Research Bio
The Holmes Lab brings techniques from machine learning, statistical linguistics, phylogenetics, and web development to bear on the interpretation and analysis of genomic data. Particular areas of interest include the development of basecalling and analysis software for nanopore sequencing; phylogenetic reconstruction using Bayesian approaches; the use of deep learning to predict and design protein structures; and the development of modern JavaScript user interfaces, backed by fast indexing and in-browser AI, to maximize the impact of genome sequences and annotations.
Research Expertise and Interest
computational biology, molecular evolution, computational genomics, Systems and Synthetic Biology
Teaching
Undergraduate Design Research [BIOENG 196]
Supervised Independent Study [BIOENG 199]
Probabilistic Modeling in Computational Biology [BIOENG 241]
Individual Study or Research [BIOENG 299]
Supervised Independent Study and Research [BIOENG 99]
Honors Undergraduate Research [BIOENG H194]
Undergraduate Design Research [BIOENG 196]
Supervised Independent Study [BIOENG 199]
Group Studies, Seminars, or Group Research [BIOENG 298]
Individual Study or Research [BIOENG 299]
Supervised Independent Study and Research [BIOENG 99]
Introduction to Computational Molecular and Cell Biology [BIOENG C131]
Introduction to Computational Molecular and Cell Biology [BIOENG C231]
Honors Undergraduate Research [BIOENG H194]
Introduction to Computational Molecular and Cell Biology [CMPBIO C131]
Introduction to Computational Molecular and Cell Biology [CMPBIO C231]
Individual Study or Research [BIOENG 299]
Undergraduate Design Research [BIOENG 196]
Supervised Independent Study [BIOENG 199]
Probabilistic Modeling in Computational Biology [BIOENG 241]
Probabilistic Modeling in Computational Biology [BIOENG 241]
Individual Study or Research [BIOENG 299]
Supervised Independent Study and Research [BIOENG 99]
Honors Undergraduate Research [BIOENG H194]