School of Engineering
Showing 461-470 of 710 Results
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William Nix
Lee Otterson Professor in the School of Engineering, Emeritus
BioI have been engaged in the study of mechanical properties of materials for nearly 50 years. My early work was on high temperature creep and fracture of metals, focusing on techniques for measuring internal back stresses in deforming metals and featuring the modeling of diffusional deformation and cavity growth processes. My students and I also studied high temperature dispersion strengthening mechanisms and described the effects of threshold stresses on these creep processes. Since the mid-1980's we have focused most of our attention on the mechanical properties of thin film materials used in microprocessors and related devices. We have developed many of the techniques that are now used to study of thin film mechanical properties, including nanoindentation, substrate curvature methods, bulge testing methods and the mechanical testing of micromachined (MEMS) structures. We are also known for our work on the mechanisms of strain relaxation in heteroepitaxial thin films and plastic deformation of thin metal films on substrates. In addition we have engaged in research on the growth, characterization and modeling of thin film microstructures, especially as they relate to the development of intrinsic stresses. Some of our recent work dealt with the mechanical properties of nanostructures and with strain gradients and size effects on the mechanical properties of crystalline materials. Our most recent work deals with the mechanical properties of lithiated nanostructures that are being considered for lithium-ion battery applications.
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Hae Young Noh
Associate Professor of Civil and Environmental Engineering
BioHae Young Noh is an associate professor in the Department of Civil and Environmental Engineering. Her research introduced the new concept of “structures as sensors” to enable physical structures (e.g., buildings and vehicle frames) to be user- and environment-aware. In particular, these structures indirectly sense humans and surrounding environments through their structural responses (i.e., vibrations) by inferring the desired information (e.g., human behaviors, environmental conditions, heating and cooling system performance), instead of directly measuring the sensing targets with additional dedicated sensors (e.g., cameras, motion sensors). This concept brought a paradigm shift in how we view these structures and how the structures interact with us.
Traditionally, structures that we inhabit (such as buildings or vehicles) are considered as passive and unchanging objects that we need to monitor and control, utilizing a dense set of sensors to collect information. This has often been complicated by “noise” caused by the occupants and environments. For example, building vibrations induced by indoor and outdoor environmental and operational conditions (e.g., people walking around, traffic outside, heating system running, etc.), have been often seen as noise that needs to be removed in traditional building science and structural engineering; however, they are a rich source of information about structure, users, environment, and resources. Similarly, in vehicle engineering, researchers and engineers have been investigating control and dynamics to reduce vehicle vibration for safety and comfort. However, vibrations measured inside vehicles contain information about transportation infrastructure, vehicle itself, and driver.
Noh's work utilizes this “noise” to empower the structures with the ability to perceive and understand the information about users and surroundings using their own responses, and actively adopt and/or interact to enhance their sustainability and the occupants’ quality of life. Since she utilizes the structure itself as a sensing medium, information collection involves a simpler set of hardware that can be easily maintained throughout the structural lifetime. However, the analysis of data to separate the desired information becomes more challenging. This challenge is addressed through high-rate dynamic sensing and multi-source inferencing. Ultimately, her work aims to allow structural systems to become general sensing platforms that are easier and more practical to deploy and maintain in a long-term.
At Stanford University, Noh received her PhD and MS degrees in the CEE department and her second MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at Cornell University. -
Paul Nuyujukian
Assistant Professor of Bioengineering and of Neurosurgery and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsOur group explores neuroengineering and its application to both basic and clinical neuroscience. Our goal is to develop brain-machine interfaces as a platform technology for a variety of brain-related medical conditions including stroke and epilepsy.
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Daniel J O'Shea
Research Engineer
Current Research and Scholarly InterestsI study the neural mechanisms that control movement, and more broadly, how neural populations spanning interconnected brain regions perform the distributed computations that drive skilled behavior. I develop experimental and computational tools to understand the neural population dynamics that establish speed and dexterity.
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Allison Okamura
Richard W. Weiland Professor in the School of Engineering and Professor of Mechanical Engineering
Current Research and Scholarly InterestsMy research focuses on developing the principles and tools needed to realize advanced robotic and human-machine systems capable of physical interaction. Application areas include surgery, simulation and training, rehabilitation, prosthetics, neuromechanics, exploration of hazardous and remote environments (e.g. space), design, and education.
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Kunle Olukotun
Cadence Design Systems Professor, Professor of Electrical Engineering and of Computer Science
On Partial Leave from 01/01/2025 To 03/31/2025BioKunle Olukotun is the Cadence Design Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is a pioneer in multicore processor design and the leader of the Stanford Hydra chip multiprocessor (CMP) research project. He founded Afara Websystems to develop high-throughput, low-power multicore processors for server systems. The Afara multi-core processor, called Niagara, was acquired by Sun Microsystems and now powers Oracle's SPARC-based servers. In 2017, Olukotun co-founded SambaNova Systems, a Machine Learning and Artificial Intelligence company, and continues to lead as their Chief Technologist.
Olukotun is the Director of the Pervasive Parallel Lab and a member of the Data Analytics tor What's Next (DAWN) Lab, developing infrastructure for usable machine learning. He is a member of the National Academy of Engineering, an ACM Fellow, and an IEEE Fellow for contributions to multiprocessors on a chip design and the commercialization of this technology. He also received the Harry H. Goode Memorial Award.
Olukotun received his Ph.D. in Computer Engineering from The University of Michigan. -
Simona Onori
Associate Professor of Energy Science Engineering, Senior Fellow at the Precourt Institute for Energy and Associate Professor, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsModeling, control and optimization of dynamic systems;
Model-based control in advanced propulsion systems;
Energy management control and optimization in HEVs and PHEVs;
Energy storage systems- Li-ion and PbA batteries, Supercapacitors;
Battery aging modeling, state of health estimation and life prediction for control;
Damage degradation modeling in interconnected systems
