Anirban Saha Anik

Anirban Saha Anik

he/him

MS Data Science @ University of North Texas | LLM Researcher

Posts by Collection

portfolio

publications

A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset

Published in International Conference on Recent Progresses in Science, Engineering and Technology, 2022

A comprehensive comparative analysis of deep learning methods for detecting COVID-19 from chest X-ray images, evaluating multiple CNN architectures.

Recommended citation: Anirban Saha Anik, Kowshik Chakraborty, Bishowjit Datta, Abdul Kader, MD. Kishor Morol. "A Comparative Analysis for the Detection of COVID-19 from Chest X-ray Dataset." International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET 2022). DOI: 10.1109/ICRPSET57982.2022.10188570.
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A Study on Future Lockdown Predictions Using ANN

Published in International Conference on Next-Generation Computing, IoT and Machine Learning, 2023

A study using artificial neural networks to predict future lockdown scenarios during the COVID-19 pandemic based on epidemiological and social data.

Recommended citation: Shuvra Smaran Das, Anirban Saha Anik, Md. Muzakker Hossain, Md. Kishor Morol, Fariha Jahan, Md. Abdullah Al-Jubair. "A Study on Future Lockdown Predictions Using ANN." International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM 2023). DOI: 10.1109/NCIM59001.2023.10212686.
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Outcome-Based Education: Evaluating Students’ Perspectives Using Transformer

Published in 27th International Conference on Computer and Information Technology, 2024

Evaluating students perspectives on outcome-based education using transformer-based natural language processing to analyze educational feedback and sentiments.

Recommended citation: Das, Shuvra Smaran and Anik, Anirban Saha and Morol, Md Kishor and Mahmood, Mohammad Sakib. "Outcome-Based Education: Evaluating Students Perspectives Using Transformer." 2024 27th International Conference on Computer and Information Technology (ICCIT).
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A Hybrid Attention-Guided Fusion Network with Grad-CAM for MPox Skin Lesion Classification

Published in 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI), 2025

A hybrid attention-guided fusion network using Grad-CAM for classifying MPox skin lesions with improved interpretability and diagnostic accuracy.

Recommended citation: M. Arman, N. Prottush, M. A. Rusho, A. Datta, Anirban Saha Anik, D. M. Dohan, M. A. U. I. Sajid, I. A. Sheikh, and M. K. Jahna. "A Hybrid Attention-Guided Fusion Network with Grad-CAM for MPox Skin Lesion Classification." 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI).
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Dynamic Fusion of Large Language Models for Crisis Communication

Published in International Conference on Information Systems for Crisis Response and Management, 2025

We propose a dynamic fusion framework using multiple LLMs to generate high-quality, real-time responses in crisis scenarios on social media.

Recommended citation: Hong, L., Song, X., Saha Anik, A., Frias-Martinez, V. (2025). "Dynamic Fusion of Large Language Models for Crisis Communication." Proceedings of the International ISCRAM Conference.
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Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation

Published in Second Conference on Language Modeling, 2025

A multi-agent LLM framework that generates evidence-based counterspeech grounded in dynamic and static retrieval sources, achieving significant improvements over RAG baselines.

Recommended citation: Anirban Saha Anik, Xiaoying Song, Elliott Wang, Bryan Wang, Bengisu Yarimbas, Lingzi Hong. (2025). "Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation." Conference on Language Models (COLM 2025).
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Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL

Published in The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025 Findings) – Accepted, 2025

We propose a Controlled-Literacy framework that integrates Retrieval-Augmented Generation (RAG) and Reinforcement Learning (RL) to generate counterspeech tailored to different health literacy levels.

Recommended citation: Song, Xiaoying, Anirban Saha Anik, Dibakar Barua, Pengcheng Luo, Junhua Ding, and Lingzi Hong. "Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL." Findings of the Association for Computational Linguistics: EMNLP 2025.
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A Dynamic Fusion Model for Consistent Crisis Response

Published in The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP 2025 Findings), 2025

We introduce a dynamic fusion framework that integrates multiple LLMs to generate consistent, high-quality crisis responses across professionalism, actionability, and relevance dimensions.

Recommended citation: Song, Xiaoying, Anirban Saha Anik, Eduardo Blanco, Vanessa Frias-Martinez, and Lingzi Hong. "A Dynamic Fusion Model for Consistent Crisis Response." Findings of the Association for Computational Linguistics: EMNLP 2025.
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ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims

Published in Working Notes of the Conference and Labs of the Evaluation Forum, 2025

We compare prompted and fine-tuned language models for verifying numerical claims in the CheckThat! Lab Task 3 challenge, demonstrating effective approaches for claim verification.

Recommended citation: Anirban Saha Anik, Md Fahimul Kabir Chowdhury, Andrew Wyckoff, Sagnik Ray Choudhury. "ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims." Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025).
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.