Hannan Medal Honors Legacy of Statistical Excellence

In a ceremony celebrated globally on April 8, 2025, Professor Noel Cressie was awarded the esteemed Hannan Medal by the Australian Academy of Science.This prestigious award, given biennially, acknowledges outstanding research contributions across statistical science, pure mathematics, applied mathematics, and computational mathematics. It serves as a tribute to the late Professor E.J. Hannan, a visionary in time series analysis, whose pioneering work continues to inspire researchers worldwide.

The Hannan Medal,awarded to Professor Cressie,recognizes his significant contributions to spatial statistics and his extensive interdisciplinary collaborations that have advanced environmental science,climate research,and planetary protection. Spatial statistics, a field gaining increasing attention in the U.S., provides critical tools for analyzing geographical data, from predicting weather patterns to understanding urban sprawl. For American readers,consider how spatial statistics are used daily – from optimizing delivery routes for companies like UPS and fedex to predicting wildfire spread in California.

The implications of Professor Cressie’s work extend far beyond academia. His expertise is vital for addressing some of the most pressing environmental challenges facing the U.S. and the world.

Professor Cressie’s Passion for Planetary Stewardship

Professor Cressie,reflecting on the award,shared his profound commitment to environmental stewardship. It’s a great honor to be given the Hannan medal. It’s only given once every six years to statisticians, and I think it is recognition that I have a role to play to make this world a better place.

He further emphasized the urgency of collective action: I am passionate about our planet. I believe we have a duty – personally and collectively – and we are not collectively taking up that responsibility. This call to action resonates deeply in the U.S.,where environmental concerns are increasingly prominent in public discourse.

advancing Environmental Science Through Spatial Statistics

Professor Cressie, a Distinguished Professor of Statistics, directs the Center for Environmental Informatics. His research focuses on developing statistical methodologies crucial for tackling global environmental challenges. His work exemplifies the increasing importance of data-driven solutions in environmental policy, a trend that’s gaining traction in Washington D.C., as policymakers seek evidence-based strategies to address climate change.

Professor Cressie’s expertise in spatial statistics has fostered collaborations on diverse projects, including Antarctic research, soil carbon cycling, and partnerships with NASA. These collaborations highlight the global nature of environmental challenges and the need for international cooperation.

Making Sense of Uncertainty: Professor Cressie’s approach

Professor Cressie describes his work as the science of uncertainty or imperfection, and there is a lot of imperfection in the world. It is indeed somewhat curious that my concentration is to take the things that are uncertain and try to make sense of them – to turn them into a way of being more certain about the things we do know in science. This sentiment speaks to the core of statistical analysis – using data to reduce uncertainty and inform decision-making.

NASA Collaboration: Planetary protection and Carbon Monitoring

Professor Cressie’s collaboration with NASA includes developing risk assessment protocols for samples returning from Mars around 2040. This work is crucial for ensuring planetary protection, preventing potential contamination of Earth by extraterrestrial organisms. Recent advancements in deep-space exploration and sample return missions have made this area of study a top priority for NASA. The Perseverance rover, currently exploring Mars, is collecting samples intended for future return to Earth.

His contribution to NASA’s Orbiting Carbon Observatory 2 (OCO-2) satellite has been pivotal in measuring and mapping carbon dioxide (CO₂) fluxes in the atmosphere. OCO-2 provides high-resolution data on CO₂ concentrations, helping scientists understand the sources and sinks of this greenhouse gas. This data has direct implications for U.S. climate policy, informing efforts to reduce carbon emissions and mitigate climate change.

WOMBAT: A statistical Framework for Understanding Carbon Fluxes

Professor Cressie’s team developed WOMBAT (Wollongong Methodology for bayesian Assimilation of Trace-gases),a complex statistical framework. Our work has been critical in understanding flux, the sources and sinks of CO₂ on a global scale, Professor cressie explained.

The WOMBAT framework exemplifies the power of Bayesian statistics in environmental modeling. Bayesian methods allow scientists to incorporate prior knowledge and uncertainties into their analyses, producing more robust and reliable estimates of CO₂ fluxes. This approach is particularly valuable in the U.S. for regions with complex landscapes and diverse carbon sources, such as the Midwest’s agricultural lands and the Pacific Northwest’s forests.

The Path to Mitigation: Measure, Monitor, and Map

Professor Cressie concludes with a call for proactive environmental management: If you measure, monitor, and map, then you have a chance to mitigate. This simple yet powerful message underscores the importance of data collection and analysis in addressing environmental challenges.For the U.S., this means investing in comprehensive monitoring networks, supporting research on new measurement technologies, and developing sophisticated mapping tools to visualize environmental data.

Counterarguments and Considerations

While Professor Cressie’s work highlights the potential of spatial statistics to address environmental challenges, some potential counterarguments and considerations shoudl be addressed:

• data Availability and Quality: The accuracy of spatial statistical models depends on the availability of high-quality data. In some regions, particularly developing countries, data may be sparse or unreliable, limiting the applicability of these models.
• Model Complexity: Spatial statistical models can be complex and require specialized expertise to develop and interpret. There is a risk of overfitting the data, leading to inaccurate predictions.
• Computational Resources: analyzing large spatial datasets requires significant computational resources. This can be a barrier to entry for researchers and organizations with limited budgets.

Though, ongoing advances in data collection technologies, statistical methods, and computing power are helping to address these challenges, making spatial statistics an increasingly valuable tool for environmental science.