School of Engineering
Showing 321-330 of 480 Results
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Eduardo Miranda
Professor of Civil and Environmental Engineering
Current Research and Scholarly InterestsRegional seismic risk assessment, ground motion directionality
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Azalia Mirhoseini
Assistant Professor of Computer Science
BioAzalia Mirhoseini is an Assistant Professor in the Computer Science Department at Stanford University. Professor Mirhoseini's research interest is in developing capable, reliable, and efficient AI systems for solving high-impact, real-world problems. Her work includes generalized learning-based methods for decision-making problems in systems and chip design, self-improving AI models through interactions with the world, and scalable deep learning optimization. Prior to Stanford, she spent several years in industry AI labs, including Anthropic and Google Brain. At Anthropic, she worked on advancing the capabilities and reliability of large language models. At Google Brain, she co-founded the ML for Systems team, with a focus on automating and optimizing computer systems and chip design. She received her BSc degree in Electrical Engineering from Sharif University of Technology and her PhD in Electrical and Computer Engineering from Rice University. Her work has been recognized through the MIT Technology Review’s 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, The Times, ZDNet, VentureBeat, and WIRED.
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Shahab Mirjalili
Physical Science Research Scientist, Mechanical Engineering
Current Research and Scholarly InterestsBroadly, my research lies in the intersection of fluid mechanics, scientific computing, and machine learning. My work aims to develop and use computational methods to provide a predictive understanding of complex flow problems, including those involving multi-physics couplings and multiphase dynamics across a wide range of scales and Reynolds numbers. In this vein, I develop physically consistent models, robust numerical schemes, and high-performance computing (HPC) software that enable high-fidelity simulations of flows involving complex multi-physics effects. These developments build upon my novel work on modeling multiphase flows and my high-performance multiphase, multi-physics software. In addition to simulations, I use asymptotic analyses and machine learning (ML) to construct reduced-order models (ROMs) that can be used for engineering analysis, control, design, and especially optimization. I am interested in a wide range of applications involving impactful problems. In particular, I am passionate about improving the predictive understanding of multiphase flows in:
- Propulsion and energy conversion/storage
- Additive manufacturing processes
- Biophysical systems
- Environmental flows -
Mohammad Javad Mirshojaeian Hosseini
Postdoctoral Scholar, Chemical Engineering
BioWith over five years of experience, my work focuses on designing, fabricating, and characterizing flexible nanostructures and organic neuromorphic circuits. My expertise extends to hands-on experience in ISO 4 cleanrooms and fabrication labs, employing a variety of techniques such as electron beam and thermal PVD, ALD, sputtering, photolithography, CVD, profilometry, and wet chemical processing. I have a strong foundation in advanced materials and technologies, including neuromorphic systems, nanofabrication, biosensors, lab-on-a-chip technologies, printing electronics, and organic nanoelectronics.
Currently, I am a postdoctoral researcher at Stanford University, where I explore stretchable neuromorphic e-skin and flexible electronics, particularly for biopotential monitoring and soft robotics applications. My multidisciplinary expertise enables me to contribute to projects that combine neuromorphic computing, smart materials, and neuroscience. These align with my long-term research goals of advancing neuromorphic systems and developing novel technologies at the interface of artificial intelligence, smart materials, and organic electronics.
