Stanford University
Showing 1-100 of 455 Results
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Sara Achour
Assistant Professor of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsI am an Assistant Professor jointly appointed to both the Computer Science and the Electrical Engineering Departments at Stanford University. My research focuses on new techniques and tools, specifically new programming languages, compilers, and runtime systems, that enable end-users to more easily develop computations that exploit the potential of emerging computing platforms that exhibit analog behaviors.
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Juan Alonso
Vance D. and Arlene C. Coffman Professor and the James and Anna Marie Spilker Chair of the Department of Aeronautics and Astronautics
BioProf. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of high-fidelity computational design methodologies to enable the creation of realizable and efficient aerospace systems. Prof. Alonso’s research involves a large number of different manned and unmanned applications including transonic, supersonic, and hypersonic aircraft, helicopters, turbomachinery, and launch and re-entry vehicles. He is the author of over 200 technical publications on the topics of computational aircraft and spacecraft design, multi-disciplinary optimization, fundamental numerical methods, and high-performance parallel computing. Prof. Alonso is keenly interested in the development of an advanced curriculum for the training of future engineers and scientists and has participated actively in course-development activities in both the Aeronautics & Astronautics Department (particularly in the development of coursework for aircraft design, sustainable aviation, and UAS design and operation) and for the Institute for Computational and Mathematical Engineering (ICME) at Stanford University. He was a member of the team that currently holds the world speed record for human powered vehicles over water. A student team led by Prof. Alonso also holds the altitude record for an unmanned electric vehicle under 5 lbs of mass.
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Afshine Amidi
Affiliate, Institute for Computational and Mathematical Engineering (ICME)
BioTeaching CME 295: Transformers & Large Language Models (https://cme295.stanford.edu/) and CME 296: DIffusion & Large Vision Models.
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Amin Arbabian
Professor of Electrical Engineering
Current Research and Scholarly InterestsMy group's research covers RF circuits and system design for (1) biomedical, (2) sensing, and (3) Internet of Things (IoT) applications.
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Zhenan Bao
K. K. Lee Professor, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Materials Science and Engineering, of Chemistry, and of Bioengineering
BioZhenan Bao joined Stanford University in 2004. She is currently a K.K. Lee Professor in Chemical Engineering, and with courtesy appointments in Chemistry, Bioengineering and Material Science and Engineering. She was the Department Chair of Chemical Engineering from 2018-2022 and in 2025. She founded the Stanford Wearable Electronics Initiative (eWEAR) and is the current faculty director. Bao received her Ph.D. degree in Chemistry from The University of Chicago in 1995 and joined Bell Labs, Lucent Technologies. She became a Distinguished Member of Technical Staff in 2001. Professor Bao currently has more than 800 refereed publications and more than 80 US patents with a Google Scholar H-index 237.
Bao is a member of the US National Academy of Sciences, National Academy of Engineering, the American Academy of Arts and Sciences and the National Academy of Inventors. Bao was elected a foreign member of the Chinese Academy of Science in 2021. She is a Fellow of AAAS, ACS, MRS, SPIE, ACS POLY and ACS PMSE.
Bao is a member of the Board of Directors for the Camille and Dreyfus Foundation from 2022. She served as a member of Executive Board of Directors for the Materials Research Society and Executive Committee Member for the Polymer Materials Science and Engineering division of the American Chemical Society. She co-founded C3 Nano Co. (acquired by Du Pont) and PyrAmes, which have produced products used in commercial smartphones and hospitals, respectively. Multiple inventions from her lab have been licensed and served as foundational technologies for several additional start-ups.
Bao was a recipient of the VinFuture Prize Female Innovator 2022, ACS Award of Chemistry of Materials 2022, MRS Mid-Career Award in 2021, AICHE Alpha Chi Sigma Award 2021, ACS Central Science Disruptor and Innovator Prize in 2020, ACS Gibbs Medal in 2020, the Wilhelm Exner Medal from the Austrian Federal Minister of Science in 2018, the L'Oreal UNESCO Women in Science Award North America Laureate in 2017. She was awarded the ACS Applied Polymer Science Award in 2017, ACS Creative Polymer Chemistry Award in 2013 ACS Cope Scholar Award in 2011. She is a recipient of the Royal Society of Chemistry Beilby Medal and Prize in 2009, IUPAC Creativity in Applied Polymer Science Prize in 2008.
