I am a postdoctoral scholar in the Department of Geophysical Sciences and a member of the Climate Extremes Theory and Data (CeTD) group. Before coming to UChicago, I received my Ph.D. and M.S. in Physics from the University of Maryland, where my thesis focused on hybridizing scientific knowledge-based models with machine learning to accurately forecast high-dimensional chaotic systems. I was also a fellow in the Computation and Mathematics for Biological Networks (COMBINE) NSF NRT program at the University of Maryland. Beginning in September 2024, I will be a fellow in the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship program.
Perferred Pronouns: He/Him
Research Interests: I am interested in using ideas and techniques from physics and data assimilation to develop accurate and stable ML/AI emulators of terrestrial weather and climate. I am additionally interested in applying these emulators to the problem of estimating the probability of extreme weather and climate events.
Wikner, A. et al. Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems.Chaos 30, 053111 (2020).
Arcomano, T., Szunyogh, I., Wikner, A., Hunt, B. R. & Ott, E. A Hybrid Atmospheric Model Incorporating Machine Learning Can Capture Dynamical Processes Not Captured by Its Physics-Based Component. Geophysical Research Letters 50, e2022GL102649 (2023).
Wikner, A. et al. Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing. Neural Networks 170, 94–110 (2024).