Institute for Computational and Mathematical Engineering (ICME)
Showing 1-20 of 161 Results
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Guillermo Aboumrad Sidaoui
Adjunct Lecturer, Institute for Computational and Mathematical Engineering (ICME)
BioWillie was born and raised in Mexico City. He later moved to the UK to complete his high school studies. In the fall of 2014, Willie arrived at Stanford to begin his undergraduate career in Mathematics. Interested in applications of mathematical theory, he later gained admission to the Master's program at ICME. He is currently pursuing a doctoral degree under the advisory of Prof. Daniel Bump.
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Izabel Pirimai Aguiar
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
Digital Transformation Course Grader, Stanford Center for Professional DevelopmentBioHello! I’m a fifth year PhD candidate at ICME where I’m lucky to be advised by Johan Ugander, and grateful to be a Knight-Hennessy Scholar and NSF Graduate Research Fellow. I received my BS in Applied Mathematics and Statistics from the Colorado School of Mines in May 2017 and my MS in Computer Science from the University of Colorado, Boulder in August 2018. After receiving my MS I was a visiting researcher in the Stanford Autonomous Systems Lab, a Safeway cake decorator, and the owner and baker of Bell’s Bakery.
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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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Ryan Michael Aronson
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2018
BioI am a sixth year PhD student in the Institute for Computational and Mathematical Engineering (ICME). I am mainly interested in developing numerical methods with applications to computational mechanics and fluid dynamics. I am particularly interested in high-order, structure-preserving, finite element, and isogeometric methods. Prior to coming to Stanford, I earned a B.S. in Aerospace Engineering Sciences at the University of Colorado Boulder, where I worked with Professor John Evans on residual-based variational multiscale turbulence modeling, isogeometric, structure-preserving collocation methods, and stabilized isogeometric collocation methods. Currently I work with Professor Hamdi Tchelepi on stabilized methods for compositional geomechanics problems. I have also had the pleasure of working industry internships with Meta Reality Labs, TotalEnergies, Walt Disney Animation Studios, and SLB.
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Amel Awadelkarim
Ph.D. Student in Computational and Mathematical Engineering, admitted Autumn 2017
BioMy academic background is in Computational Fluid Dynamics, Finite Element Analysis, and Continuum Mechanics with an M.S. in Engineering Science and Mechanics from Penn State University. I am becoming more and more intrigued by data analytics & applying machine learning techniques to social sciences and networks.
Outside of academia, my interests include consuming music at all times (digitally and at live shows), competing on various Ultimate Frisbee teams (Club and National levels), cooking, and generally exploring the surrounding area. -
Biondo Biondi
Barney and Estelle Morris Professor
On Leave from 09/01/2023 To 08/31/2024Current 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) -
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. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, 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 90 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."
