Talks and Presentations

  1. August 2024: Bayesian Inversion for Semiconductor Inverse Problems. The 31st IFIP TC7 Conference on  System Modelling and Optimization, August 12-16, 2024, Universität Hamburg, Germany.
  2. June 2024: Nanotechnology PDE models under uncertainty.  Talk at Seminar Series on Computational Engineering, June 4, 2024, LUT University, Finland (online).
  3. February 2024: A pCN-MCMC Method for a Bayesian Inverse Problem in Nanoscale Devices, Presentation at SIAM Conference on Uncertainty Quantification (SIAM UQ24), 27 February-1 March 2024, Trieste, Italy.
  4. March 2023: Uncertainty Quantification in Computational Science and Engineering. Talk at TU Wien, March 28, 2023, Vienna, Austria.
  5. November 2022: Uncertainty Quantification for Nanoelectronics. Talk at Eindhoven University of Technology, Eindhoven, The Netherlands.
  6. April 2022: Bayesian Approach for Inverse Problems in Tomographic Imaging. Presentation at SIAM Conference on Uncertainty Quantification (SIAM UQ22), April 12-15, 2022, Atlanta, Georgia, USA.
  7. December 2021: Uncertainty Quantification and Applications in Nanoelectronics and Electrical Impedance Tomography. Talk at TU Munich, Munich, Germany.
  8. November 2021: Uncertainty Quantification of PDE Models with Applications in Computational Science and Engineering. Talk at TU Darmstadt, Darmstadt, Germany.
  9. May 2021: Computational Uncertainty Quantification with Applications in Engineering.Talk at the University of Heidelberg, Heidelberg, Germany.
  10. November 2019: Progress in electrical-impedance tomography. Presentation at Vienna Center for Engineering and Medicine (ViCEM) Biennial Meeting, Medical University of Vienna, Vienna, Austria, 14-15 November 2019.
  11. July 2019: Bayesian inference for two inverse problems in tomography and biofilms. Presentation at International Conference on Scientific Computation and Differential Equations 2019 (SciCADE 2019), Innsbruck, Austria, 22-26 July 2019.
  12. February 2019: Bayesian analysis for Poisson-Boltzmann equation modeling electrical impedance tomography devices. Presentation at 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Vienna, Austria, 18-20 February 2019.
  13. April 2016: Optimal method for calculating solutions of the stochastic drift-diffusion-Poisson system. Presentation at SIAM Conference on Uncertainty Quantification (SIAM UQ16), April 5-8, 2016, Lausanne, Switzerland.
  14. December 2015: Optimal Multi-level Monte-Carlo method for a system of stochastic PDEs. Presentation at SIAM Conference on Analysis of Partial Differential Equations (SIAM PD15), Scottsdale AZ, USA, 7-10 December 2015.
  15. December 2015: Optimal Approach for the Numerical Stochastic Homogenization of Elliptic Problems. Presentation at SIAM Conference on Analysis of Partial Differential Equations (SIAM PD15), Scottsdale AZ, USA, 7-10 December 2015.
  16. September 2015: Existence and uniqueness for the Stokes-Nernst-Planck-drift-diffusion-Poisson system for modeling nanowire sensors and nanopores. Presentation at the 24th International Conference on Transport Theory (ICTT 2015), Taormina, Italy, 7-11 September 2015.
  17. September 2015: The stochastic drift-diffusion-Poisson system for modeling nanoscale devices and a multi-level Monte-Carlo method. Presentation at the 24th International Conference on Transport Theory (ICTT 2015), Taormina, Italy, 7-11 September 2015.
  18. September 2015: The stochastic drift-diffusion-Poisson system for modeling nanoscale devices and a multi-level Monte-Carlo method. Presentation at TU Wien, Vienna, Austria.
  19. April 2015: Multilevel Monte-Carlo finite element method for stochastic elliptic PDEs. Presentation at TU Wien, Vienna, Austria.
  20. June 2014: Time and space adaptive numerical integration of nonlinear evolution equations. Presentation at the Workshop: Advances in Nonlinear PDEs: Analysis, Numerics, Stochastics, Applications, June 2-3, 2014, Vienna, Austria.
  21. June 2013: Solving semi-classical Schrödinger equations using Hagedorn wavepackets. Presentation at TU Wien, Vienna, Austria.

