Self-similar processes with stationary increments on Wiener chaos

Tracking cross-validated estimates of prediction error as studies accumulate

Smoluchowski-Kramers approximation and the exit problem for infinite dimensional systems

Advances in the Normal-Normal Hierarchical Model

Statistics meets Computation: Challenges with Massive Data

Title: Structured Estimation in High-Dimensions Abstract: Modern techniques in data accumulation and sensing have led to an explosion in the complexity of data. Many of the resulting estimation problems are high-dimensional, meaning that the number of parameters to estimate can be far greater than the number of examples. A major focus of my work has been developing an understanding of how hidden low-complexity structure in large datasets can be used to develop computationally efficient estimation methods with strong statistical guarantees. I will discuss a variety of problems including... Read more about StochStatMIT: Sahand N. Negahban

Data & Technology in Government Panel Discussion

Low Latency Financial Data: Continuous Time Analysis of Fleeting Discrete Price Moves

The exact k-SAT threshold for large k

Machine Learning as the Next Frontier for Social Science and Vice Versa

Love the Processor, Hate the Process: The Temptations of Clever Algorithms and When to Resist Them (part of the MIT Big Data Initiative)

Quadratic programming in synthesis of stationary Gaussian fields

Secretary Problems with Non-Uniform Arrival Order