Sahand Negahban

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


STAT 251b / STAT 551b/ENAS 496 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 262 Computational Tools for Data Science

Introduction to the core ideas and principles of modern data analysis, bridging statistics and computer science, and providing tools for changing methods and techniques. Topics include principle component analysis, independent component analysis, dictionary learning, neural networks, clustering, streaming algorithms (streaming linear algebra techniques), online learning, large scale optimization, simple database manipulation, and implementations of systems on distributed computing infrastructures. Students require background in linear algebra, multivariable calculus, and programming.

Term: Fall
Day/Time: Tuesday & Thursday, 2:30-3:45

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
Day/Time: Monday & Wednesday,2:30pm - 3:45pm