Karen E. Willcox, PhD, NAE, MNZM
Director, Oden Institute for Computational Engineering and Sciences | Associate Vice President for Research | Professor of Aerospace Engineering and Engineering Mechanics | W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences | Peter O'Donnell, Jr. Centennial Chair in Computing Systems | External Professor, Santa Fe Institute
Presenting Digital twins: A personalized future of computing for complex systems at TEDxUTAustin. Link to Youtube video.
Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences, Associate Vice President for Research, and Professor of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin. She holds the W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences and the Peter O'Donnell, Jr. Centennial Chair in Computing Systems. Prior to joining the Oden Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as Professor of Aeronautics and Astronautics, the founding Co-Director of the MIT Center for Computational Engineering, and the Associate Head of the MIT Department of Aeronautics and Astronautics. She is also an External Professor at the Santa Fe Institute. Willcox holds a Bachelor of Engineering Degree from the University of Auckland, New Zealand, and masters and PhD degrees from MIT. Prior to becoming a professor at MIT, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group.
In 2022, Willcox was elected to the National Academy of Engineering "for contributions to computational engineering methods for the design and optimal control of high-dimensional systems with uncertainties." In 2017, she was awarded Member of the New Zealand Order of Merit (MNZM) for services to aerospace engineering and education. In 2016, she was awarded a Distinguished Alumni Award from the University of Auckland. Her professional accomplishments have been recognized through her election as a Fellow of the Society for Industrial and Applied Mathematics (SIAM) and a Fellow of the American Institute of Aeronautics and Astronautics (AIAA). Her students and postdoctoral researchers have been recognized with many awards over the years, including multiple best paper and best student paper awards.
Willcox is active in national service and leadership roles. She currently serves as a member of the Department of the Air Force Scientific Advisory Board, an elected member of the AIAA Board of Trustees, vice-chair of the National Academies Board on Mathematical Sciences and Analytics, vice-chair of the Institute for Mathematical and Statistical Innovation (IMSI) Board of Advisors, a member of the Advisory Board for the Advanced Simulation and Computing (ASC) program at the Los Alamos National Laboratory, and an elected member of the Executive Council of the International Association for Computational Mechanics (IACM). She has served on six National Academies studies and review panels, including two current studies: the National Academies Study on Post-Exascale Computing for the National Nuclear Security Administration, and the National Academies Study on Foundational Research Gaps and Future Directions for Digital Twins, for which she serves as chair. She has served on multiple visiting committee and university review panels, including serving as the current chair of the Committee to Visit Harvard University Information Technology. Willcox has served in multiple other leadership positions, including co-chair of the NSF Advisory Committee on Cyberinfrastructure, Vice-Chair and Program Director of the SIAM Activity Group on Computational Science and Engineering, Program Director of the SIAM Activity Group on Data Science, and Chair of the AIAA Multidisciplinary Design Optimization Technical Committee. She is formerly Section Editor of SIAM Journal on Scientific Computing and current Editorial Board member of AIAA Journal, Acta Numerica, IEEE Computing in Science and Engineering (CiSE), Journal on Data Centric Engineering, Computer Methods in Applied Mechanics and Engineering, and International Journal of Computational Methods in Engineering Science and Mechanics.
Willcox's research has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on reduced-order modeling as a way to learn principled physics-based approximations from data, multifidelity formulations to leverage multiple sources of information in decision-making and uncertainty quantification, and scalable methods for predictive digital twins. Her model reduction and multifidelity methods are widely applied across the scientific and engineering community, and have been incorporated into industry/government codes for aircraft system design and environmental policy decision-making. She currently has funded projects supported by the US Air Force Office of Scientific Research, Air Force Research Laboratory, DARPA, Department of Energy, NASA, and Texas Higher Education Coordinating Board. Willcox currently leads several multi-institution research teams: she is Co-director of the Department of Energy M2dt Multifaceted Mathematics Capability Center on Multifaceted Mathematics for Predictive Digital Twins; she leads an Air Force MURI team on Machine Learning for Physics-based Systems; and she leads the Rise of the Machines team developing robust, interpretable, scalable, efficient methods for digital twins under the Department of Energy AI and Decision Support for Complex Systems program. Willcox has co-authored more than 130 papers in peer-reviewed journals and advised 54 graduate students, including 22 PhD students.
In addition to her research pursuits, Willcox is active in education innovation. She served as co-Chair of the MIT Online Education Policy Initiative, co-Chair of President Rafael Reif's 2013-2014 Institute wide Task Force on the Future of MIT Education, and Chair of the MIT OpenCourseWare Faculty Advisory Board. She is a recognized innovator in the U.S. education landscape, where she was a 2015 recipient of a First in the World Department of Education grant that developed and deployed educational technologies in community colleges. She continues to direct the Mapping Lab, which develops technologies for the future of digital education.
I was born and raised in Auckland, New Zealand. I am a first-generation college student, the daughter of an air-conditioning fitter and a secretary, neither of whom finished high school. The first time I left New Zealand was in 1994, when I travelled to Boston to begin graduate school at MIT. As the first of my entire family and extended family to attend college, I fully appreciate the challenges facing today's first-generation college students: the overwhelming uncertainty of choosing a degree path, the financial need to work a part-time job during studies, the difficulty in navigating all those extra things like finding a summer internship, the challenge of being far from home, and the general burden of meeting your family's expectations while knowing that they can't possibly understand what you're going through. I am incredibly fortunate to have had some amazing mentors through my career, and that has instilled in me a lifelong commitment to mentoring.
