Publications

Consumers and corporations are driven to engage in a digital world that they cannot adequately trust. We are developing paradigms to enable online commerce and facilitate machine learning in ways that provide privacy and protect user identities, by leveraging such concepts as local differential privacy, federated machine learning, identity brokering, and blockchain technology.

The tremendous growth in the learning capacity of Machine Learning methods has yet to be met with a corresponding growth in our ability to understand these models. Equally troubling, our ability to build robust machine learning models has not kept pace with research in adversarial attacks against machine learning. As we increasingly hand over decision making to automated machine learning and AI systems, we must find ways that the life-altering decisions made by these systems can be audited for fairness, safety, robustness to adversaries, and the preservation of privacy of any personally identifiable information over which they operate.