Hi, I’m Anirban Saha Anik
About Me
Hello! I'm Anirban Saha Anik, a Ph.D. student in Computer Science and Engineering at the University of North Texas. I completed my M.S. in Data Science at UNT and received my B.Sc. in Computer Science and Engineering from the American International University–Bangladesh (AIUB).
I am currently a Graduate Research Assistant in the Human-Centered Computing Lab under Dr. Lingzi Hong, where I work on building knowledge-aware and cognitively-aware large language model (LLM) systems for high-stakes domains such as health communication and crisis response. My research focuses on retrieval-augmented generation (RAG), multi-agent reasoning, counterspeech against misinformation, and human-centered AI, with the goal of designing AI systems that are trustworthy, explainable, and aligned with human needs. I am particularly interested in integrating knowledge graphs and cognitive signals (e.g., eye-tracking, reading behavior, and user knowledge states) to improve factuality, personalization, and safety in LLM-driven systems.
I am actively seeking Summer 2026 Research Internships and Industry Internships in NLP, Large Language Models, AI Safety, and Responsible AI. If you have opportunities or would like to discuss potential collaborations, please feel free to reach out.
Recent News ⚡
- [January 2026] Started PhD in Computer Science and Engineering at the University of North Texas.
- [December 2025] Graduated with MS in Data Science from the University of North Texas.
- [November 2025] Awarded the Sinha Scholars Scholarship for the 2025–2026 academic year.
- [November 2025] Two papers selected as finalists at SIG-SM Social Media Research Competition 2025 - Multi-Agent RAG Framework (🥈 Runner-Up) and Literacy-Controlled Counterspeech!
- [October 2025] Presented at COLM 2025 Conference in Montreal, Canada!
- [October 2025] Showcased five research posters at University Research Day 2025, UNT:
- Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation [Poster]
- Dynamic Fusion of Large Language Models for Crisis Communication [Poster]
- Integrating Real-time Context into the Language Models for Dynamic Response Generation [Poster]
- Simulating Crisis-Driven Social Media Streams for Context-Aware Language Model Response Evaluation [Poster]
- Leveraging Machine Learning for Early Detection of College Student Mental Health Problems [Poster]
- [September 2025] Two papers accepted at EMNLP 2025 Findings - Literacy-Controlled Counterspeech and Dynamic Fusion for Crisis Response!
- [September 2025] Awarded COLM 2025 Travel Scholarship ($1,225) to attend the Conference on Language Modeling in Montreal!
- [July 2025] Paper accepted at COLM 2025 - Multi-Agent Retrieval-Augmented Framework for countering health misinformation!
- [April 2025] Paper accepted at ISCRAM 2025 - Dynamic Fusion of LLMs for Crisis Communication!
- [October 2024] Won 1st Place at Health Informatics & Data Science Day Poster Competition, University of North Texas 🏆
Selected Publications
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Anirban Saha Anik, Xiaoying Song, Elliott Wang, Bryan Wang, Bengisu Yarimbas, and Lingzi Hong
Conference on Language Modeling, 2025
arXiv:2507.07307 | 🥈 Runner-Up, SIG-SM 2025
Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL
Xiaoying Song, Anirban Saha Anik, Dibakar Barua, Pengcheng Luo, Junhua Ding, and Lingzi Hong
Findings of the Association for Computational Linguistics: EMNLP 2025
DOI: 10.18653/v1/2025.findings-emnlp.153 | 🏆 Finalist, SIG-SM 2025
A Dynamic Fusion Model for Consistent Crisis Response
Xiaoying Song, Anirban Saha Anik, Eduardo Blanco, Vanessa Frías-Martínez, and Lingzi Hong
Findings of the Association for Computational Linguistics: EMNLP 2025
Thank you for visiting my site. I welcome opportunities for collaboration and connection.