I am an applied mathematician developing computational methods for machine learning and inverse problems. I am a Winship Distinguished Research Associate Professor in the Department of Mathematics and the Department of Computer Science at Emory University and a member of Emory’s Scientific Computing Group. I lead the Emory REU/RET site for Computational Mathematics for Data Science. Prior to joining Emory, I was a postdoc at the University of British Columbia and I held PhD positions at the University of Lübeck and the University of Münster.
I received an NSF CAREER award and am also supported by grants from the US Department of Energy’s Advanced Scientific Computing Research program, the Air Force Office of Scientific Research, and contracts by Sandia National Laboratories. Previously, I was supported by the US Israeli Binational Science Foundation, and the Centers for Disease Control.
PhD in Mathematics, 2012
University of Münster, Germany
Diploma in Mathematics, 2010
University of Münster, Germany
My research and teaching sit at the intersection of applied mathematics and data science, particularly deep learning and inverse problems.
In deep learning, I seek to create new insights and efficient training for continuous models based on ordinary and partial differential equations. I also develop machine learning approaches for solving high-dimensional partial differential equations and optimal control problems and am interested in connections to active and reinforcement learning.
In inverse problems, I have been working on using generative modeling for inference. Over the years, I have been working on applications in image registration and reconstruction and computational techniques including optimal experimental design, uncertainty quantification, numerical optimization, multiscale and multigrid methods, and regularization. Over the years, I had many fruitful collaborations with domain-experts from public health, geophysics, and medical imaging.
I offer regular one-semester courses in applied mathematics as well as advanced courses, seminars, and workshops in my research area. As part of my NSF CAREER project, I have also developed a new graduate-level course on Numerical Methods for Deep Learning. I offered this class twice at Emory and gave short versions of the class at TU Berlin, University of Chemnitz, and Scuola Normale Superiore di Pisa. I also taught a short short course on Deep Generative Modeling as part of the Spring School on Data and Models at the University of South Carolina.
In the academic year 2023-24, I am offering our Honors Linear Algebra and Vector Calculus sequence. With these courses I seek to fast track some of our freshmen with the strongest mathematics background for upper level courses and research.
Vice-Chair, SIAM Activity Group on Data Science, since January 2022
Section Editor for Machine Learning Methods for Scientific Computing for SIAM Journal on Scientific Computing (SISC)
Associate editor, SIAM Review (SIREV) Research Spotlight Section
Program Committee Member, SIAM Conference on Uncertainty Quantification, February 27 - March 1, 2024 in Trieste, Italy
Organizing Committee Co-Chair, SIAM Conference on Mathematics of Data Science (MDS22), September 26-30, 2022 in San Diego, CA, USA.
Chair of Selection Committee, SIAM Activity Group on Supercomputing Career Prize 2022
Member of Selection Committee, SIAM Activity Group on Linear Algebra Early Career Prize 2021
Program Committee, Conference on Mathematical and Scientific Machine Learning (MSML), 2019-21
Senior Fellow, IPAM Long program on Machine Learning for Physics and the Physics of Learning, Fall 2019.
Secretary, SIAM Activity Group on Imaging Science, 2018-19
Senior Consultant, xtract.ai Technologies Inc., 2017-19