Research Areas:
- Biomedical Informatics
- Artificial Intelligence (AI)
- Machine Learning
- Pharmacogenomics
- Structural Bioinformatics
- Natural Language Processing
- Knowledge Graphs
- Drug Discovery
- Pharmacology
We develop a wide range of machine learning algorithms and are especially interested in extracting disease insights from population genomics and epigenomics. On the methodology side, we are investigating new approaches to adaptive data analysis, representation learning for bio-medical data, new probabilistic models that encourage diversity, and multi-view learning. Application topics include: whole-genome and exome sequence analysis, risk prediction, synthetic biology, chromatin dynamics and transcription regulation.
Research Areas:
- Machine/Statistical Learning
- AI for Health
- Computational Biology
Our group’s research develops artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. We have a primary focus on computer vision, and developing algorithms to perform automated interpretation and understanding of human-oriented visual data across a range of domains and scales: from human activity and behavior understanding, to human anatomy, and human cell biology. Current projects include computer vision for extracting insights and knowledge from visual data ranging from surgery and behavioral science videos, to cell imaging data.
esearch Areas:
- Machine/Statistical Learning
- Deep Learning
- Bioinformatics
- Computer Vision
- Medical Imaging
- Computational Biology
Current interest centers on the application of statistics to problems arsing from biology. We are particularly interested in questions concerning gene regulation and signal transduction.
Research Areas:
- Computational Biology
- Statistics