Institute for Computational and Mathematical Engineering (ICME)
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Juan J. Alonso
Professor of Aeronautics and Astronautics
BioProf. Alonso is the founder and director of the Aerospace Design Laboratory (ADL) where he specializes in the development of highfidelity 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 reentry vehicles. He is the author of over 200 technical publications on the topics of computational aircraft and spacecraft design, multidisciplinary optimization, fundamental numerical methods, and highperformance 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 coursedevelopment 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.

Biondo Biondi
Professor of Geophysics
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 activesource experiments (reflection data), or by faraway earthquakes (teleseismic data). The highresolution and fidelity of 3D reflectionseismic 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 oneday 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); codirector, Stanford Exploration Project (1998present); founding member, Editorial Board of SIAM Journal on Imaging Sciences (2007present); member, SEG Research Committee (1996present); chairman, SEG/EAGE Summer Research Workshop (2006) 
Reza Bosagh Zadeh
Adjunct Professor, Institute for Computational and Mathematical Engineering (ICME)
BioReza Bosagh Zadeh is an Adjunct Professor at Stanford University and Founder CEO at Matroid. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford University under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Board of Microsoft and Databricks.
As part of his research, Reza built the Machine Learning Algorithms behind Twitter's whotofollow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza's work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhDlevel classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305). 
Stephen Boyd
Samsung Professor in the School of Engineering and Professor, by courtesy, of Computer Science and of Management Science and Engineering
BioStephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is 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 the faculty of 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, CUHKShenzhen, 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 three books: Convex Optimization (with Lieven Vandenberghe, 2004), Linear Matrix Inequalities in System and Control Theory (with L. El Ghaoui, E. Feron, and V. 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 parsersolvers for convex optimization.
Professor Boyd 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 BealeOrchardHays Award, given every three years for excellence in computational mathematical programming. He is a Fellow of the IEEE, SIAM, and INFORMS, a Distinguished Lecturer of the IEEE Control Systems Society, and a member of the US National Academy of Engineering and a foreign member of the Chinese Academy of Engineering. 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, with citation: “For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization.” 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." 
Carlos Bustamante
Professor of Biomedical Data Science, of Genetics and, by courtesy, of Biology
Current Research and Scholarly InterestsMy research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My group works on a variety of organisms and model systems ranging from humans and other primates to domesticated plant and animals. Much of our research is at the interface of computational biology, mathematical genetics, and evolutionary genomics.

Emmanuel Candes
BarnumSimons Chair in Math and Statistics, and Professor of Statistics and, by courtesy, of Electrical Engineering
BioEmmanuel Candès is the BarnumSimons 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 earlycareer 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 solidstate 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

Eric Darve
Associate Professor of Mechanical Engineering
BioProfessor Darve's research is focused on the development of numerical methods for large scale scientific computing with applications in biomolecular simulations, acoustics, electromagnetics, and microfluidics. In these applications, the computational expense of simulating large and complex systems is very significant and in many instances beyond current computer capabilities. He is developing innovative numerical techniques to reduce this computational expense and enable the simulation of complex systems over realistic time scales. Professor Darve also uses processors with novel architectures, such as GPUs and the Cell processor, for scientific computing. Applications range from particle simulation to fluid dynamics and solving partial differential equations.

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.