School of Engineering
Showing 1,301-1,400 of 6,554 Results
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John DeSilva
Systems & Network Manager, Electrical Engineering
Current Role at StanfordSystems & Network Manager, David Packard Electrical Engineering Building
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Joseph M. DeSimone
Sanjiv Sam Gambhir Professor of Translational Medicine, Professor of Chemical Engineering and, by courtesy, of Chemistry, of Materials Science and Engineering, and of Operations, Information and Technology at the Graduate School of Business
BioJoseph M. DeSimone is the Sanjiv Sam Gambhir Professor of Translational Medicine and Chemical Engineering at Stanford University. He holds appointments in the Departments of Radiology and Chemical Engineering with courtesy appointments in the Department of Chemistry and in Stanford’s Graduate School of Business.
The DeSimone laboratory's research efforts are focused on developing innovative, interdisciplinary solutions to complex problems centered around advanced polymer 3D fabrication methods. In Chemical Engineering and Materials Science, the lab is pursuing new capabilities in digital 3D printing, as well as the synthesis of new polymers for use in advanced additive technologies. In Translational Medicine, research is focused on exploiting 3D digital fabrication tools to engineer new vaccine platforms, enhanced drug delivery approaches, and improved medical devices for numerous conditions, with a current major focus in pediatrics. Complementing these research areas, the DeSimone group has a third focus in Entrepreneurship, Digital Transformation, and Manufacturing.
Before joining Stanford in 2020, DeSimone was a professor of chemistry at the University of North Carolina at Chapel Hill and of chemical engineering at North Carolina State University. He is also Co-founder, Board Chair, and former CEO (2014 - 2019) of the additive manufacturing company, Carbon. DeSimone is responsible for numerous breakthroughs in his career in areas including green chemistry, medical devices, nanomedicine, and 3D printing. He has published over 350 scientific articles and is a named inventor on over 200 issued patents. Additionally, he has mentored 80 students through Ph.D. completion in his career, half of whom are women and members of underrepresented groups in STEM.
In 2016 DeSimone was recognized by President Barack Obama with the National Medal of Technology and Innovation, the highest U.S. honor for achievement and leadership in advancing technological progress. He has received numerous other major awards in his career, including the U.S. Presidential Green Chemistry Challenge Award (1997); the American Chemical Society Award for Creative Invention (2005); the Lemelson-MIT Prize (2008); the NIH Director’s Pioneer Award (2009); the AAAS Mentor Award (2010); the Heinz Award for Technology, the Economy and Employment (2017); the Wilhelm Exner Medal (2019); the EY Entrepreneur of the Year Award (2019 U.S. Overall National Winner); and the Harvey Prize in Science and Technology (2020). He is one of only 25 individuals elected to all three branches of the U.S. National Academies (Sciences, Medicine, Engineering). DeSimone received his B.S. in Chemistry in 1986 from Ursinus College and his Ph.D. in Chemistry in 1990 from Virginia Tech. -
Abhijit Devalapura
Masters Student in Computer Science, admitted Autumn 2021
BioSIEPR Undergraduate Research Fellow 2022-2023
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Thomas Devereaux
Professor of Photon Science, of Materials Science and Engineering and Senior Fellow at the Precourt Institute for Energy
Current Research and Scholarly InterestsMy main research interests lie in the areas of theoretical condensed matter physics and computational physics. My research effort focuses on using the tools of computational physics to understand quantum materials. Fortunately, we are poised in an excellent position as the speed and cost of computers have allowed us to tackle heretofore unaddressed problems involving interacting systems. The goal of my research is to understand electron dynamics via a combination of analytical theory and numerical simulations to provide insight into materials of relevance to energy science. My group carries out numerical simulations on SIMES’ high-performance supercomputer and US and Canadian computational facilities. The specific focus of my group is the development of numerical methods and theories of photon-based spectroscopies of strongly correlated materials.
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Luigi Di Lillo
Affiliate, Program-Pavone, M.
BioAt Stanford, my work bridges autonomous systems, safety, and insurance through probabilistic risk modeling under uncertainty and in sparse-data regimes.
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Ludwing Diaz
Course Developer, SCPD Open Enrollment Programs
Course Developer, Stanford Engineering Center for Global and Online EducationBioCISSP, Information Security SME with more than 25+ years of experience in Infrastructure Security for large scale networks.
BS Electronic Engineering - Universidad Pontificia Bolivariana
MS Telecommunications and Networking Systems- Florida International University
Advance Computer Security Professional Certification - Stanford SCPD
Cybersecurity Graduate Program at Stanford, NDO. -
Gerwin Dijk
Postdoctoral Scholar, Materials Science and Engineering
BioBioelectronics, neurostimulation, biosensors, conducting polymers, microfabrication.
