Simon Scheidegger

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Felix KublerUniversity of Zurich and Swiss Finance InstituteVerified email at bf.uzh.ch
Roger KäppeliETH ZurichVerified email at sam.math.ethz.ch
Olaf SchenkProfessor, Institute of Computing, Universita della Svizzera italiana, SIAM FellowVerified email at usi.ch
Laurence KotlikoffBoston UniversityVerified email at bu.edu
Tobias FischerUniversity of WroclawVerified email at uwr.edu.pl
Johannes BrummKarlsruhe Insitute of TechnologyVerified email at kit.edu
Polbin AndreyRussian Presidential Academy of National Economy and Public AdministrationVerified email at ranepa.ru
Aryan EftekhariUniversity of LausanneVerified email at unil.ch
Matthias BollhöferProfessor for Numerical Analysis, TU BraunschweigVerified email at tu-bs.de
Friedrich-Karl ThielemannProf. of theor. physics em., Univ. of Basel and guest scientist GSI DarmstadtVerified email at unibas.ch
Dmitry MikushinUniversity of LausanneVerified email at kernelgen.org
Ilias BilionisAssociate Professor, Mechanical Engineering, Purdue UniversityVerified email at purdue.edu
Philipp RennerSenior Lecturer, Department of Economics, Lancaster UniversityVerified email at lancaster.ac.uk
Ue-Li PenUniversity of Toronto, Canadian Institute of Theoretical AstrophysicsVerified email at cita.utoronto.ca
Anthony MezzacappaNewton W. and Wilma C. Thomas Endowed Chair Professor, Department of Physics and AstronomyVerified email at utk.edu
Irina SagertLos Alamos National LaboratoryVerified email at lanl.gov
Hui ChenProfessor of Finance, MITVerified email at mit.edu
Gabriel Martínez PinedoInstitut für Kernphysik (Theoriezentrum), Technische Universität DarmstadtVerified email at physik.tu-darmstadt.de
William Raphael HixOak Ridge National LaboratoryVerified email at utk.edu
Christopher KrauseKarlsruhe Institute of TechnologyVerified email at kit.edu

Simon Scheidegger

Department of Finance, Faculty of Business and Economics (HEC Lausanne), University of Lausanne

Verified email at unil.ch - Homepage

Computational economics computational finance high-performance computing machine learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Using adaptive sparse grids to solve high‐dimensional dynamic models J Brumm, S Scheidegger Econometrica 85 (5), 1575-1612, 2017	148	2017
The influence of model parameters on the prediction of gravitational wave signals from stellar core collapse S Scheidegger, R Käppeli, SC Whitehouse, T Fischer, M Liebendörfer Astronomy & Astrophysics 514, A51, 2010	107	2010
Gravitational waves from 3D MHD core collapse simulations S Scheidegger, T Fischer, S Whitehouse, L Matthias Astronomy & Astrophysics, 0	80*
Machine learning for high-dimensional dynamic stochastic economies S Scheidegger, I Bilionis Journal of Computational Science 33, 68-82, 2019	56	2019
Large-scale sparse inverse covariance matrix estimation M Bollhöfer, A Eftekhari, S Scheidegger, O Schenk SIAM Journal on Scientific Computing 41 (1), A380-A401, 2019	54	2019
Deep Equilibrium Nets M Azinovic, L Gaegauf, S Scheidegger https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3393482, 0	52*
Making Carbon Taxation a Generational Win Win LJ Kotlikoff, F Kubler, A Polbin, JD Sachs, S Scheidegger NBER Working Paper No. 25760, 2019	44	2019
FISH: a three-dimensional parallel magnetohydrodynamics code for astrophysical applications R Käppeli, SC Whitehouse, S Scheidegger, UL Pen, M Liebendörfer The Astrophysical Journal Supplement Series 195 (2), 20, 2011	39	2011
Gravitational waves from supernova matter S Scheidegger, SC Whitehouse, R Käppeli, M Liebendörfer Classical and Quantum Gravity 27 (11), 114101, 2010	35	2010
Scalable high-dimensional dynamic stochastic economic modeling J Brumm, D Mikushin, S Scheidegger, O Schenk Journal of Computational Science 11, 12-25, 2015	19	2015
Computing equilibria in dynamic stochastic macro-models with heterogeneous agents J Brumm, F Kubler, S Scheidegger Advances in economics and econometrics 2, 185, 2017	17	2017
Can today's and tomorrow's world uniformly gain from carbon taxation? LJ Kotlikoff, F Kubler, A Polbin, S Scheidegger National Bureau of Economic Research, 2021	13	2021
Machine Learning for Dynamic Incentive Problems P Renner, S Scheidegger https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3282487, 2018	11	2018
Rethinking large-scale economic modeling for efficiency: Optimizations for gpu and xeon phi clusters S Scheidegger, D Mikushin, F Kubler, O Schenk 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2018	11	2018
Pareto-improving carbon-risk taxation L Kotlikoff, F Kubler, A Polbin, S Scheidegger Economic Policy 36 (107), 551-589, 2021	10	2021
Deep structural estimation: With an application to option pricing H Chen, A Didisheim, S Scheidegger arXiv preprint arXiv:2102.09209, 2021	9	2021
Self-justified equilibria: Existence and computation F Kubler, S Scheidegger Available at SSRN 3494876, 2019	9	2019
Parallelized dimensional decomposition for large-scale dynamic stochastic economic models A Eftekhari, S Scheidegger, O Schenk Proceedings of the Platform for Advanced Scientific Computing Conference, 1-11, 2017	9	2017
The climate in climate economics D Folini, F Kübler, A Malova, S Scheidegger arXiv preprint arXiv:2107.06162, 2021	8	2021
Pricing American Options under High-Dimensional Models with Recursive Adaptive Sparse Expectations S Scheidegger, A Treccani Journal of Financial Econometrics, 2018	8	2018

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