Jana de Wiljes University Potsdam

Jana de Wiljes University Potsdam

Institute for Mathematics

University of Potsdam

Campus Golm, house 9, room 1.26

Karl-Liebknecht-Str. 24-25

14476 Potsdam-Golm

Phone: +49 331 977-1685

http://www.sfb1294.de

About me

I did my doctoral studies as a fellow in the Helmholtz-Kolleg GEOSIM in the field of machine learning with a geophysical application. I received my PhD from Free University of Berlin and was awarded the Tibertius prize (first prize for outstanding PhD across all disciplines and Universities in Berlin and Brandenburg) for my work.

Currently I am a researcher fellow at the University of  Potsdam and work on the development of computational methods for Bayesian inverse problems and derivation of mathematical principles in particular in the context of nonlinear filtering. Since june 2017 I am a PI as well as a member of the steering committee and the gender and diversity board of the Collaborative Research Centre 1294 on Data Assimilation.

Further I am PI in the GEO.X Young Academy and the Germany PI of a DAAD Programme to collaborate with members of the Centre of Excellence in Inverse Modelling and Imaging Network in Finnland .

Moreover, I am the organizer of the Golm Coder Dojo.

Recent and Upcoming Talks

05/2022    Workshop on Connections between Interacting Particle Dynamics and Data Science, Scotland

03/2021    SIAM Conference on Computational Science and Engineering, Fort Worth, Texas, USA

06/2020    One World Seminar, Zoom Talk

06/2020    Foundations of Computational Mathematics (FoCM) conference, Vancouver, Canada (cancelled)

06/2020    Conference on Interacting Particle Systems, Oxford, UK (cancelled)

05/2020    Sequential Monte Carlo (SMC) workshop, Madrid, Spain (postponed)

04/2020    MFO Workshop on Data Assimilation (postponed)

02/2020    CRiSM Seminar at University of Warwick, UK

02/2020    SQUIDS Seminar at University of Manchester, UK

11/2019    Workshop Big Data, Data Assimilation and Uncertainty Quantification, Paris, France

09/2019    Dynamics Equations and Applications (DEA), Krakow, Poland

In preparation

1. 1.C. Maier, J. de Wiljes, N. Hartung, C. Kloft, W. Huisinga (2020) A continuous learning approach to model-informed precision dosing: accounting for to model bias to meet clinical reality. in preparation.
   

Preprints

2. 2.A. M. Castillo Tibocha, J. de Wiljes, Y. Shprits, N. A. Aseev (2020) Reconstructing the dynamics of the outer electron radiation belt by means of ensemble Kalman filtering with the VERB-3D Code. under review in Space Weather.
   

Publications

3. 3.E. Saggioro, J. de Wiljes, M. Kretschmer, J. Runge (2020). Reconstructing regime-dependent causal relationships from observational time series. Chaos 30:113115-1-113115-22.
   

4. 4.S. Ruchi, S. Dubinkina, J. de Wiljes (2020). Fast hybrid tempered ensemble transform filter for Bayesian elliptical problems. accepted in Nonlinear Processes in Geophysics.
   

5. 5.C. Maier, N. Hartung, C. Kloft, W. Huisinga, J. de Wiljes (2020). Combining reinforcement learning with data assimilation for individualised dosing policies in oncology. accepted in CPT: Pharmacometrics & Systems Pharmacology.
   

6. 6.M. Hamm, I. Pelivan, M. Grott, J. de Wiljes (2020). Thermophysical modeling and parameter estimation of small solarsystem bodies via data assimilation Monthly Notices of the Royal Astronomical Society, 496(3): 2776-2785.
   

7. 7.S. K. J. Falkena, J. de Wiljes, A. Weisheimer, T. G. Shepherd (2020). Revisiting the Identification of Wintertime Atmospheric Circulation Regimes in the Euro-Atlantic Sector.  Quarterly Journal of Royal Meteorological Society, 496(3): 2776-2785.
   

8. 8.J. de Wiljes, X. T. Tong (2020) Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations. Nonlinearity, 33(9): 4752--4782.
   

9. 9.J. de Wiljes, S. Pathiraja, S. Reich (2020) Ensemble transform algorithms for nonlinear smoothing problems. SIAM SISC. 42(1): A87--114.
   

10. 10.C. Maier, N. Hartung, J. de Wiljes, C. Kloft, W. Huisinga (2020) Bayesian data assimilation to support informed decision-making in individualised chemotherapy. CPT Pharmacometrics Syst Pharmacol. 9(3): 153-164.
    

11. 11.de Wiljes, J., Stannat, W., Reich, S. (2019). Long-time stability and accuracy of the ensemble Kalman-Bucy filter for fully observed processes and small measurement noise. SIAM J. Appl. Dyn. Syst. 17(2): 1152–1181.
    

12. 12.A. Taghvaei, J. de Wiljes, P.G. Mehta, S. Reich (2018). Kalman Filter and its Modern Extensions for the Continuous-time Nonlinear Filtering Problem. 140(3): 030904-030904-11 J. Dyn. Sys., Meas., Control. 140(3): 030904-030904-11.
    

13. 13.A. David, J. de Wiljes, S. Reich (2017) . Interacting particle filters for simultaneous state and parameter estimation. technical report. arxiv:1709.09199
    

14. 14.W. Acevedo, J. de Wiljes, S. Reich (2017). A second-order accurate ensemble transform particle filter. SIAM J. Sci Comp. 39(5) A1834-A1850
    

15. 15.J. de Wiljes, (2015). Data-Driven Discrete Spatio-Temporal Models: Problems, Methods and an Arctic Sea Ice Application. Dissertation. FUDISS_ thesis_ 000000098296 .
    

16. 16.de Wiljes, J., Putzig, L. and Horenko, I. (2014). Discrete nonhomogeneous and nonstationa- ry logistic and Markov regression models for spatiotemporal data with unresolved external influences. Communications in Applied Mathematics & Computational Science, 284:184–193.
    

17. 17.de Wiljes, J., Majda, A. J. and Horenko, I. (2013). An adaptive Markov Chain Monte Car- lo approach to time series clustering of processes with regime transitions behavior. SIAM Multiscale Modeling & Simulation, 11(2):415-441.