Machine learning in adversarial environments. Machine learning underpins the technologies driving the economies of both Silicon Valley and Wall Street, from web search and ad placement, to stock predictions and efforts in fighting cybercrime. This project aims to answer the question: How can machines learn from data when contributors act maliciously for personal gain?
Statistical problems involving measurement errors and sparsity. The project tackles research in complex problems where the information contained in the data is sparse and corrupted by measurement errors. With the aid of modern computing methods, the project will develop new, sophisticated techniques that have applications in areas such as genomics, national security, environmental pollution, public health and nutrition.