Uwe Naumann

Contact Details:

  • Phone: +49 (0)241/80 28 920
  • Mail: naumann@stce.rwth-aachen.de
  • Office:  Room 230, Seffenter Weg 23

Short CV

  • Born on June 24, 1969 in Rodewisch, Germany
  • Education
    • [02/99] Dr. rer. nat. (equiv. Ph.D., Mathematics), TU Dresden, Germany
    • [05/96] Diplom (equiv. M.Sc., Mathematics), TU Dresden, Germany
    • [07/88] Abitur (equiv. A-levels), EOS Friedrich Engels, Chemnitz, Germany
  • Employment
    • [04/17-07/17] Visiting Lecturer, Mathematical Institute, University of Oxford, UK
    • [since 04/14] Director of the Steinbeis Consulting Center ``Simulation Software Analysis, Transformation, and Optimization'', Aachen, Germany
    • [since 10/08] Member and Technical Consultant of the Numerical Algorithms Group Ltd., Oxford, UK
    • [since 07/04] Professor of Computer Science, RWTH Aachen, Germany
    • [since 11/01] Visiting Researcher, Department of Computer Science, University of Hertfordshire, UK
    • [01/02-06/04] Postdoc / Assistant Computer Scientist, Mathematics and Computer Science Division, Argonne National Laboratory, USA
    • [01/00-09/01] Senior Lecturer, Department of Computer Science, University of Hertfordshire, UK
    • [01/99-12/99] Postdoc, INRIA Sophia-Antipolis, France
    • [05/96-12/98] Ph.D. Student, Institute for Scientific Computing, TU Dresden, Germany

Selected Journal Publications

  • F. Gremse, A. Höfter, L. Razik, F. Kiessling, U. N.: GPU-accelerated adjoint algorithmic differentiation, Computer physics communications 200:300-311, 2016.
  • C. Villaret, R. Kopmann, D. Wyncoll, J. Riehme, U. Merkel, U.N.: First-order uncertainty analysis using Algorithmic Differentiation of morphodynamic models, Computers & Geoscience 90:144-151, 2016
  • U.N. and J. du Toit: Adjoint Algorithmic Differentiation Tool Support for Typical Numerical Patterns in Computational Finance. To appear in Journal of Computational Finance.
  • U.N., J. Lotz, K. Leppkes, M. Towara: Algorithmic Differentiation of Numerical Methods: First-Order Tangents and Adjoints for Solvers of Systems of Nonlinear Equations. ACM TOMS, 41(4):26:1-26:21.
  • F. Gremse, A. Höfter, L.O. Schwen, F. Kiessling, U. N.: GPU-accelerated sparse matrix-matrix multiplication by iterative row merging, SIAM Journal on Scientific Computing 37(1):C54-C71.
  • D.S. Nikolopoulos, H. Vandierendonck, N. Bellas, C.D. Antonopoulos, S. Lalis, G. Karakonstantis, A. Burg, and U.N.: Energy Efficiency through Significance-Based Computing. Computer, 47(7):82--85,IEEE, 2014.
  • A. Vlasenko and P. Korn and J. Riehme and U.N.: Estimation of Data Assimilation Error: A Shallow-Water Model Study. Monthly Weather Review, 142:2502-2520, 2014
  • V. Mosenkis and U.N.: On optimality preserving eliminations for the minimum edge count and optimal Jacobian accumulation problems in linearized DAGs.Optim. Meth. Softw.27(2):337-358, 2012.
  • U.N.: DAG Reversal is NP-complete. J. Discr. Alg., 7:402-410, 2009.
  • J. Utke, U. N., C. Wunsch, C. Hill, P. Heimbach, M. Fagan, N. Tallent, and M. Strout: OpenAD/F: A modular, open-source tool for automatic differentiation of Fortran codes. ACM Trans. Math. Softw., 34(4):18:1-18:36, 2008.
  • U.N.: Optimal Jacobian accumulation is NP-complete. Math. Prog., 112:427-441, 2006.
  • U.N. and J. Riehme: A differentiation-enabled Fortran 95 compiler. ACM Trans. Math. Softw., 31(4)458-474, 2005.
  • U.N.: Optimal accumulation of Jacobians by elimination methods on the dual computational graph. Math. Prog., 3(99):399-421, 2004.

