Dr. Arezou Keshavarz
Staff Software Engineer at Google
Convex optimization has emerged as useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. We start with an overview of optimization in general, focusing on convex optimization in particular. We then cover constructive convex analysis and disciplined convex programming, the basis for several software packages for convex optimization, including CVX and CVXPY. Finally, we address several applications in finance, data fitting, and advertising. This is based on the short course offered by Stephen Boyd and his group on different occasion.
Dr. Arezou Keshavarz is a Staff Software Engineer at Google working on search features and ranking algorithms. She has worked on news, fresh content and international search retrieval and ranking algorithms. Prior to joining Google in 2013, she did her M.S. and Ph.D. studies under the supervision of Prof. Stephen Boyd at Stanford University in Electrical Engineering. At Stanford, she worked on convex optimization methods and distributed machine learning algorithms. Her research interests include predictive algorithms, large scale distributed optimization, information retrieval, and data science.
09:30 – 10:00 Opening
10:00 – 10:30 Introduction to the School
10:30 – 10:45 Coffee / Tea Break
10:45 – 12:00 Introduction to Statistics – Session 1
12:00 – 13:15 Lunch Break
13:15 – 14:15 Python Programming – Basics – Session 1
14:15 – 14:30 Coffee / Tea Break
14:30 – 15:30 Introduction to Statistics – Session 2
15:30 – 15:45 Coffee / Tea Break
15:45 – 16:30 Introduction to Statistics – Session 3
16:30 – 16:45 Coffee / Tea Break
16:45 – 17:45 Data Science in Practice Panel