Julie Nutini

julie.nutini@gmail.com
 Vancouver, BC, Canada

 PhD, Computer Science, University of British Columbia
 MSc, Mathematics, University of British Columbia (Okanagan)
 BSc, Mathematics, University of British Columbia (Okanagan)


I was born and raised in the small mountain town of Rossland, British Columbia, located in the West Kootenays. I graduated from the University of British Columbia in 2018 with a PhD in Computer Science, supervised by Mark Schmidt. My PhD thesis focused on optimization methods for large scale structured problems. I am currently a Geospatial Scientist - SAR Specialist at Planet working on using machine learning with complementary sensor datasets (e.g., synthetic aperture radar (SAR), optical and auxiliary data) to improve optical fusion products for agricultural applications. I have also worked on developing numerical optimization methods for problems such as satellite model refinement and radiometric/geometric corrections of optical remote sensing data.

Curriculum Vitae

Publications:

Y. Sun, H. Jeong, J. Nutini and M. Schmidt. ``Are we there yet? Manifold identification of gradient related proximal methods", AISTATS, 2019 [pdf] [poster].

J. Nutini, M. Schmidt and W. Hare. "Active-set complexity" of proximal gradient: How long does it take to find the sparsity pattern?, Optimization Letters, 2018 [pdf] [poster].

J. Nutini, I. Laradji, M. Schmidt and W. Hare. Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence, submitted for publication, 2017 [pdf][slides][poster][code].

I. Laradji, J. Nutini and M. Schmidt. Graphical Newton for Huge-Block Coordinate Descent on Sparse Graphs, NeurIPS Optimization Workshop, 2017 [pdf] [poster].

H. Karimi, J. Nutini and M. Schmidt. Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Condition, ECML-PKDD, 2016 [pdf] [slides] [poster].

J. Nutini, B. Sepehry, I. H. Laradji, M. Schmidt, H. Koepke and A. Virani. Convergence Rates for Greedy Kaczmarz Algorithms, and Faster Randomized Kaczmarz Rules Using the Orthogonality Graph, UAI, 2016 [pdf] [poster] [code].

*K. Bigdeli, W. Hare, J. Nutini and S. Tesfamariam. Optimizing Damper Connectors for Adjacent Buildings, Optimization and Engineering, 17(1):47-75, 2016 [pdf].

J. Nutini, M. Schmidt, I. H. Laradji, M. Friedlander and H. Koepke. Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection, ICML, 2015 [pdf] [slides] [poster] [video talk].

*W. Hare, J. Nutini and S. Tesfamariam. A survey of non-gradient optimization methods in structural engineering, Advances in Engineering Software, 59:19-28, 2013 [pdf].

*W. Hare and J. Nutini. A derivative-free approximate gradient sampling algorithm for finite minimax problems, Computational Optimization and Applications, 56(1):1-38, 2013 [pdf] [slides].

* authors listed in alphabetical order