets, followed by the development of robust methodologies for identifying persuasive, manipulative, or biased narrative techniques. By integrating natural language processing, machine learning, and human‑centered evaluation, Vivek aims to better understand how propaganda spreads by subtle forms of linguistic framing.

A core aspect of his work involves translating research insights into transparent, reproducible application systems. These tools are designed not only for academic exploration but also for practical deployment, enabling journalists, educators, and the public to critically engage with digital content. Vivek strongly believes that improving digital literacy and fostering transparency are essential to strengthening democratic information ecosystems. His applications therefore emphasize interpretability, openness, and accessibility.

Vivek continually seeks enthusiastic students and collaborators interested in misinformation, computational linguistics, and socially impactful AI systems. He welcomes individuals who share his commitment to advancing digital literacy and developing technologies that support a more informed and resilient public.