Tutorials (selected)

• Computational Information Games, a minitutorial. ICERM June 5-6, 2017. [ Part I | Part II | Part III ] [ paper ] • On the worst case approach to UQ, a minitutorial. SIAM UQ 2016. [ slides ]

Presentations (selected)

• On learning adapted kernels for numerical approximation and learning March 25, 2021 (also covers solving/learning nonlinear PDEs with GPs) [ video | slides ] • On learning adapted kernels for numerical approximation. One World Numerical Analysis Series. Nov 2, 2020 [ video | slides ] • Do ideas have shape? Plato’s theory of forms as the continuous limit of artificial neural networks. Fields Institute, Second Symposium on Machine Learning and Dynamical Systems, Fields Institute, Sep ,2020. [ video | slides | paper ] • Kernel Mode Decomposition and programmable/interpretable regression networks Oberwolfach, July 2019 [slides] • Kernel flows, from learning kernels from the data into the abyss o Crete, June 24, 2019 [slides] (ppt file, inlcudes videos/animations) o AFOSR, August 15, 2018 [slides] (pdf file, does not include videos/animations, older version) • On the interface between Numerical Approximation, Inference and Learning (Glorified linear interpolation) Oberwolfach, March 2019 [ppt slides] (with videos/animations) [pdf slides] (without videos/animations) • Multigrid/Multiresolution with rough coefficients from hierarchical information games. SIAM CSE 15 plenary talk, 2015. [ video | slides | paper ] • Bayesian Numerical Homogenization. Berkeley 2014. [ pdf ] • Bayesian Brittleness. Shanghai 2014. [ pdf ] • A calculus for the optimal quantification of uncertainties Kavli Royal Society 2014. (also covers the connection between the truncated moment and Selberg identities and the identification of new reproducing RKHS and Selberg type formulas) [ pdf ] • Can discovery be computed? AFOSR Arlington July 2014 [ pdf ] • Optimal Uncertainty Quantification (not too technical, covers the adversarial approach to UQ, the brittleness of inference and the game theoretic approach to modeling). Caltech November 2013. [ pdf ] • Optimal Uncertainty Quantification. LLNL 2012. [ pdf ] • Structure preserving homogenization/integration of stiff (possibly stochastic) Hamiltonian systems. Paris 2010 (a short course at IHP, covers Flow Averaging Integrators) [ pdf ] • Homogenization with non-separated scales. Bengalore 2010 (a short course at IIT, also covers discrete structures in homogenization and inverse homogenization) [ pdf ] [ ppt (with embedded videos) ] • Averaging vs Chaos in Turbulence? Caltech 2003. [ slides | paper ]