md rayhan importrayhan

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

Building intelligent retrieval systems and conversational AI that bridge the gap between human language and machine understanding

👨‍💻 About Me

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

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

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)"
]
}

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

Building retrieval-augmented generation systems for multi-turn query clarification

Tech: PyTorch, Transformers, FAISS, LangChain
Focus: Combining dense retrieval with LLM generation

🤖 Adversarial LLM Robustness

Evaluating and improving LLM reliability under adversarial inputs

Tech: PyTorch, HuggingFace, Custom datasets
Status: Under review at SIGIR 2026

🎯 GPU-Accelerated Model Training

Fine-tuning transformer models on HPC clusters

Tech: PyTorch, CUDA, Slurm
Scale: Multi-GPU distributed training

📚 Educational AI Systems

ML pipelines for student performance prediction

Tech: scikit-learn, Flask, AWS EC2
Publication: Q1 journal (4 citations)

📈 GitHub Stats

🏆 Key Achievements

📝 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

📚 Publications & Research

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

💡 What I'm Learning

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

🤝 Let's Connect

📌 Pinned Repositories

⭐ Featured Projects Coming Soon:

🔍 RAG-Based Document QA System
🤖 LLM Fine-tuning Pipeline (PyTorch)
📊 Conversational Search Dataset & Benchmarks
🎯 GPU-Accelerated NLP Toolkit

_{Built with 💙 by Rayhan | Powered by curiosity and countless cups of tea ☕}

md rayhan importrayhan

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

👨‍💻 About Me

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

🤖 Adversarial LLM Robustness

🎯 GPU-Accelerated Model Training

📚 Educational AI Systems

📈 GitHub Stats

🏆 Key Achievements

📚 Publications & Research

💡 What I'm Learning

🤝 Let's Connect

📌 Pinned Repositories

@importrayhan's activity is private

md rayhan importrayhan

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

👨‍💻 About Me

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

🤖 Adversarial LLM Robustness

🎯 GPU-Accelerated Model Training

📚 Educational AI Systems

📈 GitHub Stats

🏆 Key Achievements

📚 Publications & Research

💡 What I'm Learning

🤝 Let's Connect

📌 Pinned Repositories

@importrayhan's activity is private

md rayhan importrayhan

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

👨‍💻 About Me

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

🤖 Adversarial LLM Robustness

🎯 GPU-Accelerated Model Training

📚 Educational AI Systems

📈 GitHub Stats

🏆 Key Achievements

📚 Publications & Research

💡 What I'm Learning

🤝 Let's Connect

📌 Pinned Repositories

@importrayhan's activity is private

md rayhan importrayhan

🔬 Research Engineer | 🤖 NLP Specialist | 🚀 LLM Enthusiast

👨‍💻 About Me

🛠️ Technical Stack

Core AI/ML

NLP & LLMs

GPU & Compute

Development

🔬 Research Focus

📊 Current Projects

🔍 RAG-Enhanced Conversational Search

🤖 Adversarial LLM Robustness

🎯 GPU-Accelerated Model Training

📚 Educational AI Systems

📈 GitHub Stats

🏆 Key Achievements

📚 Publications & Research