Nandita Garud

Nandita is an assistant professor in the Ecology and Evolutionary Biology department at UCLA. She is interested in understanding how natural populations evolve and has been focusing on bacteria in the human microbiome and Drosophila melanogaster.

Nandita completed her M.S. in Statistics and Ph.D. in Genetics at Stanford University in  Dr. Dmitri Petrov’s lab  where she developed a new statistical method to detect signatures of rapid adaptation in Drosophila melanogaster population genomic data. Nandita completed herpostdoctoral work at the Gladstone Institute at UCSF in Dr. Katie Pollard’s lab studying the evolution of bacteria in the human microbiome.

CV, Github, Google Scholar

Email: ngarud at ucla dot edu.

Daisy Chen

Daisy is a third year undergraduate at UCLA majoring in Computer Science and Computational and Systems Biology. She is broadly interested in the use of big data to understand complex biological systems, and she is particularly concerned with issues of environmental sustainability. Daisy hopes to continue studying computational biology in graduate school.

Sara Thornburgh
Sara is a third year undergraduate at UCLA majoring in Ecology, Evolution, and Behavior. She is interested in studying the evolution and ecology of natural populations as well as population genetics. After graduation, Sara hopes to attend graduate school and continue studying Ecology and Evolutionary Biology.


Katherine Zhang

Katherine is a graduating senior at the Harker School in San Jose, California. Over the last two summers, Katherine researched the connection between the microbiome and disease by using machine learning with microbiome data to predict the disease status of human samples for various common diseases, such as colorectal cancer, Crohn’s disease, liver cirrhosis, obesity, rheumatoid arthritis, and Type 2 Diabetes. Katherine has presented work from this project at the Santa Clara Valley Synopsys Science Fair, winning two first prize awards, and at the California Science & Engineering Fair, winning a third place award. She was also named a Siemens Semifinalist in 2017 for a paper she wrote on this work. Katherine plans to study computer science at Harvard University this fall.



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