Dr Mirza Rayana Sanzana is an interdisciplinary researcher working at the intersection of artificial intelligence, energy systems engineering, sustainable infrastructure, and digital health. Trained in software engineering, computer science, and civil engineering, her work develops AI-driven, data-centric solutions for thermal and hybrid energy storage, renewable integration, smart-grid urban systems, and clinical/neurological applications of machine learning.

Her research agenda integrates five core themes:

1. AI-Driven Energy Management for Thermal & Hybrid Storage
   Development of intelligent control strategies for thermal energy storage (TES) and multi-vector hybrid systems (solar PV, batteries, hydrogen). She applies ML, reinforcement learning, and multi-agent optimisation to enhance load shifting, flexibility, resilience, and operational efficiency across energy-intensive buildings and districts.

2. Renewable Integration & Smart-Grid Urban Systems
   Integration of intermittent renewables into urban infrastructures via storage hybridisation, sector coupling, managed EV charging, and grid-interactive buildings. Her work targets scalable, low-carbon, and resilient energy ecosystems.

3. Digitalisation & Predictive Analytics in the Built Environment
   Application of digital twins, forecasting models, and deep learning to HVAC and facility management for energy optimisation, anomaly detection, and predictive maintenance—reducing consumption and operational risk in building systems.

4. Sustainable, Climate-Resilient Infrastructure & the Water–Energy Nexus
   Design of adaptive systems that combine district energy models, hybrid storage, and multi-sector energy flows (heat–power–transport) to improve climate resilience and resource efficiency, particularly in fast-warming regions.

5. AI & Digital Health
   Development of privacy-aware, data-efficient ML models for clinical use, including early breast-cancer diagnosis in low-resource settings, automated and interpretable EEG analysis for epilepsy, and improved EEG interpretation for neurodivergent populations. Her work prioritises clinical relevance, explainability, and equitable deployment.

Across these themes, Dr Sanzana advances green AI for energy systems and responsible AI for healthcare, aiming to enable smarter, more resilient, and inclusive cities.

Supervision Interests

Dr Sanzana welcomes HDR candidates interested in applied artificial intelligence for sustainable infrastructure, energy systems, and digital health. Specific supervision areas include:

Energy, Built Environment & Sustainable Infrastructure

• Applied AI for commercial building HVAC: optimisation, predictive maintenance, anomaly detection, digital twins

• Thermal Energy Storage (TES): data-driven modelling, load shifting, and control strategy design

• Hybrid & emerging storage technologies: TES–battery–hydrogen–PV systems, AI-based forecasting and optimisation

• Renewable integration & grid-interactive systems: sector coupling, demand response, and urban grid resilience

• AI for sustainable food systems: quality detection, waste reduction analytics, and carbon/energy impact optimisation

• Sector coupling & multi-vector energy systems: heat–power–transport modelling, district-scale planning

• Cybersecurity for smart-building energy systems: resilience, privacy, and operational security

AI & Digital Health

• ML for early disease detection (e.g., breast cancer, low-resource diagnostics)

• AI for neurological and neurodivergent populations

• Explainable, fair, and clinically deployable AI

• Biomedical signal processing, physiological time series, and multimodal data fusion

She is particularly interested in supervising students motivated to apply AI to real-world sustainability, resilience, and healthcare challenges.

Dr Sanzana’s work is conducted in collaboration with the Monash Climate-Resilient Infrastructure Research Hub, the Centre for Net Zero Initiatives, and the HCC, AI & Data Science Research Groups in the School of IT — offering HDR candidates rich opportunities for interdisciplinary engagement across AI, energy systems, smart infrastructure, and digital health.