Deep networks often achieve better accuracy as we employ larger models. However, modern machine learning applications involve multiple considerations alongside accuracy, such as resource-efficiency, robustness, or fairness. Deploying ML in the real-world requires sound solutions addressing these considerations. In this talk, I will first discuss optimizing fairness objectives for imbalanced data. We observe that a […]

Rapid progress in quantum computing technologies is ushering in a new era for quantum many-body physics. Today's noisy, intermediate-scale quantum (NISQ) devices, while still far from fault-tolerant quantum computers, are exceptional laboratory systems, with large many-body Hilbert spaces and unprecedented capabilities for control and measurement. This allows the exploration of quantum dynamics in new far-from-equilibrium […]

Feedback-driven decision-making systems are at the emerging frontier of machine learning applications. Upcoming applications of societal consequence, such as self-driving vehicles and smartwatch-based health interventions, have to contend with the challenge of operating in reactive stateful environments. In this talk, I will describe my work on designing principled robust algorithms for feedback-driven learning, with provable […]