כריסטיאן גרוסלר
Education
- 2017 - PhD, Engineering (Automatic Control), Lund University
- 2012 - MSc, Engineering Mathematics, Lund University
- 2011 - Diploma/MSc, Industrial Mathematics, TU Kaiserslautern
Research Interests
- Systems & Control: positive systems, model order reduction, system identification, low-complexity modelling, non-linear systems.
- Optimization: convex optimization, first order methods, sparse/low-rank optimization.
- Machine Learning: neural networks, nonnegative matrix factorization, statistics on manifolds, online learning.
Guest Appointments
- Post-doc, University of California, Berkeley, 2020-2021
- Post-doc, University of Cambridge, 2018-2020
- C. Grussler, A. Rantzer, On the Similarity of Nonnegative and Metzler Hessenberg Forms, Special Matrices, vol. 10, no. 1, pp. 1-8., 2022
- C. Grussler, R. Sepulchre, Variation diminishing linear time-invariant systems, vol. 136, pp. 109985, Automatica, 2022
- C. Grussler, T. Damm, R. Sepulchre, Balanced truncation of k-positive systems, IEEE Trans. Autom. Control, 67(1), pp. 526-531, 2022
- C. Grussler, P. Giselsson, Efficient proximal mapping computation for unitarily invariant low-rank inducing norms, J. Optim. Theory. Appl., pp. 1573-2878, 2021
- C. Grussler, A. Rantzer, On second-order cone positive systems, SIAM J. Control Optim., 59(4), 2717–2739, 2021
- C. Mostajeran, C. Grussler, R. Sepulchre, Geometric Matrix Midranges, SIAM J. Matrix Anal. Appl., 41 (3), pp. 1347–1368, 2020
- C. Grussler, P. Giselsson, Low-rank inducing norms with optimality interpretations, SIAM J. Optim., 28(4), pp. 3057–3078, 2018
- C. Grussler, A. Rantzer, P. Giselsson, Low-rank optimization with convex constraints, IEEE Trans. Autom. Control, 63(11), pp. 4000–4007, 2018
Control, Dynamical Systems
