School of Engineering
Showing 1-50 of 288 Results
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Simone D'Amico
Associate Professor of Aeronautics and Astronautics and, by courtesy, of Geophysics
BioSimone D’Amico is Associate Professor of Aeronautics and Astronautics (AA), W.M. Keck Faculty Scholar in the School of Engineering, and Professor of Geophysics (by Courtesy). He is the Founding Director of the Space Rendezvous Laboratory and Director of the AA Undergraduate Program. He received the B.S. and M.S. degrees from Politecnico di Milano (2003) and the Ph.D. degree from Delft University of Technology (2010). Before Stanford, Dr. D’Amico was research scientist and team leader at the German Aerospace Center (DLR) for 11 years. There he gave key contributions to formation-flying and proximity operations missions such as GRACE (NASA/DLR), PRISMA (OHB/DLR/CNES/DTU), TanDEM-X (DLR), BIROS (DLR) and PROBA-3 (ESA). His research aims at enabling future miniature distributed space systems for unprecedented remote sensing, space and planetary science, exploration and spaceflight sustainability. To this end he performs fundamental and applied research at the intersection of advanced astrodynamics, spacecraft Guidance, Navigation and Control (GNC), autonomy, decision making and space system engineering. Dr. D’Amico is institutional PI of three upcoming autonomous satellite swarm missions funded by NASA and NSF, namely STARLING, VISORS, and SWARM-EX. He is Fellow of AAS, Associate Fellow of AIAA, Associate Editor of AIAA JGCD, Advisor of NASA and several space startups. He was the recipient of several awards, including Best Paper Awards at IAF (2022), IEEE (2021), AIAA (2021), AAS (2019) conferences, the Leonardo 500 Award by the Leonardo da Vinci Society/ISSNAF (2019), FAI/NAA’s Group Diploma of Honor (2018), DLR’s Sabbatical/Forschungssemester (2012) and Wissenschaft Preis (2006), and NASA’s Group Achievement Award for the GRACE mission (2004).
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Marta D'Elia
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioI’m a research/computational scientist working on the design and analysis of models and data-driven algorithms for the simulation of complex, multiscale and multiphysics problems. My background and training have foundations in Numerical Analysis, Scientific Computing, Inverse Problems, Control and Optimization, and Uncertainty Quantification. In the past five years I have focused on Scientific Machine Learning (SciML) and Deep Learning. I am an expert in Nonlocal/Fractional Modeling and Simulation (10 years) with application to Continuum Mechanics, Subsurface Transport, Image Processing, and Turbulence. I have a master's degree in Mathematical Engineering from Politecnico di Milano (2007) and a PhD in Applied Mathematics from Emory University (2011).
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Onat Dalmaz
Ph.D. Student in Electrical Engineering, admitted Autumn 2023
Current Research and Scholarly InterestsMy current research centers on developing mathematical tools to enhance the explainability of image reconstruction algorithms in computational magnetic resonance imaging (MRI). By integrating principles from machine learning, signal processing, and generative models, I aim to improve the transparency and reliability of AI applications in medical imaging.
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Bruce Daniel
Professor of Radiology (Body Imaging) and, by courtesy, of Bioengineering
Current Research and Scholarly Interests1. MRI of Breast Cancer, particularly new techniques. Currently being explored are techniques including ultra high spatial resolution MRI and contrast-agent-free detection of breast tumors.
2. MRI-guided interventions, especially MRI-compatible remote manipulation and haptics
3. Medical Mixed Reality. Currently being explored are methods of fusing patients and their images to potentially improve breast conserving surgery, and other conditions. -
Eric Darve
Director, Institute for Computational and Mathematical Engineering (ICME) and Professor of Mechanical Engineering
Current Research and Scholarly InterestsThe research interests of Professor Darve span across several domains, including machine learning for science and engineering, large-language models, transformer models, surrogate and reduced order modeling, stochastic inversing, anomaly detection, numerical linear algebra, high-performance, parallel, and GPU computing.
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Reinhold Dauskardt
Ruth G. and William K. Bowes Professor in the School of Engineering
BioDauskardt and his group have worked extensively on integrating new materials into emerging technologies including thin-film structures for nanoscience and energy technologies, high-performance composite and laminates for aerospace, and on biomaterials and soft tissues in bioengineering. His group has pioneered methods for characterizing adhesion and cohesion of thin films used extensively in device technologies. His research on wound healing has concentrated on establishing a biomechanics framework to quantify the mechanical stresses and biologic responses in healing wounds and define how the mechanical environment affects scar formation. Experimental studies are complimented with a range of multiscale computational capabilities. His research includes interaction with researchers nationally and internationally in academia, industry, and clinical practice.
