Karen E. Willcox, PhD, 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

Research in Multi-fidelity uncertainty quantification, multi-fidelity optimization under uncertainty, adaptive reduced models, predictive digital twins, scientific machine learning, and data to decisions in aerospace engineering


201 E 24th Street, POB 4.102
University of Texas at Austin
Austin, TX 78712
kwillcox@oden.utexas.edu
(512) 471-3312
Presenting Predictiv Data Science at SC 19

Biography

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. Her research has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on model reduction as a way to learn principled approximations from data and on multi-fidelity formulations to leverage multiple sources of information in decision-making and uncertainty quantification. 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. Willcox currently leads several multi-institution research teams: she is Co-director of the Department of Energy AEOLUS Multifaceted Mathematics Capability Center on Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems; 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 100 papers in peer-reviewed journals and advised more than 50 graduate students, including 19 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 the 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 is 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 MIT Mapping Lab, which develops technologies for the future of digital education.

Willcox is Fellow of the Society for Industrial and Applied Mathematics (SIAM), Fellow of the American Institute of Aeronautics and Astronautics (AIAA), and member of the American Society for Engineering Education (ASEE). She has served in multiple leadership positions within AIAA and SIAM, including leadership roles in the SIAM Activity Group on Computational Science and Engineering, the SIAM Activity Group on Data Science, and the AIAA Multidisciplinary Design Optimization Technical Committee. She is currently a member of the AIAA Board of Trustees. She is formerly Section Editor of SIAM Journal on Scientific Computing and current Associate Editor of AIAA Journal. She is a current member of the National Academies Board on Mathematical Sciences and Analytics, and has served on five National Academies studies and review panels. In 2017, she was awarded Member of the New Zealand Order of Merit (MNZM).

Resume

About me

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, China, India, Iran, New Zealand, Romania, USA), and different life experiences. We all learn from each other every day, and that drives us towards excellence.

Recent News

January 2021

Karen is giving a keynote talk on "Aerospace Design in the Age of Big Data and Big Compute" at the AIAA Scitech Forum on Tuesday January 19.

Congratulations to PhD student Shane McQuarrie for having his paper Data-driven reduced-order models via regularized operator inference for a single-injector combustion process accepted for publication in the Journal of the Royal Society of New Zealand. This paper derives predictive reduced-order models for rocket engine combustion dynamics via our Operator Inference approach, a scientific machine learning approach that blends data-driven learning with physics-based modeling. The codes and example problems are available on Github.

December 2020

Karen is giving an invited talk the Machine Learning for Engineering Modeling, Simulation and Design workshop at NeurIPS 2020.

Congratulations to Elizabeth Qian for a successful PhD defense!

November 2020

Congratulations to PhD student Michael Kapteyn for winning the 2020 AIAA Best MDO Paper Prize for his paper Toward predictive digital twins via component-based reduced-order models and interpretable machine learning. This award is presented to the paper selected from among all AIAA papers published at MDO sessions in 2020 AIAA conferences.

Congratulations to Anirban for having his paper mfEGRA: Multifidelity Efficient Global Reliability Analysis through Active Learning for Failure Boundary Location accepted to Structural and Multidisciplinary Optimization. The paper develops a multifidelity version of the popular EGRA method, which is used for locating contours (e.g., failure boundaries, stability boundaries). The multifidelity formulation provides significant computational speedups.

October 2020

Congratulations to postdoc Ionut Farcas for being awarded the Heinz Schwaertzel prize for his PhD thesis. This prize is awarded to the best PhD thesis in fundamentals of computer science that was submitted to any university in Munich in the previous two years.

July 2020

Karen's keynote talk on Scientific Machine Learning at JuliaCon is posted on youtube here.

Karen had an opinion piece on Scientific Machine Learning published in Aerospace Testing International.

June 2020

Karen has been elected to the AIAA Board of Trustees.

Welcome to new postdoc James Koch, who joins us from University of Washington. James is an expert in propulsion (particularly rotating detonation engines) and nonlinear dynamical systems. He will be working on combining physics-based modeling and data-driven learning for the challenging problems in our Air Force Center of Excellence on Multi-fidelity Modeling of Rocket Combustion Dynamics.

