Faculty Recruiting Support CICS

Anna Green

Assistant Professor
348 CS Building
(413) 577-0354


Computational biology, machine learning, genomics, antibiotic resistance


Biology has become a computational science.  Thanks to affordable DNA sequencing technology, humanity can now read the genetic code responsible for all forms of life on our planet. However, deciphering the immense complexity and diversity within genomes remains a formidable task, spurring the development of computational tools that can interpret the effects of genetic variation and its impact on living organisms.  Dr. Green's research interest lies in understanding how bacteria evolve to become resistant to antibiotic treatment. We employ computational biology and machine learning models, incorporating multiple sources and modalities of data, to interpret the intricate relationship between genetic variation and antibiotic resistance. The dual goals are to improve computational identification of antibiotic resistant bacteria, understand the biology underlying the evolution of resistance, and build computational tools for the new era of DNA sequencing.


Anna G. Green is an Assistant Professor in the College of Information and Computer Sciences at UMass Amherst. She received her PhD in Systems Biology from Harvard University, where she did her thesis research on developing and applying computational models of protein sequences in Debora Marks's lab. She did her postdoctoral research in Maha Farhat's lab at the Harvard Medical School Department of Biomedical Informatics, where she developed computational models to understand the genetic basis of evolution and antibiotic resistance in Mycobacterium tuberculosis. She holds an undergraduate degree in Molecular and Cell Biology from the University of Connecticut.

Activities & Awards

Dr. Green has a strong track record of external grant support for her research funding, including a NIAID F32 fellowship to support her postdoctoral research, and an NSF GRFP supporting her PhD. She is committed to advancing the role of women and underrepresented minorities in computational fields.