Stanford University
Showing 10,851-10,860 of 36,231 Results
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Benjamin Good
Assistant Professor of Applied Physics and, by courtesy, of Biology
BioBenjamin Good is a theoretical biophysicist with a background in experimental evolution and population genetics. He is interested in the short-term evolutionary dynamics that emerge in rapidly evolving microbial populations like the gut microbiome. Technological advances are revolutionizing our ability to peer into these evolving ecosystems, providing us with an increasingly detailed catalog of their component species, genes, and pathways. Yet a vast gap still remains in understanding the population-level processes that control their emergent structure and function. Our group uses tools from statistical physics, population genetics, and computational biology to understand how microscopic growth processes and genome dynamics at the single cell level give rise to the collective behaviors that can be observed at the population level. Projects range from basic theoretical investigations of non-equilibrium processes in microbial evolution and ecology, to the development of new computational tools for measuring these processes in situ in both natural and experimental microbial communities. Through these specific examples, we seek to uncover unifying theoretical principles that could help us understand, forecast, and eventually control the ecological and evolutionary dynamics that take place in these diverse scenarios.
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Julie Good, MD
Clinical Professor, Anesthesiology, Perioperative and Pain Medicine
Clinical Professor (By courtesy), PediatricsCurrent Research and Scholarly InterestsJulie's academic interests include pediatric palliative care, pain and symptom management for children with life-threatening illness, medical acupuncture, and meaning in medicine (the humanistic side of doctoring)
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Zinaida Good, Ph.D.
Assistant Professor of Medicine (Immunology and Rheumatology)
Current Research and Scholarly InterestsOur laboratory integrates cutting-edge synthetic biology, immunology, and machine learning to engineer T cell therapies for cancer and autoimmune diseases. We have 3 research areas:
- Analysis of clinical single-cell and spatial transcriptomics datasets from T cell therapy trials to identify mechanisms of resistance
- Building AI systems to generate T cell designs predicted to improve patient outcomes
- Genetic screens of novel T cell designs in models that mimic key mechanisms of resistance
