Stanford University
Showing 4,521-4,540 of 6,597 Results
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Alejandro Pulido
Undergraduate, Mechanical Engineering
BioSophomore in the class of 2028 studying Mechanical Engineering!
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Patrick Lee Purdon
Professor of Anesthesiology, Perioperative and Pain Medicine (Department Research) and, by courtesy, of Bioengineering
BioMy research integrates neuroimaging, biomedical signal processing, and the systems neuroscience of general anesthesia and sedation.
My group conducts human studies of anesthesia-induced unconsciousness, using a variety of techniques including multimodal neuroimaging, high-density EEG, and invasive neurophysiological recordings used to diagnose medically refractory epilepsy. We also develop novel methods in neuroimaging and biomedical signal processing to support these studies, as well as methods for monitoring level of consciousness under general anesthesia using EEG. -
Stanley Qi
Associate Professor of Bioengineering
BioStanley Qi (publishing as Lei S. Qi) is a pioneer in the field of genome engineering and the architect of the foundational technologies that transitioned CRISPR from a "cutting" tool into a universal platform for Programmable Biology. As the inventor of CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa), Qi established the first methods for the precise, reversible, and targeted regulation of the human genome without altering the DNA sequence.
The Qi Lab integrates scalable genomic perturbation with live-cell and super-resolution imaging and computation-guided design to redefine the boundaries of cellular control. Under Dr. Qi’s leadership, the group has fundamentally expanded the genome engineering toolbox, evolving CRISPR from a single editing tool into a multidimensional platform for the precise control of dynamic and spatial cell states. This work includes establishing foundational technologies and architectures for precise epigenetic editing, multiplexed regulation of the transcriptome, programmable 3D genome organization, and spatial control of RNA logistics. By pioneering real-time visualization of chromatin dynamics and RNA in living cells, the lab provides an unprecedented window into the fundamental "control principles of life."
This principle-driven technology lineage has moved into the clinic, with the lab's compact epigenetic editor currently in first-in-human clinical testing for FSHD muscular dystrophy (NCT06907875). This milestone represents a core mission of the lab: translating foundational engineering into next-generation therapeutics that act predictably as dynamic, complex systems.
Beyond single-cell control, the Qi Lab is building a framework for synthetic cell–cell communication, with a particular emphasis on the bidirectional interplay between immune cells and neurons. The lab’s goal is to move beyond describing molecular parts to discovering fundamental control principles in living systems: how regulatory landscapes create stable states and memory, how spatial genome–RNA organization shapes dynamic responses, and how engineered cell–cell interactions can generate emergent multicellular behaviors.
By integrating computational design with experimental biology, Dr. Qi aims to identify the generalizable rules linking molecular programs to systems-level physiology. He is a Chan Zuckerberg Biohub Investigator and an Institute Scholar at the Sarafan ChEM-H, and is dedicated to shaping the technical and ethical frameworks that will define the future of human genome engineering. -
Gary Qian
Ph.D. Student in Management Science and Engineering, admitted Autumn 2021
BioI am currently a 2nd year PhD student in Management Science and Engineering at Stanford working with Professor Margaret Brandeau. My research focuses on the development of applied mathematical, economic models, and machine learning models to support health policy decisions. My recent work has been focused on HIV prevention and treatment programs, programs to control US opioid epidemic, and policies for minimizing spread of infectious diseases (including COVID-19).
I am passionate about using optimization theory and machine learning to implement scalable solutions in solving complex, real-world problems including but not limited to applications in healthcare.
