I am an Associate Professor at the Department of Automatic Control. My main research interests lie within optimization and its wide range of applications.
I have an open PhD position in the intersection of optimization, learning, and control with an emphasis on the former two. Application deadline is October 15.
P. Giselsson, Nonlinear Forward-Backward Splitting with Projection Correction. SIAM Journal on Optimization. Accepted for Publication. 2021.
M. Morin and P. Giselsson, SVAG: Stochastic Variance Adjusted Gradient Descent and Biased Stochastic Gradients. Submitted. 2019.
E. Ryu, A. Taylor, C. Bergeling, and P. Giselsson, Operator Splitting Performance Estimation: Tight contraction factors and optimal parameter selection. SIAM Journal on Optimization, 30(3):2251 - 2271, 2020.
C. Grussler and P. Giselsson, Low-Rank Inducing Norms with Optimality Interpretations. SIAM Journal on Optimization, 28(4):3057 - 3078, 2018.
M. Fält and P. Giselsson, Optimal Convergence Rates for Generalized Alternating Projections. In Proceedings of the 56th Conference on Decision and Control, Melbourne, Australia, 2017.
P. Giselsson, Tight Global Linear Convergence Rate Bounds for Douglas-Rachford Splitting. Journal of Fixed-Point Theory and Applications, 2017. doi:10.1007/s11784-017-0417-1.
C. Grussler, A. Rantzer, and P. Giselsson, Low-Rank Optimization with Convex Constraints. IEEE Transactions on Automatic Control, 63(11):4000 - 4007, 2018.
P. Giselsson and S. Boyd, Linear Convergence and Metric Selection in Douglas Rachford Splitting and ADMM. Transactions of Automatic Control. 62(2):532 - 544, 2017.
P. Giselsson, M. Fält, and S. Boyd, Line Search for Averaged Operator Iteration. In Proceedings of the 55th Conference on Decision and Control, Las Vegas, USA, 2016.
P. Giselsson and S. Boyd, Metric Selection in Fast Dual Forward Backward Splitting. Automatica, 62:1-10, 2015.
Master level course on Optimization for Learning. Held Sept.-Oct. every year.
PhD level course on Large-Scale Convex Optimization. Last held 2015.