Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding
I'm a Computing graduate specializing in Natural Language Processing, Information Retrieval, and Large Language Models. My research focuses on making search systems more intelligent through conversational AI and ambiguity detection.
- 🎓 MSc Computing from Edinburgh Napier University
- 🔍 Research: Clarification Need Prediction in Multi-turn Conversational Search
- 📝 Published in Computers and Education: Artificial Intelligence (Q1)
- 📄 Two papers submitted to SIGIR 2026 on adversarially robust LLMs
- 🌱 Currently exploring RAG architectures and production LLM deployment
- 💼 Former Research Associate building ML systems for education
- 🌍 Based in Edinburgh, UK
research_interests = {
"Information Retrieval": [
"Conversational Search",
"Query Understanding",
"Ambiguity Detection",
"Clarification Generation"
],
"Large Language Models": [
"Fine-tuning & Prompt Engineering",
"Adversarial Robustness",
"Retrieval-Augmented Generation (RAG)",
"Model Evaluation & Benchmarking"
],
"NLP Applications": [
"Multi-turn Dialogue Systems",
"Search Intent Classification",
"Semantic Search",
"Document Understanding"
],
"ML Systems": [
"Production ML Pipelines",
"Model Deployment (AWS, Flask)",
"GPU-Accelerated Training",
"Distributed Computing (Slurm)"
]
}|
Building retrieval-augmented generation systems for multi-turn query clarification
|
Evaluating and improving LLM reliability under adversarial inputs
|
|
Fine-tuning transformer models on HPC clusters
|
ML pipelines for student performance prediction
|
- 📝 Published Research: Q1 journal in AI & Education (cited 4 times)
- 🎓 Academic Excellence: Dean's List & Vice Chancellor's List (3 consecutive terms)
- 🚀 Production Impact: Built ML systems deployed on AWS serving real users
- 📊 Industry Impact: Led platform revamp achieving 26% engagement improvement
- 🔬 Conference Submissions: 2 papers under review at SIGIR 2026
- 🏅 MSc Dissertation: Custom LLM fine-tuning for conversational search
-
Rayhan, M., Ullah, M. Z. (2026). Early Identification of Ambiguous Search Queries Using Adversarially Robust Large Language Models. Submitted to SIGIR 2026 (Long Track). [Under Review]
-
Rayhan, M., Ullah, M. Z. (2026). Adversarial Supervised Fine-Tuning for Robust Ambiguity Detection in Queries for Conversational Search. Submitted to SIGIR 2026 (Short Track). [Under Review]
-
Rayhan, M., Alam, M. G. R., Dewan, M. A. A., & Ahmed, M. H. U. (2022). Appraisal of high stake examinations during SARS-CoV-2 emergency with responsible and transparent AI: Evidence of fair and detrimental assessment. Computers and Education: Artificial Intelligence. [Q1, Cited: 4] 📄 Paper
graph LR
A[Current Focus] --> B[RAG Architectures]
A --> C[Vector Databases]
A --> D[LLM Production Deployment]
A --> E[Prompt Engineering]
B --> F[FAISS/Pinecone]
C --> F
D --> G[TensorRT/ONNX]
E --> H[Chain-of-Thought]
- 🔍 RAG Systems: Building production-grade retrieval-augmented generation pipelines
- ⚡ Model Optimization: TensorRT, ONNX for efficient inference
- 🗄️ Vector Databases: FAISS, Pinecone, Weaviate for semantic search
- 🎯 Advanced Prompting: Chain-of-thought, few-shot learning, prompt optimization
- 🏗️ LLM Agents: Building autonomous systems with LangChain & LlamaIndex
⭐ Featured Projects Coming Soon:
- 🔍 RAG-Based Document QA System
- 🤖 LLM Fine-tuning Pipeline (PyTorch)
- 📊 Conversational Search Dataset & Benchmarks
- 🎯 GPU-Accelerated NLP Toolkit