Research Areas
The Applied Machine Learning and Intelligence (AMLI) Lab conducts interdisciplinary research that advances trustworthy, explainable, and responsible artificial intelligence for real-world applications. Our work combines fundamental AI research with practical solutions that address societal challenges in healthcare, cybersecurity, intelligent systems, and organizational decision-making.
The lab collaborates with academic institutions, healthcare organizations, government agencies, and industry partners to develop innovative AI technologies while providing undergraduate and graduate students with hands-on research experiences.
We develop AI systems that are transparent, explainable, reliable, fair, and accountable. Our research investigates methods that improve trust in AI-assisted decision-making and support the responsible deployment of AI technologies across critical domains.
Representative topics include:
- Explainable Artificial Intelligence (XAI)
- Trustworthy and Responsible AI
- Human-AI Collaboration
- AI Governance and Responsible AI
- AI Transparency and Accountability
- AI Evaluation and Validation
Our research explores the next generation of intelligent AI systems, including large language models, generative AI, and emerging agentic AI. We investigate how these technologies can safely augment human expertise while maintaining reliability, transparency, and organizational trust.
Representative topics include:
- Large Language Models (LLMs)
- Generative AI Applications
- Agentic AI Systems
- Intelligent Decision Support Systems
- Responsible Deployment of AI Systems
The AMLI Lab develops AI-driven solutions that improve healthcare delivery, clinical decision support, patient safety, and medical documentation. Our research emphasizes trustworthy clinical AI that enhances healthcare professionals rather than replacing them.
Representative topics include:
- Clinical Artificial Intelligence
- Healthcare Informatics
- Clinical Decision Support Systems
- AI-Driven Clinical Documentation
- Clinical Speech-to-Text Systems
- Mental Health Informatics
- Medical Data Analytics
- Privacy, Security, and Governance in Healthcare AI
We investigate how artificial intelligence can improve cybersecurity through intelligent threat detection, cyber defense, risk analysis, and organizational resilience. Our work also examines emerging AI-enabled cyber threats and strategies for responsible defense.
Representative topics include:
- AI-Driven Cyber Defense
- Ransomware Detection and Mitigation
- Cryptojacking Detection
- Cloud Security
- Cyber Threat Intelligence
- Security Analytics
- Explainable Cybersecurity
- Organizational Cyber Resilience
Our research develops advanced machine learning methods for extracting meaningful insights from structured, unstructured, temporal, and multimodal data. We focus on building predictive and interpretable models that support complex decision-making.
Representative topics include:
- Machine Learning
- Deep Learning
- Predictive Analytics
- Data Science
- Multimodal AI
- Time Series Analysis
- Pattern Recognition
- Knowledge Discovery
We develop computer vision and perception systems that enable intelligent understanding of visual environments for healthcare, transportation, safety, and industrial applications.
Representative topics include:
- Computer Vision
- Image Analysis
- Video Analytics
- Activity Recognition
- Object Detection
- Intelligent Monitoring Systems
- Vision-Based Decision Support