Sahand Negahban

Sahand Negahban's picture
Assistant Professor of Statistics
24 Hillhouse Ave, New Haven, CT 06511-6814


STAT 251b / STAT 551b Stochastic Processes

Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.

Term: Spring
Day/Time: Monday, Wednesday 1:00pm - 2:15pm

STAT 365b / STAT 665b Data Mining and Machine Learning

Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.

Term: Spring

STAT 679a High-Dimensional Statistical Estimation

In this course we will review the recent advances in high-dimensional statistics. We will cover concepts in empirical process theory, concentration of measure, and random matrix theory in the context of understanding the statistical properties of high-dimensional estimation methods. In this discussion we will also overview the computational constraints that are involved with solving high-dimensional problems and touch upon concepts in convex optimization and online learning.

Term: Fall
Day/Time: Tuesday, Thursday 12:00pm - 1:15pm