NLP Researcher at MBZUAI · Specializing in LLMs, fact-checking & reasoning · 5 years building AI solutions that matter.
I'm a dedicated Natural Language Processing researcher and data scientist with 5 years of hands-on experience in AI, machine learning, and NLP-driven research. Currently pursuing my M.S. in NLP at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in Abu Dhabi with a GPA of 3.83.
My research focuses on fact-checking, temporal reasoning, and spatial reasoning — areas where I believe AI can have a transformative real-world impact. I'm passionate about pushing the boundaries of what LLMs can understand and reason about.
Beyond research, I have extensive industry experience building scalable data pipelines, AI systems, and full-stack applications that have served enterprise clients across the US and UAE.
Mohamed bin Zayed University of AI (MBZUAI)
GPA: 3.83 / 4.00University of Engineering & Technology (UET)
GPA: 3.49 / 4.00GoPython Software Solutions → Prognos Health Inc
GoPython Software Solutions → Instant Labs Inc
Data Science Lab, Al-Khawarizmi Institute of Computer Science
Data Science Lab, KICS & Sabz Qalam
arXiv preprint · Annual Meeting of the ACL 2026.
Annual Meeting of the Association for Computational Linguistics 2026.
arXiv preprint, 2026.
Annual Meeting of the Association for Computational Linguistics 2026.
arXiv preprint, 2026.
European Conference on Information Retrieval (ECIR) 2026.
Findings of ACL: EMNLP 2025 — Suzhou, China.
Journal of Molecular Biology, 2025.
20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025).
Conference and Labs of the Evaluation Forum (CLEF) 2025.
Current Computer-Aided Drug Design, Vol. 20, Issue 6, pp. 773–783. DOI: 10.2174/1573409920666230817101913
Sabz Qalam, Vol. 1, p. 167. ISBN: 978-969-7941-00-1
Curated a high-quality Urdu MCQ image dataset for multimodal reasoning. Developed baselines using VLMs and LLMs for cross-modal understanding in low-resource languages.
Agentic fact-checking framework for Urdu with evidence boosting, benchmarking, and LLM integration — accepted at EMNLP 2025.
Investigated limitations of ROUGE/entity metrics. Proposed structure-aware LLM evaluation at node, argument, and claim levels for long-form AI-generated content.
ML pipeline leveraging single-cell RNA sequencing data to classify COVID-19 severity. Used PCA + SVM; identified gene biomarkers (RSAD2, CXCL10, S100A8) via SHAP.
Evaluated hate speech detection in German, Hindi, and English using traditional ML and advanced LLMs, demonstrating LLM superiority in low-resource multilingual settings.
Ensemble of LLM embeddings and CNNs for peptide classification, achieving state-of-the-art results for Anti-Viral Peptides without handcrafted features.
Transfer learning with DenseNet-201 for binary classification of histopathology images, achieving ~87% accuracy across multiple magnification levels.
Evaluated 8 ML + 3 DL algorithms for multi-label emotion classification. Custom BERT with RoBERTa embeddings achieved macro F1 of 0.73 on Go-Emotions Dataset.
Cleaned and analyzed global COVID-19 data. Created interactive graphs and 3D geo-scatter plots to visualize global spread patterns over time.
React.js web app enabling users to visually create and connect complex database schemas. Paired with a Blockly-based tool generating SQL queries dynamically at runtime.
Fact-checking, temporal & spatial reasoning, multilingual benchmarking, agentic pipelines, and evaluation frameworks for LLMs.
End-to-end ML pipelines, OMOP CDM data modelling, AWS-driven automation, and enterprise healthcare data systems at scale.
Designing shared tasks, curating datasets, building evaluation pipelines, and publishing in top-tier NLP venues.
React/Go web apps, RESTful APIs, Docker/Kubernetes orchestration, and cloud-native solutions across AWS and Azure.
I'm always open to discussing new research opportunities, collaborations, or interesting projects in NLP and AI. Whether you have a question or just want to say hello, my inbox is always open.