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Bruno Felisberto Martins Ribeiro is an Associate Professor in the Department of Computer Science at Purdue University, where he has been since Fall 2015. His research primarily focuses on endowing machine learning algorithms with the ability to learn robust invariant representations of relational temporal data across associational causal tasks. This work incorporates concepts from mathematics, physics, and statistics to achieve advancements in object invariance, particularly under a pre-specified set of transformations. Specific research areas include the utilization of graphs and tensors, with an emphasis on stationarity and robustness against adversarial attacks. Additionally, Ribeiro designs recommender systems for social networks, robotic systems that reason about relationships between objects, and methodologies for drug discovery through causal relationships and logical rule extraction from data. The outcomes of Ribeiro's research have broad implications, providing a principled framework for counterfactual tasks in machine learning models.
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