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
8+
Publications
23.8
ACM Computing Surveys IF
Tools & Technologies
Languages & Frameworks
Python PyTorch Hugging Face LangChain LangGraph
Agentic & Evaluation
LLM-as-Judge LangFuse 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
Research
Research Themes
Agentic System Evaluation
Designing rigorous evaluation frameworks for LLM-based agents — measuring task success, reasoning quality, faithfulness, latency, and failure modes in research and production settings.
LLM Reliability & Safety
Building trust in large language models through hallucination reduction, structured reasoning, and LLM-as-Judge methodologies for high-stakes clinical and enterprise deployments.
Clinical AI & NLP
Developing conversational AI for cancer care navigation, mental health assessment, and clinical decision support — with emphasis on responsible, patient-safe deployment.
RAG Systems
Designing and evaluating retrieval-augmented generation pipelines across structured, unstructured, and web-based sources — optimising for accuracy, grounding, and production readiness.
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.
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
Review-Based Recommendation Systems: A Survey of Approaches, Challenges and Future Perspectives IF 23.8
ACM Computing Surveys · Ranked 1/143 in CS Theory & Methods
2024
Accommodation Review Ranking for Tourism Recommendation 2nd Place
RecTour 2024 Workshop @ ACM RecSys · Bari, Italy
2024
ViLBias: A Framework for Bias Detection using Linguistic and Visual Cues
arXiv preprint · Raza, Saleh, Hasan et al.
2022
Multicriteria Rating and Review-Based Recommendation Model
IEEE International Conference on Big Data
View All Publications →
Contact
Get in Touch

Open to research collaborations, speaking engagements, and advisory roles in agentic AI evaluation and clinical NLP.