Explore Fuad Hasan's research interests in explainable machine learning, healthcare AI, biomedical signals, and federated learning.
Research focus on trustworthy machine learning, healthcare AI, and biomedical data science.
Machine Learning
Building ML models whose decisions can be understood and trusted by clinicians and policymakers using SHAP, LIME, and attention-based methods.
Title: Interpretable & Explainable Machine Learning
Description: Building ML models whose decisions can be understood and trusted by clinicians and policymakers using SHAP, LIME, and attention-based methods.
Icon: brain
Category: Machine Learning
Tags: XAI
Healthcare AI
Developing AI systems that are safe, fair, and generalizable across diverse clinical settings and patient populations.
Title: Trustworthy Healthcare AI
Description: Developing AI systems that are safe, fair, and generalizable across diverse clinical settings and patient populations.
Icon: shield
Category: Healthcare AI
Tags: Trustworthy AI
Healthcare AI
Applying deep learning to ECG, MRI, and other biomedical modalities for automated diagnostic support and clinical triage.
Title: Medical Imaging & Biomedical Signals
Description: Applying deep learning to ECG, MRI, and other biomedical modalities for automated diagnostic support and clinical triage.
Icon: heart
Category: Healthcare AI
Tags: ECG, MRI
Deep Learning
Designing neural architectures including CNNs, Transformers, and hybrid models for risk stratification and patient outcome prediction.
Title: Deep Learning for Clinical Decision Support
Description: Designing neural architectures including CNNs, Transformers, and hybrid models for risk stratification and patient outcome prediction.
Icon: layers
Category: Deep Learning
Tags: Clinical AI
Healthcare AI
Training models across distributed hospital data silos without centralizing sensitive patient information, preserving privacy by design.
Title: Federated Learning in Healthcare
Description: Training models across distributed hospital data silos without centralizing sensitive patient information, preserving privacy by design.
Icon: network
Category: Healthcare AI
Tags: Federated Learning
Language Models
Using large language models to synthesize clinical literature, generate evidence cards, and support structured medical decision-making.
Title: LLM-Assisted Clinical Evidence Systems
Description: Using large language models to synthesize clinical literature, generate evidence cards, and support structured medical decision-making.
Icon: book
Category: Language Models
Tags: LLM