Department of Statistics Professor Matthias Katzfuss recently earned the prestigious honor of being elected Fellow of the American Statistical Association (ASA), the world’s largest organization for statisticians. Our department extends its sincere congratulations to Professor Katzfuss on this well-earned recognition.
The association honored Katzfuss for his “outstanding methodological contributions to computational spatial statistics, innovative applications to environmental science, impactful NASA collaborations, excellence in mentoring, and exemplary service to the ASA and the statistics community.”
Under ASA rules, fewer than 1% of their nearly 20,000 members can be elected as Fellows annually. This year, Katzfuss was one of 47 Fellows selected from leading universities, businesses, and government agencies across the country.
In a brief conversation, Katzfuss reflected on what this honor means to him, how he draws inspiration from his students and colleagues, and the impactful, interdisciplinary research that helped make him one of the newest ASA Fellows.
What does it mean to you, professionally and personally, to be named ASA Fellow?
Being named an ASA Fellow is a significant milestone in my career. It signifies recognition from my peers for my contributions to statistical science.
Personally, it is a source of immense pride. Knowing that my efforts have made an impact motivates me to continue pushing boundaries. I’m grateful for the mentors, colleagues, and students who supported me along the way.
What specific research areas or projects are you most proud of that factored into your being selected as ASA Fellow?
My group develops scalable statistical methods for Gaussian-process inference, geospatial analysis, and data assimilation, with a recent focus on machine learning and uncertainty quantification. In other words, We’re working on ways to understand patterns and make predictions, even when there’s not complete information available. For example, trying to forecast weather patterns or track the spread of a disease are complex statistical problems. We’re developing methods that can handle these kinds of complicated, real-world problems more efficiently and accurately.
Much of this work is interdisciplinary in nature, including collaborations in satellite remote sensing, tornado forecasting, agriculture, pollutant monitoring, and climate science.
I have maintained a long-standing funded collaboration for over 15 years with numerous researchers at the National Aeronautics and Space Administration (NASA), during which we have developed novel statistical methods and software for massive remote-sensing satellite data and for the analysis of climate-model output.
Specifically, we have worked to improve spatial retrievals of atmospheric and surface data inferred from [light] spectra observed by satellite instruments and to fuse measurements with varying quality and spatial resolution from different instruments. Put another way, our team is working on making satellite data more useful and accurate. We’re developing better ways to analyze measurements from satellites so we can understand what’s happening in specific locations on Earth.
We have also developed methods for scalable spatio-temporal data assimilation, which is an ubiquitous task in many fields of science, with numerical weather prediction as a prominent example. It is the process of sequentially inferring the true state of a system by combining noisy observations with a numerical model that describes how the system evolves over time. Think of it like putting together various types of information to create a clearer overall image of what’s happening on Earth, whether it’s monitoring air pollution or observing climate patterns. This work culminated in an NSF CAREER award.
Is there anyone in particular who has inspired your work? If so, how have they done so?
I am incredibly proud of my PhD students and postdocs. Their dedication, curiosity, and fresh perspectives have enriched my own understanding. The mentor-mentee relationship is a two-way street—I’ve learned as much from them as they have from me. Their contributions to research and their growth as scholars bring me tremendous joy and fulfillment.
What are some interesting new research frontiers that you hope to contribute to or see further advancements in during your career?
One of the things I am currently interested in is the emulation, analysis, and calibration of climate models—basically, how to fine-tune predictions about possible future climate scenarios.
I am also interested in extending statistical and Bayesian techniques to probabilistic and generative machine learning, potentially enabling AI tools to make better predictions based on existing training data. These are complex but exciting challenges.
For a full list of 2024 ASA Fellows, view this page. You can also see a full list of all ASA Fellows since 1931.
To explore the full list of our outstanding faculty, visit this page.