Biomedical Informatics and Systems Modeling covers a diverse field at the intersection of computational science, biology and medicine. The overarching goal is to develop machine learning and artificial intelligence methods, mechanistic models, and simulations to describe observed biological phenomena and data, derive new biological insights, and ultimately translate to impacts on scientific discoveries, human health, and patient care. The field uses methodologies in computer science, statistics, electrical engineering, and informatics to integrate experimental and clinical data, derive knowledge, and make actionable decisions. Typical applications include bioinformatics, systems biology, clinical informatics, computational neuroscience, image analysis, precision medicine, and immunoengineering, all of which are highly synergistic to other research areas in the department.
Core Competencies
- Bioinformatics
- Biostatistics
- Machine Learning and Artificial intelligence
- Systems and Computational Biology
- Systems Physiology
- Networks and Graphs Modeling
- Big Data Analytics
- Complex Biomedical Data Integration
- Computational Neuroscience
- Dynamics Modeling in Immunology
- Clinical Informatics
- Public Health Informatics
- Imaging Informatics
- High-performance and Distributed computing
- Text mining and Natural Language Processing
- Wearable Devices and Mobile Health
