Beatrice M. Bock, Ph.D.
Fungi, ecology, data, and other fun things
About Me
I’m a fungal ecologist and computational biologist interested in plants, fungi, and big datasets.
I currently work with microbial genomes and biological collections at USDA-ARS, after finishing my PhD at Northern Arizona University. My research has taken me from growing plants and fungi in the lab, greenhouse, and field to mining tens of thousands of papers with machine learning.
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Currently
USDA-ARS
Postdoctoral Fellow, ORISE, National Center for Agricultural Utilization Research
Working with microbial genomes and the NRRL culture collection, with a focus on phylogenomics, comparative genomics, and making microbial genetic resources more useful.
I also collaborate on projects involving microbial communities, computational biology, and machine learning.
Experiments with Sorghum and dark septate endophytes testing whether non-mycorrhizal fungi can form common networks and move resources between plants.
What does a fungal symbiosis cost a tree under drought?
Greenhouse and field experiments with Populus hybrids examining fungal colonization, plant traits, and drought response.
AI + Fungal Ecology: Can a computer read 20,000 papers?
A machine-learning pipeline for extracting fungal endophyte occurrence, geography, and ecological information from the scientific literature.
Research
Some different things I've worked on. Click on something to learn more.
Experiments with Sorghum and dark septate endophytes testing whether non-mycorrhizal fungi can form common networks and move resources between plants.
What does a fungal symbiosis cost a tree under drought?
Greenhouse and field experiments with Populus hybrids examining fungal colonization, plant traits, and drought response.
AI + Fungal Ecology: Can a computer read 20,000 papers?
A machine-learning pipeline for extracting fungal endophyte occurrence, geography, and ecological information from the scientific literature.
My current USDA work involves genome curation and phylogenomics of microbial germplasm in the NRRL culture collection.
Machine learning beyond biology
Using graph neural networks to predict the outcomes of microbial community coalescence, with applications ranging from microbial inoculations to soccer transfers. Yes, Messi is in the dataset :)
Beyond (bea-yond?) the Lab
Teaching & mentoring
Undergraduate research, Mycology, data science workshops, and Carpentries.
Community
Invasive-species outreach, department events, and other things that make academic life even more fun.