CV

Education

• Bachelor of Technology. in Computer Science and Engineering (CSE), University of Petroleum and Energy Studies, Dehradun, India, 2013
• M.S. (by Research) in Speech Signal Processing (CSE), Indian Institute of Information Technology Sri City, Chittoor, India, 2019
  • Thesis
• Ph.D in Detecting and Characterizing Harmful Memes: Techniques and Applications (EE-Computer Technology), Indian Institute Of Technology Delhi (IIT Delhi), New Delhi, India, (Expected thesis submission - Dec’24)

Work experience

• September 2019 - Present: Research Lead
  • Wipro R&D (Lab45), Wipro Ltd., India
  • Duties included: R&D, Data Analysis, POC Web Applications for AI systems
  • Projects: Hate-speech ELIcitation and Observation System (HELIOS), Domain Adaptive Fake News Verification, and Precision Treatment Pathways (PTP)
• October 2018 - March 2019: Research Trainee
  • IDIAP Research Institute, Martigny, Switzerland
  • Duties included: Investigating end-to-end modeling of acoustic cues within infant cries using 1-D CNN (part of masters thesis).
  • Supervisor: Dr. Mathew Magimai Doss
• Fall 2015 - Spring 2016: Research Associate
  • IIIT Sri City, Chittoor, India
  • Duties included: Investigating fall detection sensor (IOT application).
  • Supervisor: Dr. Vinay Kumar Mittal
• May 2014 - June 2015: Programmer Analyst
  • Cognizant Technology Solutions Pvt. Ltd., India
  • Duties included Development and Support Operations - Data warehousing and Business Intelligence (Healthcare).

For more information, please refer to the detailed CV.

Publications

Detecting Harmful Memes and Their Targets

Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021. Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2783–2796, Online. Association for Computational Linguistics.

MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets

Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021. MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics.

Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes

Shivam Sharma, Tharun Suresh, Atharva Kulkarni, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 1–11, Dublin, Ireland. Association for Computational Linguistics.

Detecting and Understanding Harmful Memes: A Survey

Sharma, Shivam and Alam, Firoj and Akhtar, Md. Shad and Dimitrov, Dimitar and Da San Martino, Giovanni and Firooz, Hamed and Halevy, Alon and Silvestri, Fabrizio and Nakov, Preslav and Chakraborty, Tanmoy. (2015). "Detecting and Understanding Harmful Memes: A Survey.” Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22.

DISARM: Detecting the Victims Targeted by Harmful Memes

Shivam Sharma, Md Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2022. DISARM: Detecting the Victims Targeted by Harmful Memes. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1572–1588, Seattle, United States. Association for Computational Linguistics.

Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis

Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 792–805, Online only. Association for Computational Linguistics.

Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?

Shivam Sharma, Atharva Kulkarni, Tharun Suresh, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, and Tanmoy Chakraborty. 2023. Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2149–2163, Dubrovnik, Croatia. Association for Computational Linguistics.

MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization

Shivam Sharma, Ramaneswaran S, Udit Arora, Md. Shad Akhtar, and Tanmoy Chakraborty. 2023. MEMEX: Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5272–5290, Toronto, Canada. Association for Computational Linguistics.

What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

Sharma, S., Agarwal, S., Suresh, T., Nakov, P., Akhtar, M. S., & Chakraborty, T. (2023). What Do You MEME? Generating Explanations for Visual Semantic Role Labelling in Memes. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9763-9771.

Emotion-Aware Multimodal Fusion for Meme Emotion Detection

S. Sharma, R. S, M. S. Akhtar and T. Chakraborty, "Emotion-Aware Multimodal Fusion for Meme Emotion Detection," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2024.3378698.

MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing

Siddhant Agarwal, Shivam Sharma, Preslav Nakov, and Tanmoy Chakraborty. 2024. MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing. In Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand. Association for Computational Linguistics.

Factuality Challenges in the Era of Large Language Models and Opportunities for Fact-Checking

Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty*, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, Giovanni Zagni, Factuality Challenges in the Era of Large Language Models, In Nature Machine Intelligence, 2024.