Distinguished Professor Fang Chen, Executive Director UTS Data Science Institute, has been named the 2026 Intelligent Transport Systems (ITS) Australia’s Woman of the Year.
AI leader named Woman of the Year
An internationally acclaimed leader in AI, data science and transport innovation, Fang has delivered some of Australia’s most advanced data-driven transport solutions. From rail performance modelling with Sydney Trains, to predictive analytics for V/Line, to groundbreaking research integrating transport and energy systems with AEMO, her work continues to reshape the future of mobility.
ITS Australia CEO Susan Harris praised her impact and leadership.
“Distinguished Professor Chen is an extraordinary innovator whose research and real-world applications have redefined what’s possible in Australia’s transport technology landscape. Her ability to bridge academia and industry has delivered tangible benefits for commuters, operators, and policymakers alike. She is an exemplary recipient of the ITS Australia Woman of the Year Award.”
“This recognition celebrates the collective effort of our research, industry and government partners working together to make transport smarter, safer, and more sustainable through data-driven innovation,” said Fang.
Distinguished Professor Chen is an extraordinary innovator whose research and real-world applications have redefined what’s possible in Australia’s transport technology landscape.
Throughout her career, Professor Chen has delivered pioneering research with substantial social and economic outcomes, including:
Revolutionary Rail Network Performance Modelling
In partnership with Sydney Trains, Distinguished Professor Chen and her team developed the world’s leading comprehensive, data-driven model for evaluating train timetable robustness. This system applies advanced machine learning to assess, predict, and optimise operational performance across entire rail networks in real time. The technology has since been adapted for Victorian train operations, improving efficiency across more than 4,000 kilometres of rail and millions weekly passenger journeys.
Advanced Disruption Forecasting for Regional Networks
Working with V/Line Victoria, Distinguished Professor Chen’s team created an integrated analytics platform that combines GPS-based route visualisation, predictive performance forecasting, and resource optimisation. This system enables operators to anticipate service interruptions, optimise response strategies, and enhance resilience for regional rail communities.
Pioneering Transport–Energy Integration for Electric Vehicle Futures
In collaboration with the Australian Energy Market Operator (AEMO), Distinguished Professor Chen and the team developed ground-breaking modelling that unites transport modelling and energy systems. The research demonstrates how electric vehicle adoption patterns affect grid demand and road congestion—providing essential insights to guide infrastructure planning and policy for Australia’s transition to a sustainable transport future.
Defining trust in the AI era
To achieve true adoption with productivity gains from AI, Fang believes that we must first understand how humans build trust in both data/information and AI systems.
“Trust is not abstract. It grows from transparency, reliability, and explainability, and from users’ confidence in the data and outcomes a system produces,” Fang said.
“Without trust, even the most capable AI tools fail to achieve adoption or impact.”
Fang believes said that AI is not just a non-technical or ethical consideration.
“Trust also spans multiple domains, including AI performance, transparency and explainability, and compliance with legal and technical regulations,” she said.
Without trust, even the most capable AI tools fail to achieve adoption or impact.
In practical terms, Fang says trust in AI adoption has two essential dimensions.
“The first is trust in the data and information used to train and update models, focusing on their quality, representativeness, and integrity. The second is trust in AI system performance, ensuring that systems perform as claimed and compliant, producing explainable, transparent, and measurable results.”
Building trust by design
“Safety for humans means AI outcomes must be worthy of trust by design,” said Fang.
This requires reliable data, models with quantified uncertainty and clear explainability, and ongoing monitoring as new data and retraining occur.
“A trustworthy AI system should demonstrate, through evidence, how it achieves measurable productivity gains while identifying possible risks in specific contexts.”
Fang says that human trust is emotional and hard to rebuild when it breaks.
In contrast, trust in AI can be restored through transparent processes and clear evidence.
“Measuring and calibrating this trust should be part of responsible AI development and deployment,” she said.
Fang believes that not all AI carries the same risks nor has the same trust issues.
Measuring and calibrating this trust should be part of responsible AI development and deployment.
“It is important not to generalise all AI as large, resource-intensive LLMs. Many AI systems are lightweight, explainable and bring measurable public and economic value at low risk,” she said.
Fang argues that AI should not only automate processes but also enhance human understanding and decision-making.
“Ultimately, to achieve true adoption with productivity gains from AI, we must build measurable pathways where both data integrity and system performance are demonstrably trustworthy, delivering sustainable value while keeping human confidence and safety at the centre.”
Congratulations Fang on this well-deserved recognition of your leadership and research contributions.
Celebrating Women in Transport Innovation
The ITS Australia Woman of the Year Award, proudly sponsored by Q-Free Australia, celebrates and elevates the growing impact of women shaping Australia’s intelligent transport future. Learn about the ITS Australia Awards.