The Zitnik Lab opened doors in December 2019!
AI holds tremendous promise in enabling scientific breakthroughs and discoveries in diverse areas. We investigate applied machine learning with a current focus on networked systems that require infusing structure and domain knowledge.
Our research strategy is to create foundational models, including pre-trained models, self-supervised models, general-purpose models, multi-purpose models, and multi-modal models, that are trained on broad data at scale. We:
- Invent ways to infuse domain knowledge & structure into complex and heterogeneous datasets to reduce uncertainty and enable generalization to entirely new scenarios not seen during training.
- Develop methods that produce actionable & trustworthy representations and can reason over massive datasets.
- Translate machine learning research into innovative applications.
This research creates new avenues for network biology, developing safe & effective medicines, and giving the right patient the right treatment at the right time to have medicinal effects that are consistent from person to person and with results in the laboratory.
AI4Science
Our world is interconnected, from the molecular level to the level of connections between diseases in a person, and all the way to the societal level encompassing human interactions within a society. These interactions at different levels give rise to a bewildering degree of complexity.
To disentangle the complexity, science inextricably relies on the existence of scientific instruments. While in the past science used physical instruments to facilitate the discoveries, modern science needs the new kind of instruments, which will, we postulate, in a vital way be optimized for learning and reasoning from data.
Read about our research. . .
