Yahav Bechavod

Yahav is a computer scientist interested in developing principled techniques to ensure reliable machine learning for consequential decision-making. To this end, he designs algorithms that aim to guarantee fairness in prediction, be incentive-aware, and address bias along the different parts of the ML training pipeline — from data collection, to the feedback structure, to incorporating constraints and characterizing their costs and impacts. His work draws on and combines techniques and concepts from machine learning theory, optimization, and algorithmic game theory. He holds a PhD from the Hebrew University, during which he was also an Apple Scholar in AI/ML.