In Stanford, Bao has pioneered molecular design concepts and fabrication processes to advance the scope and applications of skin-inspired electronics. Her group discovered nano confinement effect of conjugated polymers in polymer blends, which established the fundamental foundation for skin-inspired electronic materials and devices. Her work has resulted in new materials and device solutions for soft robotics, wearable and implantable electronics for precision health, precision mental health and advanced tools for understanding neuroscience and treatment of neurodegenerative diseases. Building on chemical insights, her group has developed foundational materials and devices that enabled a new generation of skin-inspired soft electronics. They provide unprecedented opportunities for understanding human health through developing monitoring, diagnosis and treatment tools. Some examples include: a neuromorphic e-skin that can sense force and temperature and directly communicate with brain, a wireless wound healing patch, a soft NeuroString for simultaneous neurochemical monitoring in the brain and gut, soft high-density electrophysiological recording array, a meta-learned skin sensor for detailed body movements, a reconfigurable self-healing electronic skin. -
Clark Barrett
Mizuki Asano and Thomas McGrath Professor
Current Research and Scholarly InterestsAutomated reasoning; satisfiability modulo theories (SMT); formal methods;
formal verification; verification of smart contracts; verification of neural
networks; AI safety; security; hardware design productivity and verification. -
Stacey Bent
Jagdeep & Roshni Singh Professor in the School of Eng, Professor of Energy Science and Eng, Senior Fellow at Precourt & Prof, by courtesy, of Electrical Eng, Materials Sci Eng & Chemistry
On Leave from 04/01/2025BioThe research in the Bent laboratory is focused on understanding and controlling surface and interfacial chemistry and applying this knowledge to a range of problems in semiconductor processing, micro- and nano-electronics, nanotechnology, and sustainable and renewable energy. Much of the research aims to develop a molecular-level understanding in these systems, and hence the group uses of a variety of molecular probes. Systems currently under study in the group include functionalization of semiconductor surfaces, mechanisms and control of atomic layer deposition, molecular layer deposition, nanoscale materials for light absorption, interface engineering in photovoltaics, catalyst and electrocatalyst deposition.
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Biondo Biondi
Barney and Estelle Morris Professor
Current Research and Scholarly InterestsResearch
My students and I devise new algorithms to improve the imaging of reflection seismic data. Images obtained from seismic data are the main source of information on the structural and stratigraphic complexities in Earth's subsurface. These images are constructed by processing seismic wavefields recorded at the surface of Earth and generated by either active-source experiments (reflection data), or by far-away earthquakes (teleseismic data). The high-resolution and fidelity of 3-D reflection-seismic images enables oil companies to drill with high accuracy for hydrocarbon reservoirs that are buried under two kilometers of water and up to 15 kilometers of sediments and hard rock. To achieve this technological feat, the recorded data must be processed employing advanced mathematical algorithms that harness the power of huge computational resources. To demonstrate the advantages of our new methods, we process 3D field data on our parallel cluster running several hundreds of processors.
Teaching
I teach a course on seismic imaging for graduate students in geophysics and in the other departments of the School of Earth Sciences. I run a research graduate seminar every quarter of the year. This year I will be teaching a one-day short course in 30 cities around the world as the SEG/EAGE Distinguished Instructor Short Course, the most important educational outreach program of these two societies.
Professional Activities
2007 SEG/EAGE Distinguished Instructor Short Course (2007); co-director, Stanford Exploration Project (1998-present); founding member, Editorial Board of SIAM Journal on Imaging Sciences (2007-present); member, SEG Research Committee (1996-present); chairman, SEG/EAGE Summer Research Workshop (2006) -
Jeannette Bohg
Associate Professor of Computer Science
BioJeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Division of Robotics, Perception and Learning (RPL) at KTH in Stockholm. In her thesis, she proposed novel methods towards multi-modal scene understanding for robotic grasping. She also studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning. Jeannette Bohg has received several awards, most notably the 2019 IEEE International Conference on Robotics and Automation (ICRA) Best Paper Award, the 2019 IEEE Robotics and Automation Society Early Career Award and the 2017 IEEE Robotics and Automation Letters (RA-L) Best Paper Award.
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Ivo Bolsens
Adjunct Professor
BioDirector of System X and instructor for EE310
Ivo retired from AMD as Senior Vice-President Corporate Research and Advanced Development. He managed advanced hardware and software technology development, including future architectures and software stacks to enable emerging opportunities in the fields of AI and embedded computing. His team was also driving the university partnerships to create a thriving, global ecosystem for AMD technology in academia.
