CS Professor Steve Wright recently won the George B. Dantzig Prize and was profiled by SIAM, the Society for Industrial and Applied Mathematics, who awards the prize. Here is the profile in its entirety:
Dr. Stephen Wright, University of Wisconsin – Madison, is the 2024 recipient of the George B. Dantzig Prize. He received the prize for his fundamental contributions to nonlinear optimization and algorithms for control, compressed sensing, machine learning, and data science. He pioneered infeasible interior point methods which culminated in his 1997 SIAM monograph on the subject. Moreover, Dr. Wright contributed highly cited, outstanding, and very influential work in a broad range of fields in mathematical optimization, including algorithms for control, nonsmooth optimization with applications to compressed sensing, machine learning, and data science. His comprehensive contributions range from theory, algorithm design and analysis, to applications and the development of high-impact software.
The George B. Dantzig Prize is awarded ever three years to one or more individuals for original research which by its originality, breadth, and depth is having a major impact on the field of mathematical optimization. The Mathematical Optimization Society (MOS) administers the prize and it is awarded jointly by MOS and SIAM.
Dr. Wright holds the George B. Dantzig Professorship, the Sheldon Lubar Chair, and the Hilldale Professorship at the University of Wisconsin-Madison and serves as Chair of the Computer Sciences Department. His research is in computational optimization and its applications to data science and many other areas of science and engineering. Prior to joining UW-Madison in 2001, he held positions at North Carolina State University (1986-90) and Argonne National Laboratory (1990-2001).
Dr. Wright was elected to the SIAM Board of Trustees for the maximum three terms (2005-14) and has served as Chair of the Mathematical Optimization Society (2007-10). He has remained an active member of SIAM for 38 years and was named a 2011 SIAM Fellow. He served as Editor-in-Chief of SIAM Journal on Optimization (2014-19) and Mathematical Programming Series B. He has also served as an associate editor of Mathematical Programming Series A, SIAM Review, SIAM Journal on Scientific Computing, and several other journals and book series.
In 2014, Dr. Wright won the Institute of Electrical and Electronics Engineers (IEEE) W.R.G. Baker Award for best paper in an IEEE archival publication during 2009-2011. Additionally, he was awarded the 2020 Khachiyan Prize by the INFORMS Optimization Society for lifetime achievements in optimization, as well as the 2020 Neural Information Processing Systems Conference Test of Time Award. He was also recently elected to the National Academy of Engineering in 2024. He has authored and co-authored widely used text and reference books in optimization including Primal Dual Interior-Point Methods and Numerical Optimization. He has published widely on optimization theory, algorithms, software, and applications.
Q: Why are you excited to receive the award?
A: I’m thrilled to receive the George B. Dantzig Prize because it is a high honor bestowed by the optimization community, which I am so proud to be a part of. Having been deeply involved with MOS and SIAM, the two societies that sponsor the prize, it is particularly gratifying to be honored in this way.
Q: Could you tell us about the research that won you the award?
A: The Dantzig prize is a career prize, and my research over the years has covered many areas of continuous optimization and its applications. I have worked on nonlinear constrained optimization and nonsmooth optimization at various times. I had great fun participating in the interior-point “gold rush” of the 1990s, writing a book on that subject in 1997 and contributing several software packages. I started working on algorithms for compressed sensing and machine learning around 2006, and much of my work since then has focused on optimization topics relevant to machine learning.
My interest in algorithms is a constant theme. I like to work on algorithms that are useful in practice and to understand their mathematical properties. I apply these algorithms to interesting problems in science and engineering, collaborating with domain experts in machine learning, process control, computational statistics, image processing, computer networking, cancer treatment planning, and several other areas. In some cases, this work has been quite influential. Optimization is a field that is interdisciplinary by nature, producing modeling techniques and algorithmic tools that are useful to wide communities of researchers and practitioners. Besides relishing the interdisciplinary and practical sides of the field, I love its mathematical depth and enjoy participating in the interplay between theory and practice that has been a driving force in the development of the area.
It has been exciting to witness the remarkable growth in the profile of optimization in recent years. More and more domain researchers are discovering the usefulness of optimization as a way to organize models and objectives in a structured, systematic way, and also as a source of algorithmic, computational techniques to find useful solutions to the resulting problems. Optimization is indispensable to the machine learning/AI revolution, a fact that accounts in part for the increased scope and intensity of optimization research, and the growing number of researchers in the area. The skill level of younger researchers working in optimization is incredible, and both daunting and inspiring.
Q: What does your work mean to the public?
A: Because it is deeply embedded in so many technologies, optimization touches members of the public in many ways. Like many optimization researchers, algorithms and software and applications that I have worked on have touched many products in daily use; for example, in recommender systems for online commerce, investment portfolio optimization, and process control in chemical production to name a few. I believe that my books, particularly Numerical Optimization with co-author Jorge Nocedal, have spread the word about optimization to several generations of researchers, students, and practitioners.
Q: What does being a member of SIAM mean to you?
A: The prize is jointly sponsored by MOS and SIAM, and both societies are close to my heart. I remember seeing SIAM journals in the library at my father’s workplace as a boy. I joined SIAM as soon as I graduated, in 1985, later serving on the Board of Trustees for nine years and as Editor-in-Chief of SIAM Journal on Optimization for five years. I served in the leadership of MOS for 17 years, including a term as Chair. Both societies do a wonderful job nurturing and promoting their overlapping professional communities through their journals, conferences, and book series. I helped to establish the MOS-SIAM Series on Optimization. Through both societies, I have been privileged to work alongside colleagues whose dedication to service and research accomplishments are constant sources of inspiration.