About Me
I am a postdoctoral researcher at the Institute of Mathematics, University of Potsdam, Germany in Prof. Sebastian Reich's group. I received degrees in both mathematics and engineering and am a University Medallist.
One of my overarching goals is to develop stronger connections between the mathematical & statistical foundations of data science methods and their applications. I am motivated by 1) how applications can inspire new theory and 2) how theory be developed in a more practically relevant way.

I am currently a researcher in the Collaborative Research Centre on Data Assimilation SFB1294 at the University of Potsdam in Project A02:Long time stability and accuracy of ensemble transform filters. Here I mainly focus on studying the theoretical properties of a new class of methods for sequential Bayesian filtering in the nonlinear, nonGaussian setting. These methods involve interacting particle systems characterised by a controlled stochastic differential equation in the mean field limit.
News

I was recently accepted to the FRIAS Young Academy for Sustainability Research. Looking forward to our first meeting in October!

I am organising a minisymposium at SIAMUQ22 with Hakon Hoel called Advanced methods for high dimensional Bayesian inference and nonlinear filtering.
My Research Interests
Analysis
Analysis of the Feedback Particle Filter
Pathiraja*, S., Reich, S., Stannat, W. (2021) McKeanVlasov SDEs in nonlinear filtering, SIAM Journal on Control and Optimization, [accepted]. arXiv:2007.12658
Pathiraja*, S., Stannat, W. (2021) Analysis of the feedback particle filter with diffusion map based approximation of the gain, Foundations of Data Science, 3(3), pp.615645. doi:10.3934/fods.2021023 arXiv:2109.02761
Research Interests
Analysis

stochastic analysis of controlled particle filters, incl. the feedback particle filter and its diffusion map based approximation

localisation methods in ensemble Kalman filtering

interacting particle systems
Algorithms
Applications

computational Bayesian inference

model uncertainty quantification

time varying parameter estimation based on sequential filtering

computational approaches to optimal transport based ensemble smoothing (e.g. second order corrections)

hydrology, rainfallrunoff modelling

intracranial hemodynamics

mathematics for sustainability science
Publications
Stochastic analysis of data science methods
Algorithmic & Methodological developments
Applications  Hydrology & Hemodynamics

Pathiraja*, S., Stannat, W. (2021) Analysis of the feedback particle filter with diffusion map based approximation of the gain, Foundations of Data Science, 3(3), pp.615645. doi:10.3934/fods.2021023 arXiv:2109.02761

Pathiraja*, S., Reich, S., Stannat, W. (2021) McKeanVlasov SDEs in nonlinear filtering, SIAM Journal on Control and Optimization, [accepted]. arXiv:2007.12658

Pathiraja*, S. (2020) L^2 convergence of smooth approximations of stochastic differential equations with unbounded coefficients, Stochastic Analysis & Applications, [under review]. arXiv:2011.13009.

Bishop, A. N., del Moral, P. and Pathiraja, S. (2018) Perturbations and projections of KalmanBucy semigroups, Stochastic Processes and their Applications, 128(9). doi:10.1016/j.spa.2017.10.006. arXiv:1701.05978

Pathiraja*, S., van Leeuwen, P. (2021) Multiplicative nonGaussian model error estimation in data assimilation, Journal of Advances in Modeling Earth Systems, [under review]. arXiv:1807.09621

de Wiljes, J., Pathiraja, S. and Reich, S. (2020) Ensemble transform algorithms for nonlinear smoothing problems, SIAM Journal on Scientific Computing, 42(1), pp.A87A114. doi: 10.1137/19M1239544. arXiv:1901.06300

Pathiraja, S. and Reich, S. (2019). Discrete gradients for computational Bayesian inference. Journal of Computational Dynamics, 6(2), pp.385400. doi:10.3934/jcd.2019019. arXiv:1903.00186

Pathiraja*, S., Moradkhani, H., Marshall, L., Sharma, A. and Geenens, G. (2018) Datadriven model uncertainty estimation in hydrologic data assimilation, Water Resources Research, 54(2), pp. 12521280. doi: 10.1002/2018WR022627.

