School of Engineering
Showing 4,061-4,080 of 5,904 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.
We are a neuroengineering lab focused on brain dynamics, brain health, and the neural mechanisms of anesthesia. Our research aims to understand the brain dynamics of aging, Alzheimer’s disease, child development, sleep, anesthesia, and consciousness. We use this knowledge to develop novel technologies for brain monitoring and physiological control. We also teach anesthesiologists how to use EEG to provide personalized anesthesia care. -
Stanley Qi
Associate Professor of Bioengineering and, by courtesy, of Biomedical Data Science
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.