Selected Conference Publications

  • J. Lotz, M. Schwalbach, U. N.: A Case Study in Adjoint Sensitivity Analysis of Parameter Calibration, Procedia Computer Science, 80:201-211, Elsevier, 2016.
  • V. Vassiliadis, J. Riehme, J. Deussen, K. Parasyris, C. Antonopoulos, N. Bellas, S. Lalis, U.N.: Towards automatic significance analysis for approximate computing, IEEE/ACM International Symposium on Code Generation and Optimization (CGO), 182-193, 2016.
  • M. Towara, M. Schanen, U. N.: MPI-parallel discrete adjoint OpenFoam, Procedia Computer Science, 51:19-28, Elsevier, 2015.
  • A. Sen, M. Towara, and U. N.: A Discrete Adjoint Version of an Unsteady Incompressible Solver for OpenFOAM Using Algorithmic Differentiation.6th European Conference on Computational Fluid Dynamics, 5014-5023, ECCOMAS, 2014.
  • J. Lotz, U.N., M. Sagebaum, and M. Schanen: Discrete Adjoints of PETSc through dco/c++ and Adjoint MPI. Euro-Par 2013 Parallel Processing, 497-507,Springer, 2013.
  • M. Towara and U.N.: A Discrete Adjoint Model for OpenFOAM. Procedia Computer Science, 18:429--438, Elsevier, 2013.
  • F. Rauser, J. Riehme, K. Leppkes, P. Korn, and U.N.: On the use of discrete adjoints in goal error estimation for shallow water equations. International Conference of Computational Science (ICCS 2010), pages 107-115, Amsterdam, 2010.
  • R. Hannemann, W. Marquardt, U.N., and B. Gendler: Discrete first- and second-order adjoints and automatic differentiation for the sensitivity analysis of dynamic models International Conference of Computational Science (ICCS 2010), pages 297-305, Amsterdam, 2010.
  • J. Utke, L. Hascoet, C. Hill, P. Hovland, and U.N.: Toward Adjoinable MPI. IEEE International Parallel & Distributed Processing Symposium (IPDPS 2009),pages 1-8, Rome, 2009.
  • U.N., J. Utke, J. Riehme, P. Hovland, and C. Hill: A framework for proving correctness of adjoint message passing programs. European PVM/MPI Users’ Group conference (Euro PVM/MPI 2009), pages 316-321, Rome, 2009.
  • U.N.: Call Tree Reversal is NP-complete. International Conference on Automatic Differentiation (AD 2008) pages 13-22 in LNCSE 64 (Springer), Bonn, 2008.

Selected Books

  • U.N.: The Art of Differentiating Computer Programs. SIAM, 2012. [siam] [amazon]
  • U.N. and O. Schenk (eds.):
    Combinatorial Scientific Computing. Computational Science Series , CRC Press, 2012. [crc press] [amazon]
  • C. Bischof, M. Bücker, P. Hovland, U.N., and J. Utke (eds.):
    Advances in Automatic Differentiation. LNCSE 64, Springer, 2008. [springer] [amazon]
  • M. Bücker, G. Corliss, P. Hovland, U.N., and B. Norris (eds.): Automatic Differentiation: Applications, Theory, and Tools. LNCSE 50, Springer, 2006.[springer] [amazon]
  • G. Corliss, C. Faure, A. Griewank, L. Hascoet, and U.N. (eds.): Automatic Differentiation of Algorithms: From Simulation to Optimization. Springer, 2002. [springer] [amazon]


Google Scholar profile

Selected Third-Party Funded Research Projects

  • Toward Adjoint-Based Large-Scale Parameter Sensitivity Analysis and Uncertainty Quantification in Predictive Simulations and Hierarchical Modeling, RWTH EXC Seed Fund, 2016-1017.
  • Aachen Dynamic Optimization Environment, DFG NA487/8-1, 2016-2018.
  • Incremental multi-scale and multi-disciplinary Modeling of Processes in Bioeconomy, BioSC Boost Fund, 2014-2017.
  • Adjoint-based Optimization of Industrial and Unsteady Flows, EU ITN 317006, 2012-2016.
  • Significance-based Computing for Reliability ad Power Optiimization, EU FETOpen 323872, 2013-2016.
  • A Hybrid Approach to Adjoint C++ Code, DFG NA487/4-1, 2010-2013.
  • The Aachen Platform for Structured Automatic Manipulation of Mathematical Models, RWTH Aachen Exploratory Research Space, 2009-2011.
  • Combinatorial Aspects in Algorithmic Differentiation, DFG NA 487/2-1, 2008-2011.
  • Differentiation-Enabled Compiler Technology, EPSRC GR/R/55252/01, EPSRC EP/D062071/1, EPSRC EP/F069383, 2002-2010.
  • Adjoint Compiler Technology and Standards, NSF-ITR OCE-0205590, 2002-2005.

Selected Administrative Activities

  • Director of Mentoring Program, Dept. of Computer Science
  • Head of Kommission für Servicelehre, Dept. of Computer Science
  • Member of Unterkommission für Lehre, BSc Computational Engineering Science
  • Member of Prüfungskommission, BSc Computational Engineering Science