The Molloy Lab is a computational biology research group, housed in the Department of Computer Science at the University of Maryland, College Park. Our research is at the intersection of computer science, statistics, and evolutionary genomics; we study algorithms & develop software .
A major focus of our group is the development of methods for reconstructing evolutionary histories from molecular sequence data or features derived from it. This includes phylogenetic trees as well as phylogenetic networks, admixture graphs, and ancestral recombination graphs. Accurate and efficient inference of these graphical models is critical for resolving fundamental questions in biology. We are also interested in applications of these models, i.e., leveraging evolutionary histories to make sense of data coming from medicine, public health, and agriculture.
Broadly speaking, our goal is to enable fast, principled, and robust analyses of the increasingly large and complex data sets being generated today, through method development. To that end, we study methods from the theoretical and empirical perspectives, considering statistical guarantees (like consistency), optimality guarantees, computational complexity, parallel efficiency, and robustness to error and model violations. Our work combines discrete optimization, graph algorithms, statistics, high performance computing, and more recently machine learning.
We are funded by the State of Maryland (start-up to EKM) and the U.S. National Science Foundation (award: DGE-1840340 Graduate Research Fellowship to TR).