Nobin Sarwar

I am a second-year Computer Science Ph.D. student in the Language Understanding Lab at the University of Maryland, Baltimore County, advised by Prof. Francis Ferraro.

My current research studies post-training optimization and system-level methods for LLMs and multimodal foundation models, with a focus on agentic reasoning and controllable adaptation. Ongoing work spans

Reasoning reliability and verification: retrieval-grounded structured inference and agentic retrieve-verify workflows for scientific claim and feasibility assessment, hallucination mitigation in multimodal QA (FilterRAG)
Privacy-preserving and controllable model adaptation: federated fine-tuning of LLMs with Differential Privacy (FedMentor, FedMentalCare), targeted multimodal unlearning (Multimodal Unlearning Survey)

Previously, I earned my MS in Computer Science from the University of Texas Rio Grande Valley, where I worked on privacy-preserving Federated Learning for biometrics and Differential Privacy, contributing to projects that received NSF funding.

Research News (View All)

Apr 06, 2026	🎉 Our Multimodal Unlearning Survey has been accepted as Findings of ACL 2026.
Feb 28, 2026	🚀 Released the repository and project page for our survey paper on Multimodal Unlearning.
Feb 01, 2026	🏛️ Awesome Academic NLP Research Labs Worldwide released — a curated list of academic NLP research labs worldwide.
Jan 21, 2026	📢 Our paper Multimodal Unlearning Survey has been online.
Jan 07, 2026	🎉 Our paper FedMentor has been accepted for poster presentation at the Trusted AI Symposium, hosted by the Amazon AGI team.

Publications Spotlight

Full publication list on Google Scholar →

ACL’26

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, and Vaidehi Patil

In Findings of ACL

TechRxiv Bib Code Website

@inproceedings{sarwar2026mm-unlearning-survey,
  title = {{Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks}},
  author = {Sarwar, Nobin and {Roy Dipta}, Shubhashis and Liu, Zheyuan and Patil, Vaidehi},
  booktitle = {Findings of ACL},
  year = {2026},
  address = {San Diego, California, USA},
  month = jul,
  publisher = {Association for Computational Linguistics},
  techrxiv = {http://dx.doi.org/10.36227/techrxiv.176945748.88280394/v1},
  dimensions = {true},
}

NeurIPS-W’25

FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health

Nobin Sarwar and Shubhashis Roy Dipta

In GenAI4Health Workshop, NeurIPS

arXiv Bib PDF Code Poster

@inproceedings{sarwar2025fedmentor,
  title = {FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health},
  author = {Sarwar, Nobin and {Roy Dipta}, Shubhashis},
  booktitle = {GenAI4Health Workshop, NeurIPS},
  year = {2025},
  url = {https://genai4health.github.io/},
  dimensions = {true}
}

ICCV-W’25

FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA

Nobin Sarwar

In T2FM Workshop, ICCV

arXiv Bib PDF Poster Slides

@inproceedings{sarwar2025filterrag,
  title = {FilterRAG: Zero-Shot Informed Retrieval-Augmented Generation to Mitigate Hallucinations in VQA},
  author = {Sarwar, Nobin},
  booktitle = {T2FM Workshop, ICCV},
  year = {2025},
  url = {https://t2fm-ws.github.io/T2FM-ICCV25/},
  dimensions = {true}
}