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David Dill
Donald E. Knuth Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsSecure and reliable blockchain technology at Facebook.
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Katryna Dillard
Senior Program Manager, Program-Bao Z.
BioKatryna Dillard joined Stanford University in 2021 as the program manager for the Stanford Wearable Electronics (eWEAR) Initiative. As the program manager Katryna manages the logistics of annual symposiums, monthly seminars/newsletters, tracking and updating current affiliate member companies, and acts as a point of contact with affiliate members while providing administrative support. Prior to joining eWEAR Katryna worked in hotels at the front desk and events for 5 years. She graduated from Whittier College with a B.A. in Sociology and Theatre Communication Arts with an emphasis in Design and Technology.
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Jennifer Dionne
Professor of Materials Science and Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Radiology (Molecular Imaging Program at Stanford)
BioJennifer (Jen) Dionne is a Professor of Materials Science and Engineering and, by courtesy, of Radiology at Stanford. She is also a Chan Zuckerberg Biohub Investigator, deputy director of Q-NEXT (a DOE National Quantum Initiative), and co-founder of Pumpkinseed, a company developing quantum sensors to understand and optimize the immune system. From 2020-2023, Jen served as Stanford’s Inaugural Vice Provost of Shared Facilities, raising capital to modernize instrumentation, fund experiential education, foster staff development, and support new and existing users of the shared facilities. Jen received her B.S. degrees in Physics and Systems Science and Mathematics from Washington University in St. Louis, her Ph. D. in Applied Physics at the California Institute of Technology in 2009, and her postdoctoral training in Chemistry at Berkeley. As a pioneer of nanophotonics, she is passionate about developing methods to observe and control chemical and biological processes as they unfold with nanometer scale resolution, emphasizing critical challenges in global health and sustainability. Her research has developed culture-free methods to detect pathogens and their antibiotic susceptibility; amplification-free methods to detect and sequence nucleic acids and proteins; and new methods to image light-driven chemical reactions with atomic-scale resolution. Jen’s work has been featured in NPR, the Economist, Science, and Nature, and recognized with the NSF Alan T. Waterman Award, a NIH Director’s New Innovator Award, a Moore Inventor Fellowship, and the Presidential Early Career Award for Scientists and Engineers. She was also featured on Oprah’s list of “50 Things that will make you say ‘Wow’!”. She also perceives outreach as a critical component of her role and frequently collaborates with visual and performing artists to convey the beauty of science to the broader public.
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Varun Dolia
Ph.D. Student in Materials Science and Engineering, admitted Autumn 2021
BioVarun Dolia is a Benchmark Fellow and a Ph.D. candidate in Prof. Jen Dionne's lab. He is excited about developing nanophotonic platforms for health and environmental monitoring.
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Stefan P. Domino
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioDr. Domino’s research interest rests within low-Mach fluid mechanics methods development for complex systems that drive the coupling of mass, momentum, species and energy transport. His core research resides within the intersection of physics elucidation, numerical methods research, V&V techniques exploration, and high performance computing and coding methods for turbulent flow applications. Stefan also supports the teaching of ME469, Computational Methods in Fluid Mechanics, is a former Distinguished Member of the Technical Staff at Sandia National Laboratories, and is the CEO of the 501(c)(3) Computational Marine Ethology Research Institute, https://www.comeri.org
Education: University of Utah
Ph.D. Department of Chemical Engineering, 2000
"Methods towards improved simulations for the oxides of nitrogen in pulverized-coal furnaces"
Professor Philip J. Smith, Advisor
Select Recent Publications:
* Domino, S. P., Scott, S., Hubbard, J., "Structural uncertainty assessment for fire-engulfed objects in crosswind: Establishing credibility for a multiphysics wall-modeled large-eddy simulation paradigm", Phys. Rev. Fluids, 2025. (PR Journal Club, May 1, 2025)
* Domino, S. P., "On the subject of large-scale pool fires and turbulent boundary layer interactions", Phys. Fluids, 2024. (Featured)
* Domino, S. P., Wenzel, E. A, "A direct numerical simulation study for confined non-isothermal jet impingement at moderate nozzle-to-plate distances: capturing jet-to-ambient density effects", Int. J. Heat Mass Trans, 2023.
* Benjamin, M., Domino, S. P., Iaccarino, G., "Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges", Eur. Phys. J. E., 2023.
