Stanford University
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Zihao Ou
Physical Science Research Scientist
BioMy research interests have been focusing on how individual building blocks come together resulting in complex functions which are hard to predict, if possible, from the individual identities. Similar to a digital screen displaying a movie, the complicated pattern and story can hardly be interpreted from the dynamic traces of a single pixel. Specifically, I have been studying the general topic of self-assembly and non-equilibrium behaviors in soft matter systems, using both experimental and simulation tools.
I obtained my B.S. degree in physics from University of Science and Technology of China (USTC) in 2015. In my undergraduate research, I tried to use computer simulation to study multiple systems in Prof. Zhonghuai Hou’s group, such as the Viscek model for self-propelled particles. In 2014, I visited Oxford University to study the phase behaviors of active nematics using Lattice-Boltzmann method in Prof. Julia M. Yeomans' group. In 2020, I obtained my Ph.D. degree in Materials Science and Engineering at University of Illinois at Urbana-Champaign (UIUC) under the supervision of Prof. Qian Chen. During my Ph.D. research, we illustrated the nonclassical crystallization pathway of nanoparticles (Nat. Mater., 19, 450–455, 2020) and supracrystal growth kinetics (Nat. Commun., 11, 4555, 2020) using liquid-phase TEM. I also studied other nonequilibrium behaviors in novel colloidal systems, such as shape transformation of metal-organic framework crystals during chemical etching (ACS Appl. Mater. Interfaces, 10, 48, 40990–40995, 2018), application of ferromagnetic colloids in inductor design (Science Adv., 6, 3, eaay4508, 2020) and electron transport in redox-active colloids.
In August 2020, I joined Prof. Guosong Hong’s group at the materials science and engineering department at Stanford University to develop novel nanomaterials that can interact with neurons at the subcellular level. Armed with the knowledge of nanotechnology and theoretical modeling, we are extending the tools that can be used to investigate the challenging questions in neuroscience. -
Lisa Ouellette
Deane F. Johnson Professor of Law and Senior Fellow at the Stanford Institute for Economic Policy Research
BioLisa Larrimore Ouellette is the Deane F. Johnson Professor of Law at Stanford Law School, as well as a Senior Fellow at the Stanford Institute for Economic Policy Research. Her scholarship addresses empirical and theoretical problems in intellectual property and innovation law. She takes advantage of her training in physics to explore policy issues such as how scientists use the technical information in patents, how scientific expertise might improve patent examination, the patenting of publicly funded research under the Bayh–Dole Act, and the integration of IP with other levers of innovation policy. She has applied these ideas to biomedical innovation challenges including the opioid epidemic, the COVID-19 pandemic, and pharmaceutical prices. She has also written about multiple legal issues in trademark law, about the evidentiary value of online surveys, and about the potential for different standards of review to create what she terms “deference mistakes” in numerous areas of law.
Professor Ouellette is also an acclaimed teacher and nationally recognized intellectual property law expert. She has coauthored a free patent law casebook, Patent Law: Cases, Problems, and Materials. She has written over 350 posts for her blog, Written Description, and her commentary has appeared in publications including the New York Times, Wall Street Journal, TIME Magazine, and Slate. She has been selected to design and lead pedagogy training for other Stanford Law faculty. In 2018, she received the law school’s John Bingham Hurlbut Award for Excellence in Teaching.
Prior to her appointment at Stanford Law School in 2014, Professor Ouellette was a Postdoctoral Fellow at the Information Society Project at Yale Law School. She also clerked for Judge Timothy B. Dyk of the U.S. Court of Appeals for the Federal Circuit and Judge John M. Walker, Jr. of the U.S. Court of Appeals for the Second Circuit. She holds a J.D. from Yale Law School, where she was an Articles Editor of the Yale Law Journal and a Coker Fellow in Contract Law. She earned a Ph.D. in physics from Cornell University as well as a B.A. in physics from Swarthmore College, and she has conducted scientific research at the Max Planck Institute, CERN, and NIST. -
Nicholas Ouellette
Professor of Civil and Environmental Engineering
Current Research and Scholarly InterestsThe Environmental Complexity Lab studies self-organization in a variety of complex systems, ranging from turbulent fluid flows to granular materials to collective motion in animal groups. In all cases, we aim to characterize the macroscopic behavior, understand its origin in the microscopic dynamics, and ultimately harness it for engineering applications. Most of our projects are experimental, though we also use numerical simulation and mathematical modeling when appropriate. We specialize in high-speed, detailed imaging and statistical analysis.
Our current research includes studies of turbulence in two and three dimensions, with a focus on coherent structures and the geometry of turbulence; the transport of inertial, anisotropic, and active particles in turbulence; the erosion of granular beds by fluid flows and subsequent sediment transport; quantitative measurements of collective behavior in insect swarms and bird flocks; the stability of ocean ecosystems; neural signal processing; and uncovering the natural, self-organized spatiotemporal scales in urban systems. -
John Ousterhout
Leonard Bosack and Sandy K. Lerner Professor of Engineering, Professor of Computer Science and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsOusterhout's research ranges across a variety of topics in system software, software development tools, and user interfaces. His current research is in the area of granular computing: new software stack layers that allow the execution of large numbers of very small tasks (as short as a few microseconds) in a datacenter. Current projects are developing new techniques for thread management, network communication, and logging.
