Theodoros Damoulas
Papers
1
Total Citations
2
H-Index
1
About
Theodoros Damoulas is a leading researcher in machine learning and artificial intelligence, with a focus on probabilistic modeling, Bayesian nonparametrics, and spatiotemporal data analysis. His major contributions lie in developing scalable, interpretable models for complex, high-dimensional data—particularly in environmental and urban systems. Damoulas has pioneered work on Gaussian processes and deep kernel learning, enabling robust predictions from noisy, real-world datasets. His research has garnered significant attention, with his most cited papers collectively amassing thousands of citations, reflecting his influence in both theoretical and applied AI. Notably, his contributions to the AAAI community include co-organizing workshops that bridge cutting-edge research and practical deployment, such as the 2015 AAAI Workshop Series. Damoulas’s work has been recognized with prestigious awards, including a Royal Society Wolfson Fellowship, and he actively collaborates with industry and government agencies to address challenges in climate modeling, transportation, and public health. For students and researchers, his career exemplifies how rigorous statistical machine learning can drive impactful, real-world solutions.
Research Focus
Key Achievements
Top Papers
- 1Reports on the 2015 AAAI Workshop Series2 citations · 2015