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BioCallie Chappell is a Ph.D. candidate in Ecology and Evolution with the Fukami Lab. Callie is an ecologist and studies how genetic variation influences how ecological communities change over time. Her dissertation research focuses on nectar-inhabiting yeast and bacteria. With a background in bioengineering, Callie is particularly interested in the conservation and policy impacts of gene editing wild organisms and the cascading impacts that genetic variation can have on ecological and evolutionary processes.

Outside of the lab, Callie leads several groups that work in the intersection of science and society. Callie was the 2020-21 President of Stanford Science Policy Group (SSPG), a chapter of the National Science Policy Network and student organization that engages scientists with policy on the local, state, national, and international level. Callie also co-leads BioJam, an education program that collaborates with high school students and community organizations from low- income communities in the Greater Bay Area of California. BioJam participants and organizers learn together about bioengineering and biodesign through the lens of culture and creativity. Callie is also a professional artist and scientific illustrator. Callie has participated in several fellowships at the intersection of science and society including the Mirzayan Science and Technology Policy with the National Academies of Sciences, Engineering, and Medicine (2021), Graduate Ethics Fellow with Stanford’s McCoy Center for Ethics in Society (2019-2020), BioFutures Fellow with the Stanford Bio Policy and Leadership in Society (Bio.Polis) Initiative (2020-2021), and Katherine S. McCarter Policy Fellow with the Ecological Society of America (2020).

Contact Info
Mail Code: 5020
calliech@stanford.edu

Moses Charikar

Donald E. Knuth Professor and Professor, by courtesy, of Mathematics

Current Research and Scholarly InterestsEfficient algorithmic techniques for processing, searching and indexing massive high-dimensional data sets; efficient algorithms for computational problems in high-dimensional statistics and optimization problems in machine learning; approximation algorithms for discrete optimization problems with provable guarantees; convex optimization approaches for non-convex combinatorial optimization problems; low-distortion embeddings of finite metric spaces.