* Hubbard, J., Cheng, M., Domino, S. P., "Mixing in low-Reynolds number reacting impinging jets in crossflow", J. Fluids Engr., 2023.
* Domino, S. P. “Unstructured finite volume approaches for turbulence,” in Numerical Methods in Turbulence Simulation, edited by R. Moser (Elsevier, 2023), Ch. 7, pp. 285–317.
* Scott, S., Domino, S. P., "A computational examination of large-scale pool fires: variations in crosswind velocity and pool shape", Flow, 2022.
* Domino, S. P., Horne, W., "Development and deployment of a credible unstructured, six-DOF, implicit low-Mach overset simulation tool for wave energy applications", Renew. Energy, 2022.
* Hubbard, J., Hansen, M., Kirsch, J., Hewson, J., Domino, S. P., “Medium scale methanol pool fire model validation”, J. Heat Transfer, 2022.
* Barone, M., Ray, J., Domino, S. P., "Feature selection, clustering, and prototype placement for turbulence datasets", AIAA J., 2021,
* Domino, S. P., Hewson, J., Knaus, R., Hansen, M., "Predicting large-scale pool fire dynamics using an unsteady flamelet- and large-eddy simulation-based model suite", Phys. Fluids, 2021. (Editor's pick)
* Domino, S. P., "A case study on pathogen transport, deposition, evaporation and transmission: linking high-fidelity computational fluid dynamics simulations to probability of infection", Int. J. CFD, 2021.
* Domino, S. P., Pierce, F., Hubbard, J., "A multi-physics computational investigation of droplet pathogen transport emanating from synthetic coughs and breathing", Atom. Sprays, 2021.
* Jofre, L., Domino, S. P., Iaacarino, G., "Eigensensitivity analysis of subgrid-scale stresses in large-eddy simulation of a turbulent axisymmetric jet", Int. J. Heat Fluid Flow, 2019.
* Domino, S. P., Sakievich, P., Barone, M., "An assessment of atypical mesh topologies for low-Mach large-eddy simulation", Comp. Fluids, 2019.
* Domino, S. P., "Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach ", J. Comput. Physics, 2018.
* Jofre, L., Domino, S. P., Iaacarino, G., "A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures", Flow Turb. Combust., 2018.
CV: https://github.com/spdomino/cv/blob/main/dominoCV.pdf -
Changxin Lyla Dong
Ph.D. Student in Materials Science and Engineering, admitted Autumn 2022
BioLyla Dong is committed to advancing innovative materials solutions that address critical challenges in health and environmental sustainability. Her research spans multiple fields, including hydrogel development, materials characterization, and electrochemistry. As a PhD candidate at Stanford University advised by Professor Eric A. Appel, she focuses on creating cutting-edge materials to protect against wildfires and improve therapeutic delivery systems.
Prior to her studies at Stanford, Lyla conducted research under the mentorship of Professors Pulickel M. Ajayan and Haotian Wang at Rice University. She developed functional materials for batteries and explored technologies for carbon capture, discovering her passion for sustainable materials science.
Through her interdisciplinary approach, Lyla strives to bridge the critical intersections between health and environmental sustainability, creating solutions that have a real-world impact. -
David Donoho
Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences
BioDavid Donoho is a mathematician who has made fundamental contributions to theoretical and computational statistics, as well as to signal processing and harmonic analysis. His algorithms have contributed significantly to our understanding of the maximum entropy principle, of the structure of robust procedures, and of sparse data description.
Research Statement:
My theoretical research interests have focused on the mathematics of statistical inference and on theoretical questions arising in applying harmonic analysis to various applied problems. My applied research interests have ranged from data visualization to various problems in scientific signal processing, image processing, and inverse problems. -
Jonathan Dotan
Program Coordinator, Electrical Engineering
Staff, Program-Weissman T.BioJonathan Dotan is the founding director of The Starling Lab at Stanford University and USC, where he leads applied research on the decentralized web and human rights. For over 20 years, he’s navigated the intersections of media, tech, and policy as a tech founder.
Jonathan is a fellow at Stanford’s Center for Blockchain Research and Compression Forum, where he is researching strategy and policy for distributed ledger technologies. His scholarship examines Internet governance frameworks, the transition to Web 3.0 and the prospects for a more decentralized internet.
