Blending algorithms and Bayesian machine learning in computational biology.
The Efficient Learning and Graph Algorithm Techniques for Omics (EL GATO) Lab at UConn was founded in 2018 and includes researchers with a wide range of interests and specialties. Our research aims to develop probabilistic machine learning models, combinatorial algorithms, and scalable inference methods to better understand high-dimensional data, particularly genomics and genetics data applied to complex disease.
Some recent news
Prof. Derek presented the group’s work titled “Deep statistical modelling of nanopore sequencing translocation times reveals latent non-B DNA structures” in Brown University’s Center for Computational Molecular seminar series.
UConn Today article about the lab
Aguiar, an associate professor of computer science, uses his computational expertise to help understand complex diseases – and develop presidential chatbots. See the full story.
Our Research
We focus on Bayesian machine learning and combinatorial methods development across a wide range of areas.