School of Engineering
Showing 4,481-4,500 of 6,554 Results
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Friedrich Prinz
Leonardo Professor, Professor of Mechanical Engineering, of Materials Science and Engineering and Senior Fellow at the Precourt Institute for Energy
BioFritz Prinz is the Leonardo Professor in the School of Engineering at Stanford University, Professor of Materials Science and Engineering, Professor of Mechanical Engineering, and Senior Fellow at the Precourt Institute for Energy. He also serves as the Director of the Nanoscale Prototyping Laboratory and Faculty Co-director of the NPL-Affiliate Program. A solid-state physicist by training, Prinz leads a group of doctoral students, postdoctoral scholars, and visiting scholars who are addressing fundamental issues on energy conversion and storage at the nanoscale. In his Laboratory, a wide range of nano-fabrication technologies are employed to build prototype fuel cells and capacitors with induced topological electronic states. We are testing these concepts and novel material structures through atomic layer deposition, scanning tunneling microscopy, impedance spectroscopy and other technologies. In addition, the Prinz group group uses atomic scale modeling to gain insights into the nature of charge separation and recombination processes. Before coming to Stanford in 1994, he was on the faculty at Carnegie Mellon University. Prinz earned a PhD in Physics at the University of Vienna.
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Adrienne Propp
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2021
BioI am a fourth year PhD student in ICME (the Institute for Computational and Mathematical Engineering). Prior to Stanford, I was working as a technical analyst at the RAND Corporation where I spent most of my time designing microsimulations and other models to investigate topics in healthcare, education, disaster relief, and international relations.
My research interests lie at the intersection of mathematics, data, and modeling, which has led me to a focus on scientific machine learning (SciML). Specifically, I am working on developing new graph-based surrogate modeling methods for low-data regimes. I am grateful to be advised by Daniel Tartakovsky, During my PhD, I have also collaborated with Jenny Suckale to model volcanic lava fountaining, and Susan Athey and Sanath Kumar Krishnamurthy to design improved algorithms for contextual bandits.
Past research projects have ranged from computational models of the heart to inverse modeling to predict satellite performance. -
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. -
Lei (Stanley) Qi
Associate Professor of Bioengineering
BioDr. Lei (Stanley) Qi (publishes as Lei S. Qi) is an Associate Professor in the Department of Bioengineering at Stanford University, an Institute Scholar at Sarafan ChEM-H, and a Chan Zuckerberg Biohub Investigator. Trained in physics and mathematics (Tsinghua University) and bioengineering (UC Berkeley), he was a Systems Biology Fellow at UCSF before joining the Stanford faculty in 2014.
Qi is a pioneer in CRISPR technology and genome engineering. His lab created the first nuclease-deactivated Cas9 (dCas9) for targeted gene regulation, establishing CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Since then, his group has expanded CRISPR from an editing tool into a platform for programmable control of dynamic and spatial cell state, integrating scalable perturbation, live-cell and super-resolution imaging, and computation-guided design. This work has produced technologies for multiplexed transcriptome regulation, programmable 3D genome organization, spatial RNA logistics control, and real-time visualization of chromatin and transcriptional events in living cells.
A distinctive focus of the Qi lab is closed-loop biology, combining perturbation with high-content measurements to infer mechanisms and iteratively refine control strategies. The lab develops platforms spanning multiplexed transcriptional and epigenetic control, spatial genome–transcriptome organization, and quantitative live-cell imaging of chromatin and transcriptional dynamics. A compact nuclease-dead CRISPR epigenetic editor from this technology lineage has advanced to first-in-human clinical testing for facioscapulohumeral muscular dystrophy (FSHD; NCT06907875), underscoring the translational potential of principle-driven control systems.
Beyond single-cell control, Qi’s lab is building a framework for synthetic cell–cell communication, with 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 experimental bioengineering with computation and machine learning, the lab aims to identify generalizable rules linking molecular programs to systems-level physiology and disease trajectories and to translate those rules into next-generation therapeutic cells.
