Bruno Felisberto Martins Ribeiro

Bruno Felisberto Martins Ribeiro is an Associate Professor of Computer Science at Purdue University, joining the department in Fall 2015. His research focuses on endowing machine learning algorithms with robust invariant representations for relational and temporal data, emphasizing causal and associational tasks. Key research areas include Networking and Operating Systems, Artificial Intelligence, Machine Learning, and Natural Language Processing. He holds a Ph.D. in Computer Science from the University of Massachusetts Amherst (2010).

Education:

• Ph.D., Computer Science, University of Massachusetts Amherst, 2010

Research Interests: Explores invariances in mathematics and machine learning to improve model robustness. Key topics include graph and tensor invariances, causal relationships, adversarial robustness, and applications in recommendation systems, robotics, and drug discovery. His lab’s work has advanced counterfactual task frameworks and causal reasoning in machine learning.

Recent Contributions: Recent publications address zero-shot generalization in graph neural networks, causal discovery methods, and defenses against adversarial attacks. His work spans conferences like ICML, NeurIPS, and SIGCOMM.

Awards:

• Best Paper Award at ACM CODASPY 2021
• Best Paper Award at SIGMETRICS 2016
• Best Paper Award at IEEE NetSciCom 2014

Advising & Students: Supervises current PhD students Beatrice Bevilacqua, Jincheng Zhou, and Yucheng Zhang, along with MSc student Ipsit Mantri. Notable former students include S Chandra Mouli (Meta), Yangze Zhou (Spotify), and Jianfei Gao (Vector Institute).

Labs & Teams: Leads research in invariant representations and causal ML, collaborating with institutions like Stanford during his sabbatical. His work bridges theory and practice, impacting areas like network analysis and AI-driven healthcare.