He joined AMD in 2022, as part of the Xilinx acquisition. At Xilinx, he served as the Chief Technology Officer in charge of corporate research. He joined Xilinx in 2001 from the Interuniversity Microelectronics Centre (IMEC), an international research center based in Belgium. At IMEC he was vice president leading the R&D of digital signal processing hardware and software. During his tenure at IMEC, he spun-out several successful startups in the field of SOC design tools and wireless systems.
He serves on the advisory boards of IMEC, the Engineering Departments of San Jose State University and Santa Clara University, and the Department of Electrical Engineering and Computer Sciences at UC Berkeley.
He received his Master’s degree and PhD degree (EE) from the KU Leuven university in Belgium. -
Stephen Boyd
Samsung Professor in the School of Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, and a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance.
Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Tsinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, Harbin Institute of Technology, NYU, MIT, UC Berkeley, CUHK-Shenzhen, and IMT Lucca. He holds honorary doctorates from Royal Institute of Technology (KTH), Stockholm, and Catholic University of Louvain (UCL).
Professor Boyd is the author of many research articles and four books: Introduction to Applied Linear Algebra: Vectors, Matrices, and Least-Squares (with Lieven Vandenberghe, 2018), Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with El Ghaoui, Feron, and Balakrishnan, 1994), and Linear Controller Design: Limits of Performance (with Craig Barratt, 1991). His group has produced many open source tools, including CVX (with Michael Grant), CVXPY (with Steven Diamond) and Convex.jl (with Madeleine Udell and others), widely used parser-solvers for convex optimization.
He has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, for excellence in computational mathematical programming. In 2023, he was given the AACC Richard E. Bellman Control Heritage Award, the highest recognition of professional achievement for U.S. control systems engineers and scientists. In 2025, he was awarded the IFAC Nathaniel B. Nichols Medal.
He is a Fellow of the IEEE, SIAM, INFORMS, IFAC, and ACA, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, a foreign member of the Chinese Academy of Engineering, and a foreign member of the National Academy of Engineering of Korea. He has been invited to deliver more than 100 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.
He has developed and taught many undergraduate and graduate courses, including Signals & Systems, Linear Dynamical Systems, Convex Optimization, and a recent undergraduate course on Matrix Methods. His graduate convex optimization course attracts around 300 students from more than 20 departments. In 1991 he received an ASSU Graduate Teaching Award, and in 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering. In 2003, he received the AACC Ragazzini Education award, for contributions to control education. In 2016 he received the Walter J. Gores award, the highest award for teaching at Stanford University. In 2017 he received the IEEE James H. Mulligan, Jr. Education Medal, for a career of outstanding contributions to education in the fields of interest of IEEE, with citation "For inspirational education of students and researchers in the theory and application of optimization." -
Leticia Britos Cavagnaro
Adjunct Professor
BioLeticia Britos Cavagnaro is a scientist-turned-designer working to shape the future of teaching and learning at the Stanford Hasso Plattner Institute of Design (d.school). She holds a PhD from Stanford, at the intersection of Developmental Biology and Computer Science.
Leticia has started and leads several d.school initiatives that support pedagogical innovation globally, such as the Teaching and Learning Studio –an open-enrollment professional development program that has trained over 1000 higher education professors and leaders since 2016– and the Innovative Teaching Scholars program –a collaboration with the Stanford Engineering Center for Global & Online Education to train university educators in Thailand.
She was the founding Deputy Director of the National Center for Engineering Pathways to Innovation (Epicenter), an NSF-funded initiative to foster innovation and entrepreneurship in engineering education across the US that was spearheaded by Stanford's Management Science & Engineering department from 2011 to 2016. With Epicenter as its launchpad, Leticia co-founded, scaled, and spun-off the University Innovation Fellows program, which empowers students globally to be co-designers of their education in collaboration with faculty and leaders at their schools. The program has trained over 3200 Fellows from over 326 universities in 24 countries.
Leticia teaches Capstone Project and Advanced Reflective Practice to graduate students in Stanford’s MS Design program. She also develops tools that leverage emerging technologies like AI to empower learners to be self-directed, action-oriented, and reflective. Riff –the generative AI reflection assistant Leticia developed– is being used by hundreds of educators globally and has supported over 25,000 student reflections in the past year.
