Dr. Sahani Pathiraja

CV

I am a Lecturer (tenure track assistant professor) in Data Science in the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney).  I have a double Bachelors degree in both mathematics and environmental engineering from UNSW for which I was awarded a University Medal.  I received a UNSW Research Excellence award and CSIRO Postgraduate Award for my PhD studies where my dissertation topic was “Improving data assimilation algorithms for enhanced environmental predictions.” My current research interests lie in the mathematical and statistical foundations of various data science methods.   

I have an interdisciplinary background with intimate knowledge of stochastic analysis, data science and Bayesian statistics through my postdoctoral research, and also of hydrologic applications and sustainability science through my PhD research and undergraduate studies, respectively.  One of my overarching goals is to develop stronger connections between the mathematical & statistical foundations of data science methods and their applications.  I am particularly interested in the two-way connections between mathematics and sustainability science, specifically: How can existing mathematical techniques be used to solve sustainability issues? And also, how can complex multi-faceted sustainability problems inspire new mathematical theory? This represents an exciting new field of research for me which is necessary to address the grand challenges facing our and future generations.
 

Selected Publications

• Pathiraja, S., Reich, S., Stannat, W. (2021) McKean-Vlasov SDEs in non-linear filtering, SIAM Journal on Control and Optimization, 59(6), DOI: 0.1137/20M1355197.
• 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), DOI: 10.3934/fods.2021023.
• Pathiraja, S. (2022) L2 Convergence of Smooth approximations of stochastic differential equations with unbounded coefficients, Stochastic Analysis and Applications (accepted).
• Pathiraja, S., Marshall, L., Sharma, A. and Moradkhani, H. (2016) Hydrologic modeling in dynamic catchments: a data assimilation approach, Water Resources Research, 52, pp. 3350-3372. DOI: 10.1002/2015WR017192.
• Pathiraja, S., Marshall, L., Sharma, A. and Moradkhani, H. (2016) Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation, Advances in Water Resources. 94, pp. 103-119. DOI: 10.1016/j.advwatres.2016.04.021.
• Pathiraja, S., Moradkhani, H., Marshall, L., Sharma, A. and Geenens, G. (2018) Data-driven model uncertainty estimation in hydrologic data assimilation, Water Resources Research, 54(2), pp. 1252-1280. DOI: 10.1002/2018WR022627.

YAS-Project

• Environmental Knowledge of Disastrous Water in Urban Europe (with Luisa Cortesi and Corinna Köpke)

Other projects & third-party funding

• Eva Mayr Stihl Foundation Seed Funding “Natural Hazards and Resilient Regions” (30,000 EUR)

• UNSW Faculty Research Grant for project “Bayesian Deep Learning for Climate Extremes” ($15,000 AUD)

• CSIRO Next Generation Graduate Program Funding “Sports Data Science and AI” ($2.3 Million AUD)