While our interests encompass a wide range of topics, our core mission revolves around identifying targets and deepening our understanding of their biology at scale. More specifically, our department is actively engaged in ongoing research across the following applied domains:

• Learning and deploying foundational representations of cells and their spatial organization (CSO)
• Understanding complex interactions in disease systems via stem-cell derived organoid models (MAGIC)
• Designing regulatory elements and genome engineering (ReLU)
• Advance foundational machine learning for biology (DELTA)
• Inferring gene functions using perturbation screen (Perturbations)

In addition to our applied research efforts, we maintain a dedicated theoretical section committed to advancing the theory and algorithms of machine learning.