Machine learning can accelerate molecular discovery by designing molecules and planning experiments. However, many scientific challenges demand molecules with very rare properties, and in this sparse ...

Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data. We develop geometric ...

A generative modeling framework enables large-scale designed DNA synthesis, yielding candidate cell therapies.

Advance large scale data generation for chemical and biological AI in a 3 year PhD. Work on the frontier of active learning, and develop novel probabilistic machine learning techniques for experiment ...

Advance large scale data generation for chemical and biological AI in a 3 year PhD. Work on the frontier of active learning, and develop novel probabilistic machine learning techniques for experiment ...

VISTA, an AI-controlled data loop system, guides large-scale antibody design from scratch for applications in cancer therapy.

Lund University. Lund University’s body of researchers is getting reinforcements. If all goes according to plan, no fewer than 25 new researchers from all over the world will arrive in Lund this autum...

Calendar of the Machine Learning Department at Carnegie Mellon University

If you are an ambitious early-career scientist ready to propel your research to new heights, we invite you to be part of AITHYRA’s founding generation of Principal Investigators. Together, we will sha...

Abstract: Generative models offer a powerful paradigm for designing novel functional DNA, RNA and protein sequences. In this talk, I introduce a method to efficiently synthesize designs from generative models in the real world. The method involves a combined machine learning and wet lab procedure that implements generative sampling algorithms physically, through controlled stochastic chemical reactions.