Pathiraja*, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L. and Moradkhani, H. (2018) Timevarying parameter models for catchments with land use change: the importance of model structure, Hydrology and Earth System Sciences, 22(5), pp. 29032919. doi: 10.5194/hess2229032018.

Moradkhani, H., Nearing, G., Abbaszadeh, P. and Pathiraja, S. (2018) Fundamentals of data assimilation and theoretical advances, in Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H. L., and Schaake, J. C. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 126. doi:10.1007/9783642404573 301.

Pathiraja*, S., Marshall, L., Sharma, A. and Moradkhani, H. (2016) Hydrologic modeling in dynamic catchments: a data assimilation approach, Water Resources Research, 52, pp. 33503372. doi: 10.1002/2015WR017192.

Gaidzik, F., Pathiraja, S., Saalfeld, S., Stucht, S., Speck, O., Thevenin, D., Janiga, G. (2020) Hemodynamic data assimilation in a subjectspecific Circle of Willis geometry. Clinical Neuroradiology, doi: 10.1007/s00062020009592.

Pathiraja*, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L. and Moradkhani,H. (2018) Insights on the impact of systematic model errors on data assimilation performance in changing catchments, Advances in Water Resources. 113(December 2017), pp. 202222. doi:10.1016/j.advwatres.2017.12.006.

Pathiraja*, S., Marshall, L., Sharma, A. and Moradkhani, H. (2016) Detecting nonstationary hydrologic model parameters in a paired catchment system using data assimilation, Advances in Water Resources. 94, pp. 103119. doi: 10.1016/j.advwatres.2016.04.021.

Pathiraja, S.,Westra, S. and Sharma, A. (2012) Why continuous simulation? The role of antecedent moisture in design flood estimation, Water Resources Research. 48(6). doi: 10.1029/2011WR010997.
*= articles where I am the corresponding author
Current Projects

Stochastic analysis of the diffusion map approximation of the Feedback Particle Filter with Wilhelm Stannat (TU Berlin), Sebastian Reich & Jana de Wiljes (Uni Potsdam)

Generalisations of McKeanVlasov SDEs and their properties for nonlinear filtering with Wilhelm Stannat (TU Berlin), Sebastian Reich & Jana de Wiljes (Uni Potsdam)

Uncertainty quantification of neural network based methods for medical image segmentation with Franziska Gaidzik, Soumick Chatterjee & Gabor Janiga (OvGU Magdeburg)

Ensemble filtering and Bayesian inverse problems with Jana de Wiljes (Uni Potsdam), Lassi Roininen & Heikki Haario (LUT)
Recent & Upcoming Talks

Lappeenranta University of Technology Seminar, Finland (August 2021)

SIAM Conference on Computational Science and Engineering, Texas, USA (March 2021)

Data Assimilation Research Centre (DARC) Seminar, University of Reading, United Kingdom (March 2021)
CV
For further details see my CV
Nov. 2017 
University of Potsdam
Postdoctoral Researcher in the Institute of Mathematics and in the Collaborative Research Centre on Data Assimilation in Project A02: Long time stability and accuracy of ensemble transform filters
Mar 2014  Feb 2018
University of New South Wales
Doctor of Philosophy in the School of Civil and Environmental Engineering.
Thesis topic: improving data assimilation algorithms for enhanced environmental predictions.
Advisors: Ashish Sharma & Lucy Marshall.
AWARDS:

UNSW Research Excellence Award

CSIRO Flagship Postgraduate Research Award
Mar. 2007  Nov. 2011
University of New South Wales
Double Degree: Bachelor of Science (Mathematics) combined with Bachelor of Engineering (Environmental)
AWARDS: University Medal for exceptional academic performance.
Contact Information
Institute of Mathematics
University of Potsdam
house 29, room 2.52
KarlLiebknecht Str. 2425
14476 PotsdamGolm
Germany
tel: +49 331 977 203158
email: pathiraja (at) unipotsdam (dot) de