May 2020

Congratulations to Michael Kapteyn for having his paper Data-driven physics-based digital twins via a library of component-based reduced-order models accepted for publication in International Journal for Numerical Methods in Engineering. This paper will appear as part of a special issue on Digital Twins. This work is collaborative with David Knezevic, Phuong Huynh and Minh Tran of Akselos.

April 2020

Congratulations to Elizabeth Qian for being selected as a winner of the 2020 SIAM Student Paper Prize for her paper Multifidelity Monte Carlo estimation of variance and sensitivity indices. The paper appeared in SIAM/ASA Journal on Uncertainty Quantification in 2018. This paper is joint work with our DiaMonD collaborators Monty Vesselinov and Dan O'Malley at Los Alamos National Laboratory.

Welcome to new postdoc Rudy Geelen, who joins us from Duke University. Rudy is an expert in phase-field models for fracture. He will be working as part of the AEOLUS team to develop reduced-order modeling and uncertainty quantification methods in our additive manufacturing and materials testbed application problems.

March 2020

Welcome to new postdocs Mengwu Guo and Ionut Farcas. Mengwu joined the group in January, from EPFL. His research expertise includes reduced-order modeling, uncertainty quantification, and scientific machine learning. Ionut joined the group in February, from TU Munich. His research expertise includes multifidelity modeling, reduced-order modeling, sparse grids, and high performance computing.

February 2020

Congratulations to Elizabeth Qian for having her paper Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems accepted for publication in Physica D: Nonlinear Phenomena. The paper lays the theoretical foundations for a new physics-informed machine learning method for systems governed by nonlinear partial differential equations. The Lift & Learn method uses lifting transformations, which introduce auxiliary variables to expose polynomial structure. This polynomial structure is exploited to achieve non-intrusive learning from simulation snapshot data, through the lens of projection (which preserves polynomial structure). Elizabeth's analysis of the method shows that in some settings Lift & Learn models recover the generalization accuracy of intrusive projection-based reduced models.

Congratulations to Max Opgenoord and Doug Allaire for having their paper Variance-based sensitivity analysis to support simulation-based design under uncertainty recognized as one of the top 10 most accessed articles in Journal of Mechanical Design in 2019.

Congratulations to Renee Swischuk and Boris Kramer for having their paper Learning physics-based reduced-order models for a single-injector combustion process accepted to AIAA Journal. This work is collaborative with Dr. Cheng Huang on our Air Force Center of Excellence on Multi-fidelity Modeling of Rocket Combustion Dynamics and shows the power of our non-intrusive Lift & Learn model reduction method on a challenging combustion example.

January 2020

Congratulations to Anirban Chaudhuri and Boris Kramer for having their paper Information Reuse for Importance Sampling in Reliability-Based Design Optimization accepted to Reliability Engineering and System Safety.

Congratulations to PhD student Michael Kapteyn for winning the Southwest Research Institute Student Paper Prize for his AIAA Scitech paper Toward predictive digital twins via component-based reduced-order models and interpretable machine learning.

Congratulations to Laurence Cook for his paper Design optimization using multiple dominance relations being accepted to International Journal for Numerical Methods in Engineering.

Congratulations to Remi Lam for his paper Multifidelity dimension reduction via active subspaces being accepted to SIAM Journal on Scientific Computing.

Karen, Anirban and Michael will all be at the 2020 AIAA Scitech meeting in Orlando. MIT PhD student Michael Kapteyn will present Toward predictive digital twins via component-based reduced-order models and interpretable machine learning on Monday January 6. Karen will present Learning physics-based reduced-order models for a single-injector combustion process on Wednesday January 8. MIT postdoc Anirban Chaudhuri will present Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization in the Managing Multiple Sources of Information MURI Special Session on Friday January 10.

December 2019

Congratulations to Alex Marques, Max Opgenoord, Remi Lam and Anirban Chaudhuri for their paper A multifidelity method for locating aeroelastic flutter boundaries being accepted to AIAA Journal. Great to see the collaboration among different group members bringing different aspects of multifidelity modeling together to develop a method that can produce accurate estimates of the flutter boundary at a reduced cost by combining information from low- and high-fidelity aeroelastic models.

November 2019

The video of Karen's invited talk on Predictive Data Science at ICIAM 2019 is posted here. The slides are available here.

Karen is giving an invited talk at SC19. Here are her presentation slides on Predictive Data Science, highlighting our work building an unmanned aerial vehicle and its Digital Twin.

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