Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence (CausalAI) Laboratory at Columbia University. His research focuses on causal and counterfactual inference and their applications to artificial intelligence, machine learning, and data science in biomedical and social domains. In particular, Bareinboim is a leading proponent and driving force behind the causal-based approach to AI, which he argues is essential for achieving more general forms of AI and capabilities such as decision-making (including reinforcement learning), explainability (including fairness analysis), and generalizability. His scientific contributions include the first general solution to the problem of 'data fusion,' providing practical methods for combining data generated under different experimental conditions and affected by various biases. Bareinboim currently serves as the editor-in-chief of the Journal of Causal Inference (JCI), the first journal dedicated to causal inference research, and as an action editor of the Journal of Machine Learning Research (JMLR), the premier journal focused on machine learning. He received his Ph.D. from the University of California, Los Angeles, where he was advised by Professor Judea Pearl. Recognized by IEEE as one of 'AI's 10 to Watch', Bareinboim has also received several awards, including the NSF CAREER Award, ONR Young Investigator Award, DARPA Young Faculty Award, Dan David Prize Scholarship, the 2014 AAAI Outstanding Paper Award, and the 2019 UAI Best Paper Award. His research has been funded by grants and gifts from public agencies and private institutions, including the NSF, ONR, AFOSR, DARPA, the DoE, NIH, Amazon, JP Morgan, and the Alfred P. Sloan Foundation.