Emrul Hasan
Emrul Hasan
Postdoctoral Research Fellow · UBC & BC Cancer
Agentic AI Technical Specialist · Vector Institute

I am a Postdoctoral Research Fellow at the University of British Columbia and BC Cancer, developing agentic AI systems for healthcare — including conversational assistants for cancer patients and clinicians. My work centres on design, evaluation, and reliability of large-scale agentic architectures and RAG systems in high-stakes clinical settings.

As Agentic AI Technical Specialist at the Vector Institute (2+ years), I have advised 10+ startups on production agentic AI — multi-hop RAG pipelines, tool-augmented agents, and rigorous evaluation frameworks for reliability and failure-mode analysis. At Amazon, I pioneered LLM-as-Judge and LLM-as-Jury pipelines for RAG evaluation, improving contact deflection by 2% and cutting manual review effort by four weeks.

I publish in ACM Computing Surveys (IF 23.8), IEEE, and ACM RecSys.

10+
Start-ups consulted
2+
Yrs at Vector Institute
Intern
Applied Scientist · Amazon
8+
Publications
Tools & Technologies
Languages & Frameworks
Python PyTorch Hugging Face LangChain LangGraph
Agentic & Evaluation
LLM-as-Judge ReAct Agents RAG Evaluation RAGAS
Cloud & Platforms
AWS SageMaker AWS Bedrock EC2 / S3 GCP Elasticsearch
NLP & ML
BERT / Transformers Contrastive Learning Multimodal LLMs Prompt Engineering Fine-Tuning
Research
Research Themes
NLP & GenAI
Research on natural language processing and generative AI — including large language models, RAG systems, text understanding, clinical NLP, and conversational AI for cancer care and mental health.
Agentic AI and Evaluation
Designing and evaluating LLM-based agentic architectures — multi-hop RAG pipelines, tool-augmented agents, LLM-as-Judge/Jury frameworks, and rigorous evaluation for production and clinical settings.
Recommendation Systems
Multi-criteria, review-based, and explainable recommendation using deep learning, contrastive learning, and NLP — with publications in ACM Computing Surveys (IF 23.8) and ACM RecSys.
Trustworthy and Reliable AI
Building trust in AI through hallucination reduction, fairness, bias detection in multimodal content, and responsible deployment — with emphasis on accountability in high-stakes clinical and enterprise settings.
Publications
Selected Publications
2025
Contrastive Learning for Aspect Representation towards Explainable Recommendation Best Student Paper
24th IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology
2024
ACM Computing Surveys · Ranked 1/143 in CS Theory & Methods
2024
RecTour 2024 Workshop @ ACM RecSys · Bari, Italy
2024
arXiv preprint · Raza, Saleh, Hasan et al.
2022
IEEE International Conference on Big Data
View All Publications →