School of Engineering
Showing 4,601-4,700 of 6,554 Results
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Kate Reidy
Affiliate, Materials Science and Engineering
BioKate Reidy will begin as an Assistant Professor of Materials Science and Engineering at Stanford in September 2026. Her research takes a ‘bottom up' approach to nanoscale design, tailoring material properties by understanding and manipulating their atomic structure. She combines advanced characterization with in situ microscopy to elucidate growth mechanisms, chemical composition, and response to stimuli at the atomic scale.
Her research group aims to push the limits of nanoscale engineering by observing and controlling atomic-scale kinetic and thermodynamic phenomena such as adsorption, diffusion, nucleation, defect and interface formation - mapping such structural dynamics to quantum, energy, and opto-electronic properties. She is broadly interested in the functional utilization of quantum properties of nanomaterials in our classical world.
Prior to joining Stanford, Kate was a Miller Postdoctoral Fellow at UC Berkeley and Lawrence Berkeley National Lab. She completed her PhD in Materials Science & Engineering at MIT as a MIT Energy initiative and William Asbjornsen Albert Memorial Fellow, entitled 'Atomic-Scale Design at the 2D/3D Interface using Electron Microscopy'. She received her B.Sc in Nanoscience, Physics, and Chemistry of Advanced Materials from Trinity College Dublin, Ireland. Her work has been recognized by the MIT School of Engineering, Microscopy Society of America, Materials Research Society Gold Award, 'Best Doctoral Thesis' Award at MIT DMSE, and the Lemelson-Vest Award for Innovation. -
Philipp Reineke
Ph.D. Student in Management Science and Engineering, admitted Spring 2019
Current Research and Scholarly InterestsIn his dissertation research, Phil examines Decentralized Autonomous Organizations (DAOs) and decentralization more generally.
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Martin Reinhard
Professor (Research) of Civil and Environmental Engineering, Emeritus
BioReinhard studies the fate of organic substances in the subsurface environment and develops technologies for the remediation of groundwater contaminated with chlorinated and non-chlorinated hydrocarbon compounds. His research is concerned with mechanistic aspects of chemical and biological transformation reactions in soils, natural waters, and treatment systems.
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Rahul Rejeev
Student Employee, Computer Science
Undergraduate, Vice Provost for Undergraduate EducationBio- An incoming Freshman interested in urbanism, computing, and the outdoors.
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Anka Reuel
Ph.D. Student in Computer Science, admitted Autumn 2022
Current Research and Scholarly InterestsCompared to the technical advancements in AI, the area of technical AI ethics is significantly understudied. Novel, complex autonomous systems are being developed without devoting enough time to their potential negative implications and how they can be mitigated. Given the increasing use of such systems throughout society, this discrepancy sparked Anka's interest in contributing to research in responsible AI, both from a technical and a governance perspective.
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Stephen E Richardson
Software Developer Associate, Electrical Engineering
BioPublications: https://scholar.google.com/citations?user=O3IrDzwAAAAJ
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Ellen Youngsoo Rim
Assistant Professor of Chemical Engineering
BioPlants are increasingly vulnerable to environmental stressors—such as pathogen infection, drought, and heat—from climate change. These challenges threaten global food security and limit the carbon sequestration potential of plants. Our research goal is to sustainably enhance plant productivity and resilience through protein engineering. We engineer proteins involved in plant immune and hormone signaling pathways using directed evolution in high-throughput single cell systems. Directed evolution is a synthetic biology approach that enables rapid development of proteins with novel or improved functions. We combine this approach with machine learning, which allows us to learn from large datasets generated during the directed evolution process. Engineered proteins are then introduced into plants to enhance crop yields and climate resilience.
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Juan Rivas-Davila
Associate Professor of Electrical Engineering and Senior Fellow at the Precourt Institute for Energy
Current Research and Scholarly InterestsModern applications demand power capabilities beyond what is presently achievable. High performance systems need high power density and bandwidth that are difficult to achieve.
Power density can be improved with better semiconductors and passive componets, and by reducing the energy storage requirements of the system. By dramatically increasing switching frequency it is possible to reduce size of power converters. I'm interested in high performance/frequency circuits switching >10 MHz. -
Eric Roberts
The Charles Simonyi Professor in the School of Engineering, Emeritus
BioFrom 1990-2002, Roberts served as associate chair and director of undergraduate studies for the Computer Science Department before being appointed as Senior Associate Dean in the School of Engineering and later moving on to become Faculty Director for Interdisciplinary Science Education in the office of the VPUE.
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Stephen Rock
Professor of Aeronautics and Astronautics, Emeritus
BioProfessor Rock's research interests include the application of advanced control and modeling techniques for robotic and vehicle systems (aerospace and underwater). He directs the Aerospace Robotics Laboratory in which students are involved in experimental programs designed to extend the state-of-the-art in robotic control. Areas of emphasis include planning and navigation techniques (GPS and vision-based) for autonomous vehicles; aerodynamic modeling and control for aggressive flight systems; underwater remotely-operated vehicle control; precision end-point control of manipulators in the presence of flexibility and uncertainty; and cooperative control of multiple manipulators and multiple robots. Professor Rock teaches several courses in dynamics and control.
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Oscar Rodriguez
Graduate, Stanford Center for Professional Development
BioPursuing a Graduate Certificate in Artificial Intelligence. Outside of Stanford, I work on Machine Learning infrastructure for Gemini training setups at Google.
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Carlos Jose Rodriguez Santiago
Ph.D. Student in Chemical Engineering, admitted Autumn 2022
BioCarlos Rodriguez Santiago is a Chemical Engineering PhD candidate working in the lab of Dr. Judith Shizuru to develop protein therapeutics that will facilitate hematopoietic stem cell transplantation without the need for chemotherapy or radiation. His PhD thesis work is at the intersection of immunology, oncology, and protein engineering. Carlos is also a Sarafan CheM-H Lipshultz Graduate Fellow participating in the Chemistry/Biology Interface (CBI) Predoctoral training program which aims to cultivate interactions and thinking across disciplinary lines to enable innovations that improve human health.
Prior to his PhD work, Carlos helped found the Protein Engineering Knowledge Center (PEKC) at Stanfords Innovative Medicines Accelerator (IMA). There he collaborated with researchers to discover and engineer antibodies against therapeutically relevant targets. Several antibodies discovered by Carlos have officially been licensed out for further therapeutic development. -
Justin S. Rogers
Research Oceanographer, Civil and Environmental Engineering
Staff, Program-Fringer O.BioPh.D. Civil & Environmental Engineering, Stanford University, 2016
M.S. Civil & Environmental Engineering, University of Wisconsin-Madison, 2006
B.S. Civil Engineering (Minor in Chemistry), University of Arizona, 2004
Research interests:
-Coastal resilience, risk, sea level rise, extreme events, compound hazards
-Impact of climate change on human and natural systems in coastal and nearshore environments
-Core model development for coastal applications, storm surge, tropical cyclones, flood risk, bottom boundary layers, turbulence, and multiscale physics.
I leverage the power of cloud computing, HPC systems and modern code frameworks, and adapt multiple analysis methods including dynamical models, machine learning, statistical methods, and field observations.