I am passionate about engineering and the contribution it makes to society. Engineers have literally built the world around us. They have made our lives better in countless ways and they have enabled the human race to make superhuman achievements. I am inspired to advance the impact of engineering on the world through my research that sits at the interfaces of engineering, mathematics, and computation. I also have a strong commitment to teaching and education. Every day I am inspired to be a part of educating the next generation of engineers. I am also passionate about the role that engineering thinking and technology can play in advancing education more broadly. I have an unwavering commitment to building a more diverse and more inclusive professional community. Diversity of perspective is a driving part of my research group — the group brings together students with different backgrounds (engineering, mathematics, computer science, physics), different nationalities (Belgium, Canada, Colombia, Germany, India, Italy, New Zealand, Romania, Ukraine, USA), and different life experiences. We all learn from each other every day, and that drives us towards excellence.
Karen is honored to be appointed to the Department of the Air Force Scientific Advisory Board.
The group have co-authored four papers that will be presented at the January 2023 AIAA Scitech Forum. Ben Zastrow will present our collaborative work with Lockheed Martin on reduced-order modeling for coupled aerostructural problems. Ionut Farcas will present our collaborative work with AFRL on parametric reduced-order modeling for rotating detonation rocket engines. Vignesh Sella will present a study of multifidelity regression methods for data-poor applications. Anirban Chaudhuri is a co-author on a collaborative paper with the Kim group at UCSD on multifidelity robust topology optimization.
Karen is chairing a National Academies study on Foundational Research Gaps and Future Directions for Digital Twins.
We have released the Operator Inference in Python package, with new documentation and tutorial examples.
Congratulations to postdoc Rudy Geelen on having his paper Operator inference for non-intrusive model reduction with nonlinear manifolds accepted for publication in Computer Methods in Applied Mechanics and Engineering. A collaboration with Steve Wright, this paper proposes a novel approach for learning a data-driven quadratic manifold from high-dimensional data, then employing this quadratic manifold to derive efficient physics-based reduced-order models.
Karen will deliver the 2022 Leçons Jacques-Louis Lions at the Sorbonne. The mini-course "Learning physics-based models from data: Perspectives from projection-based model reduction" will take place October 25, 26 and 27. The colloquium "Mathematical and computational foundations for enabling predictive digital twins at scale" will take place on October 28.
Karen presented Digital twins: A personalized future of computing for complex systems at TEDxUTAustin in March 2022. The invited talk is now published on Youtube.
Congratulations to CSEM PhD student Sean McBane on the successful defense of his PhD thesis "Topology optimization and uncertainty quantification using component-wise reduced order modeling." Sean's thesis shows the power of decomposition approaches together with component-based reduced-order modeling to address the challenges of complex systems with high-dimensional parameter spaces. Sean will start a job at Cadence this month.
On July 1, Karen starts a six-year term as a member of the International Association for Computational Mechanics (IACM) Executive Council.
Karen is giving an invited keynote talk at the Department of Energy AI for Science, Engineering and Security workshop.
Congratulations to postdoc Parisa Khodabakhshi, who will start a tenure track position in the fall at Lehigh University. In the past three years working with me, Parisa has made some excellent contributions in the development of reduced-order modeling for additive manufacturing and multifidelity uncertainty quantification methods for nonlocal problems.
Congratulations to postdoc Marco Tezzele for winning the ECCOMAS prize for best thesis of 2021 for his thesis Data-driven parameter and model order reduction for industrial optimisation problems with applications in naval engineering, which he completed with SISSA Professor Gianlugi Rozza.
The Oden Institute will be co-hosting Rising Stars in Computational and Data Sciences in Albuquerque April 20-21. We had so many outstanding nominations that it was very difficult to choose the 32 attendees. I can't wait to meet all these amazing young women for two days of research presentations, career discussions and networking.
Congratulations to Elizabeth Qian and Ionut Farcas for having their paper Reduced operator inference for nonlinear partial differential equations accepted to SIAM Journal on Scientific Computing. The paper develops the Operator Inference reduced-order modeling approach in the continuous (i.e., PDE) setting and applies the method to a large-scale 3D combustion problem.
Farewell to postdoc Michael Kapteyn, who leaves us to pursue the next stage of his career. Michael's PhD work in digital twins has laid the groundwork for so many exciting new projects. Best of luck Michael!
Karen has been elected to the National Academy of Engineering "for contributions to computational engineering methods for the design and optimal control of high-dimensional systems with uncertainties." I am so appreciative of all the opportunities I have had throughout my career, made possible only by working with the most amazing students, postdocs, collaborators, colleagues and staff.
Congratulations to Michael Kapteyn for having his paper A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scale selected for the Nature Computational Science one-year anniversary collection.
Karen was honored to give the AIAA Structures, Structural Dynamics, and Materials (SDM) Lecture, presented at AIAA SciTech Forum. Here is a link to her slides for her talk titled From reduced-order modeling to scientific machine learning: How computational science is enabling the design of next-generation aerospace systems.