Dr. Hesam Nejati Sharif Aldin | ARC Hub for ICT

Dr. Nima Afraz is a faculty member in the School of Computer Science at University College Dublin and an SFI-funded investigator with expertise in scalable systems, data-driven infrastructure, and applied AI. As lead TR, he provides overall project leadership, ensures alignment with institutional and funding requirements, and supports the strategic direction of the transitional pathway, including positioning the platform for real world deployment and adoption.

Last updated: 10 July 2026

Research Themes: Sustainability & Environmental Management

Research Programmes

AgriFuTech

An AI genomic prediction engine that learns directly from complex, noisy datasets to deliver reliable trait predictions across crops and environments.

AI-Driven Energy Prediction and Workload Placement

AI-Driven Energy Prediction and Workload Placement

Predicts the energy a computing workload will use before it runs, helping data centres cut costs, reduce emissions and meet EU reporting rules.

AI-Sprayer

A chemical-free precision weeding, fertigation and irrigation platform for organic farms, using computer vision to target hot water at individual weeds.

Climate Parametric Risk Solution (CPRS)

Climate Parametric Risk Solution (CPRS)

Parametric climate risk products for agriculture and renewable energy, helping farmers, lenders and operators manage weather-driven revenue volatility.

ColdChain

A low-cost, disposable RFID tag that signals remotely if frozen goods rise above zero, enabling continuous cold-chain monitoring across the supply chain.

EleVate

An EV fleet battery platform using real-time vehicle data and AI to extend battery life, cut maintenance costs and improve fleet reliability.

IBSO

The Irish Building Stock Observatory links building performance with social, economic and financial data so retrofit investment can be targeted with evidence.

OptiSIFT

Certifies metal powder oxidation at the point of use in additive manufacturing, replacing destructive lab tests that take three to five days.

Physics-Informed Bayesian Network for Predictive Maintenance and Energy Loss Assessment in Thermal Process Plants

Physics-Informed Bayesian Network for Predictive Maintenance and Energy Loss Assessment in Thermal Process Plants

Detects combustion drift, fouling and steam trap failure in SME thermal plants, recovering fuel losses of €14,000–€25,000 a year without costly sensors.

Road AI

Automatically assesses road surface condition from imagery using computer vision, replacing slow manual inspection of defects such as potholes.

TrueFrame

Predicts optimal AV1 encoder settings in a single pass, removing the repeated trial encodes that drive up cloud costs and energy use.

Wingsense

Low-cost Doppler radar sensors that identify and count flying pollinators non-invasively, bringing biodiversity monitoring into environmental IoT networks.