Leticia is a thought leader in pedagogical innovation. Her recent book Experiments In Reflection invites us to expand how we see the past and present and to become bold shapers of the future.
She was born in Uruguay and lives in San Francisco with her husband.
Connect with Leticia: https://www.linkedin.com/in/leticiabc/ -
Mark Brongersma
Director, Geballe Laboratory for Advanced Materials (GLAM), Stephen Harris Professor, Professor of Materials Science and Engineering and, by courtesy, of Applied Physics
BioMark Brongersma is a Professor in the Department of Materials Science and Engineering at Stanford University. He received his PhD in Materials Science from the FOM Institute in Amsterdam, The Netherlands, in 1998. From 1998-2001 he was a postdoctoral research fellow at the California Institute of Technology. During this time, he coined the term “Plasmonics” for a new device technology that exploits the unique optical properties of nanoscale metallic structures to route and manipulate light at the nanoscale. His current research is directed towards the development and physical analysis of nanostructured materials that find application in nanoscale electronic and photonic devices. Brongersma received a National Science Foundation Career Award, the Walter J. Gores Award for Excellence in Teaching, the International Raymond and Beverly Sackler Prize in the Physical Sciences (Physics) for his work on plasmonics, and is a Fellow of the Optical Society of America, the SPIE, and the American Physical Society.
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Brighton Brown
Undergraduate, Hasso Plattner Institute of Design
Design Leadership Coach, d.schoolBiowww.brightonbrown.com
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Emmanuel Candes
Barnum-Simons Chair of Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics, a professor of electrical engineering (by courtesy) and a member of the Institute of Computational and Mathematical Engineering at Stanford University. Earlier, Candès was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His research interests are in computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization with applications to the imaging sciences, scientific computing and inverse problems. He received his Ph.D. in statistics from Stanford University in 1998.
Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by the National Science Foundation, and which recognizes the achievements of early-career scientists. He has given over 60 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. -
Gunnar Carlsson
Ann and Bill Swindells Professor, Emeritus
BioDr. Carlsson has been a professor of mathematics at Stanford University since 1991. In the last ten years, he has been involved in adapting topological techniques to data analysis, under NSF funding and as the lead PI on the DARPA “Topological Data Analysis” project from 2005 to 2010. He is the lead organizer of the ATMCS conferences, and serves as an editor of several Mathematics journals
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Carissa Carter
Adjunct Professor
BioCarissa Carter is the Academic Director at the Stanford d.school. In this role she guides the development of the d.school’s pedagogy, leads its instructors, and shapes its class offerings. She teaches courses on the intersection of data and design, design for climate change, design for emerging tech, and maps and the visual sorting of information.
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Haoxuan Chen
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2022
BioPersonal website: https://haoxuanstevec00.github.io/
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Srabanti Chowdhury
Professor of Electrical Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Materials Science and Engineering
Current Research and Scholarly InterestsWide bandap materials & devices for RF, Power and energy efficient electronics
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John M. Cioffi
Hitachi America Professor in the School of Engineering, Emeritus
Current Research and Scholarly InterestsCioffi researches and teaches in the area of digital transmission. He introduced the basic transmission methods that are a foundation for all modern broadband internet connectivity, which corresponding patents are Stanford Engineering's all-time #2 royalty generator (after #1 search engine). Roughly half his career was spent in industry during various periods as Stanford student or faculty. He has been primary advisor for over 90 Stanford PhD students, and taught communications to 1000's.
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Daniel Norbert Congreve
Assistant Professor of Electrical Engineering
BioDan is an Assistant Professor in the Department of Electrical Engineering at Stanford University. Prior to Stanford, Dan received his B.S. and M.S. from Iowa State in 2011, working with Vik Dalal studying defect densities of nano-crystalline and amorphous silicon. He then received his PhD from MIT in Electrical Engineering in 2015, studying under Marc Baldo. His thesis work focused on photonic energy conversion using singlet fission and triplet fusion as downconverting and upconverting processes, respectively. He spent a year as a postdoc with Will Tisdale in Chemical Engineering at MIT studying perovskite nanoplatelets. He joined the Rowland Institute in 2016 as a Rowland Fellow before starting at Stanford in 2020. Dan is a Moore Inventor Fellow, Sloan Research Fellow, Intel Rising Star, and co-founder of Quadratic3D, a startup looking to commercialize 3D printing technologies. His current research interests focus on engineering nanomaterials to solve challenging problems.
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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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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.
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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. -
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
