Consultant Profile

Dr Sandhya Patidar

Region of consultancy:


Dr Sandhya Patidar has a track record in the inter-disciplinary application of the Mathematical/Statistical/MachineLearning techniques to a range of real-world problems, specifically in the areas of climate change, energy, built environment, water, and agriculture. She has extensive experience (with over 65 peer-reviewed publications) in the analysis and modelling of large datasets through the integration of novel computational techniques which are applied across a range of high-profile UKRI projects, such as e.g. EP/F038240/1, EP/I03534X/1, EP/K013513/1, EP/L000180/1, EP/N030419/1, F12R10013-TSB. All statistical/computational/time-series models developed by her are transferable and can be adapted for a range of applications, e.g. my recent novel computational model (STL_HMM_GP) is applied across a range of energy and streamflow simulations. Two of her publications won best-paper awards. She also won the ‘Sir David Wallace Prize’ for best presentation at Loughborough University and one as part of the £20m CESI project at Newcastle University.


Dr Sandhya Patidar is currently working as Professor (Associate) in Statistical Modelling at Heriot-Watt University, Edinburgh. She joined Heriot-Watt University in 2009 as a post-doctoral researcher in the School of Mathematical and Computational Sciences. Her work (highly-interdisciplinary application of statistical approaches) attracted lots of attention across the university and she has been offered an open-ended contract to work as a Lecturer of Statistical modelling in the School of Energy, Geoscience, Infrastructure and Society in 2013. With her growing success and commitment to the work, she has been promoted to Associate professor in 2019. She holds a Ph.D. in Applied Mathematics from Loughborough University (UK) and an MSc in Mathematics with specialisation in Statistics (Devi Ahilya Vishwavidhyalay, India) passed with distinction.


Developing application-specific tailored solutions (in form of hybrid models) for real-world problems through the novel application of a range of Mathematical/Statistical/Computational models. With her broad educational and extensive inter-disciplinary work experience, she has developed skills to analyse, model, or visualise all kinds of datasets (large/small, primary/secondary) using a range of mathematical/statistical/AI-based approaches.