EIC Pathfinder DeepRAP - Trustworthy, Neuro-symbolic, Cognitive, Agentic AI Applications

With a PhD in Applied Mathematics (Loughborough University), my research interests are in multidisciplinary applications of mathematical/statistical/machine learning techniques to real-world engineering and scientific problems, specifically in challenge-driven AI for decarbonization, resilient infrastructure, smart energy systems, climate/environmental risk and socio-technical digital twins.

I offer expertise in -

• Applied hybrid AI, statistics, GenAI, LLM, and digital-twin methods

• Combining neuro-symbolic AI, ML, Statistical modelling and physics-informed models

• Novel LLM-enabled agents that combine multimodal evidence and expert rules,

• Trustworthiness Layers - quality check, reasoning, abstraction, semantic metadata, governance, privacy and bias awareness.

• Cognitive Digital Twin and uncertainty quantification for risk intelligence

• Evidence-to-action tool - involves explainable, transparent and evidence-based responsible AI for recommendations to stakeholders.

I have extensive experience working with big data, UKRI, and EU projects involving multidisciplinary academic and industry partners, as well as a range of stakeholders.

Strong scientific programming skills (e.g., Python, R, MATLAB, C, C++, FORTRAN).

My work has been documented in over 100 peer-reviewed interdisciplinary research publications.

Two of my outputs have won best paper awards at international conferences, two best presentation awards, including the prestigious ‘Sir David Wallace Prize’ (Loughborough University) and Alfred Carr award for best paper from the Institution of Civil Engineering (ICE).

My publication can be accessed through

https://researchportal.hw.ac.uk/en/persons/sandhya-patidar

or

http://scholar.google.co.uk/citations?user=40lHLgYAAAAJ&hl=en&oi=ao

or

https://www.researchgate.net/profile/S_Patidar2/contributions