Contributed Talks and Conference Proceedings

  1. Ahmad Karimi, Leila Taghizadeh, and Clemens Heitzinger. Optimal Bayesian experimental design for EIT inverse problems. In Proc. SIAM Conference on Computational Science and Engineering (CSE 2021), Texas, USA, 1-5 March 2021.
  2. Clemens Heitzinger, Daniel Pasterk, and Leila Taghizadeh. Computational Bayesian inversion for nanocapacitor-array biosensors and electrical-impedance tomography. In Proc. SIAM Conference on Uncertainty Quantification (UQ 2020), Munich, Germany, 24-27 March 2020. Conference canceled due to coronavirus pandemic.
  3. Ervin K. Lenzi, Luiz R. Evangelista, Leila Taghizadeh, Daniel Pasterk, Rafael S. Zola, Trifce Sandev, Clemens Heitzinger, and Irina Petreska. Interpreting impedance spectroscopy data by using Poisson-Nernst-Planck anomalous models. In Proc. DPG (Deutsche physikalische Gesellschaft) Spring Meeting of the Condensed Matter Section, page DY 56.5, Dresden, Germany, 15-20 March 2020. Conference canceled due to coronavirus pandemic.
  4. Andrea Cossettini, Benjamin Stadlbauer, Jose A. Morales Escalante, Leila Taghizadeh, Luca Selmi, and Clemens Heitzinger. Determination of micro- and nano-particle properties by multi-frequency Bayesian methods and applications to nanoelectrode array sensors. In Proc. IEEE Sensors 2019, page 1--4, Montreal, Canada, 27-30 October 2019.
  5. Clemens Heitzinger, Amirreza Khodadadian, Daniel Pasterk, and Leila Taghizadeh. Modeling and simulation of nanotechnological sensors. In Proc. 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), page 309, Valencia, Spain, 15-19 July 2019.
  6. Clemens Heitzinger and Leila Taghizadeh. Bayesian PDE inversion in electrical-impedance tomography. In Proc. 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019), page 352, Valencia, Spain, 15-19 July 2019.
  7. Clemens Heitzinger, Jose Morales Escalante, Benjamin Stadlbauer, and Leila Taghizadeh. Bayesian estimation and machine learning: current problems and challenges with examples in electrical impedance tomography and nanosensors. In Proc. Workshop on Research Challenges and Opportunities at the interface of Machine Learning and Uncertainty Quantification, page online, University of Southern California, Los Angeles, CA, USA, 4-6 June 2018.
  8. Benjamin Stadlbauer, Leila Taghizadeh, Jose Morales Escalante, Clemens Heitzinger, Andrea Cossettini, and Luca Selmi. Bayesian estimation for transport equations for nanocapacitors. In Proc. SIAM Conference on Uncertainty Quantification (UQ 2018), pages 69-70, Garden Grove, CA, USA, 16-19 April 2018.
  9. Andrea Cossettini, Paolo Scarbolo, Jose Morales Escalante, Benjamin Stadlbauer, Naseer Muhammad, Leila Taghizadeh, Clemens Heitzinger and Luca Selmi. Calibration, compensation, parameter estimation, and uncertainty quantification for nanoelectrode array biosensors. In Proc. SIAM Conference on Uncertainty Quantification (UQ 2018), page 81, Garden Grove, CA, USA, 16-19 April 2018.
  10. Leila Taghizadeh, Jose Morales Escalante, Benjamin Stadlbauer, and Clemens Heitzinger. Bayesian estimation for a tomography problem. In Proc. SIAM Conference on Uncertainty Quantification (UQ 2018), page 138, Garden Grove, CA, USA, 16-19 April 2018.
  11. Leila Taghizadeh, Amirreza Khodadadian, and Clemens Heitzinger. Optimal multi-level randomized-quasi-Monte-Carlo methods for the stochastic drift-diffusion-Poisson system and for stochastic homogenization. In Proc. International Conference on Scientific Computation and Differential Equations (SciCADE 2017), page 129, Bath, UK, 11-15 Sep 2017.
  12. Leila Taghizadeh, Amirreza Khodadadian, Stefan Rigger, and Clemens Heitzinger. Optimal multi-level Monte-Carlo methods for the stochastic drift-diffusion-Poisson system and for stochastic homogenization. In Proc. 14th US National Congress on Computational Mechanics (USNCCM), page 321, Montreal, Canada, 17-20 July 2017.
  13. Amirreza Khodadadian, Leila Taghizadeh, and Clemens Heitzinger. Optimal multi-level Monte Carlo method for the stochastic drift-diffusion-Poisson system. In Proc. 13th Austrian Numerical Analysis Day 2017, page 16, Salzburg, Austria, 4-5 May 2017.
  14. Clemens Heitzinger, Amirreza Khodadadian, Gudmund Pammer, Stefan Rigger, and Leila Taghizadeh. Optimal numerical methods for stochastic PDEs. In Proc. SIAM Conference on Computational Science and Engineering (CSE 2017), page 129, Atlanta, GA, USA, 27 February – 3 March 2017.
  15. Leila Taghizadeh, Amirreza Khodadadian, and Clemens Heitzinger. The stochastic drift-diffusion-Poisson system for modeling nanowire and nanopore sensors. Talk at ECMI 2016.
  16. Clemens Heitzinger, Caroline Geiersbach, and Leila TaghizadehOptimal numerical approaches to the stochastic homogenization of elliptic equations and to the stochastic drift-diffusion-Poisson system. In Proc. SIAM Conference on Mathematical Aspects of Materials Science (MS16), page 136, Philadelpha, PA, USA, 8-12 May 2016.
  17. Leila Taghizadeh, Amirreza Khodadadian, and Clemens Heitzinger. Stochastic modeling of dopant atoms in nanoscale transistors using multi-level Monte Carlo. In Proc. SIAM Conference on Uncertainty Quantification 2016 (SIAM UQ16), page 9, Lausanne, Switzerland, 5-8 April 2016.