He lectures at Stanford’s School of Engineering and Graduate School of Business. Jonathan’s teaching asks students to consider the never-simple relationship between innovation and progress — recognizing how each new technology brings choices and responsibilities. -
Angelo Dragone
Associate Professor of Photon Science and, by courtesy, of Electrical Engineering
BioAngelo Dragone is an Associate Professor of Photon Science and Electrical Engineering (by courtesy). He has over 20 years of experience in the research and development of Instrumentation for Scientific experiments. He received his Ph.D. in Microelectronics from the Polytechnic University of Bari, Italy, for his research on mixed-signal readout architecture for radiation detectors, conducted at Brookhaven National Laboratory. He worked in the Instrumentation Division at Brookhaven National Laboratory from 2004, before joining SLAC National Accelerator Laboratory in 2008. Over the past 15 years, he has been designing radiation detectors, with a focus on innovative architectural solutions for state-of-the-art scientific instruments and sensor interfaces. These solutions have applications in photon science, particle physics, medical imaging, and national security. At SLAC, he focused his research on designing high frame rate, large dynamic range X-ray detectors for the Linac Coherent Light Source SLAC X-ray Free-electron Laser facility. Since 2012, he has held a management position as head of the Integrated Circuits Department within the Instrumentation Division of the Technology Innovation Directorate (TID) at SLAC. During the past three years, Dr. Dragone has been working on the strategic R&D planning for the SLAC X-ray detectors Initiative and leads, as Program Director, TID Detector R&D, and the applied Microelectronics program. Recently, he has been appointed as Deputy Associate Lab Director for TID strategy. His current research interests are on ultra-fast X-ray detector architectures for X-ray Free-Electron Lasers applications and developing efficient, scalable systems with "smart" real-time processing capabilities. More broadly, he is interested in understanding the fundamental performance limits of radiation detection systems.
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Persis S. Drell
Provost, Emerita, James and Anna Marie Spilker Professor, Professor of Materials Science and Engineering and of Physics
BioPersis Drell is the James and Anna Marie Spilker Professor in the School of Engineering, a professor of materials science and engineering, and a professor of physics. From Feb 1, 2017 to Sept. 30, 2023, Drell was the provost of Stanford University.
Prior to her appointment as provost in February 2017, she was dean of the Stanford School of Engineering from 2014 to 2017 and director of U.S. Department of Energy SLAC National Acceleratory Laboratory from 2007 to 2012.
She earned her bachelor’s degree in mathematics and physics from Wellesley College and her PhD in atomic physics from UC Berkeley. Before joining the faculty at Stanford in 2002, she was a faculty member in the physics department at Cornell University for 14 years. -
Leora Dresselhaus-Marais
Assistant Professor of Materials Science and Engineering, of Photon Science and, by courtesy, of Mechanical Engineering
Current Research and Scholarly InterestsMy group develops new methods to update old processes in metals manufacturing
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Ron Dror
Cheriton Family Professor and Professor, by courtesy, of Structural Biology and of Molecular & Cellular Physiology
Current Research and Scholarly InterestsMy lab’s research focuses on computational biology, with an emphasis on 3D molecular structure. We combine two approaches: (1) Bottom-up: given the basic physics governing atomic interactions, use simulations to predict molecular behavior; (2) Top-down: given experimental data, use machine learning to predict molecular structures and properties. We collaborate closely with experimentalists and apply our methods to the discovery of safer, more effective drugs.
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Shaul Druckmann
Associate Professor of Neurobiology, of Psychiatry and Behavioral Sciences and, by courtesy, of Electrical Engineering
Current Research and Scholarly InterestsOur research goal is to understand how dynamics in neuronal circuits relate and constrain the representation of information and computations upon it. We adopt three synergistic strategies: First, we analyze neural circuit population recordings to better understand the relation between neural dynamics and behavior, Second, we theoretically explore the types of dynamics that could be associated with particular network computations. Third, we analyze the structural properties of neural circuits.
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Junting Duan
Ph.D. Student in Management Science and Engineering, admitted Autumn 2020
BioJunting Duan is a PhD candidate in the Department of Management Science and Engineering (MS&E) at Stanford University. Prior to joining Stanford, she received her B.S. in Mathematics and Applied Mathematics from Peking University in 2020.
Junting's research interests lie broadly in data-driven decision-making, focusing on statistical inference and machine learning, with applications to causal inference and finance. Her research develops new methodologies with rigorous statistical foundations that enable reliable decision-making with complex and imperfect data, and lies at the intersection of (1) statistical learning for high-dimensional data; (2) causal inference; and (3) machine learning for finance and risk management. Her work has been recognized through publications and revisions at top journals including Management Science and the Journal of Econometrics, as well as invitations to present at major conferences such as the American Economic Association Annual Meeting, the NBER-NSF Time-Series Conference, the NBER Forecasting & Empirical Methods Conference, and the INFORMS Annual Meeting.
Visit her personal website for more details: https://juntingduan.com. -
John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
