Rate My Professor Fabio Ramos

Ramos's academic interests center on modeling and understanding uncertainty for prediction and decision-making tasks, including Bayesian statistics, data fusion, anomaly detection, and reinforcement learning. These approaches have been applied to robotics, mining and exploration, environmental monitoring, and neuroscience. Key publications include "Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent" (RSS 2015, with Lionel Ott), "Joint clustering and anomaly detection" (NIPS 2014), "Learning Non-Stationary Space-Time Models for Environmental Monitoring" (AAAI 2012, with Sahil Garg and Amarjeet Singh), and "Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds" (ECML 2018, Best Student Paper Award). His scholarship demonstrates substantial influence, with 17,114 citations and an h-index of 53 on Google Scholar. Ramos has secured major funding, such as A$1.9 million from the Australian Centre for Renewable Energy for machine learning methods in geothermal exploration.