University Ph.D. Dissertation Defense, Department of Electrical Engineering, Advisor: Stephen Boyd

My first public lecture on the generalized signal decomposition framework I’ve been working on

A Toolkit for Unsupervised PV System Loss Factor Analysis

A preview of optimal signal demixing (OSD)

How can we use machine learning techniques to solve a classic statistics problem?

Real world PV data is often messy. In this post I show some methods I’ve developed as part of my research to handle messy PV data.

Scraping data from Wikipedia to investigate how much land a utility PV power plant requires

Using quantile regression to fit the clear sky signal in a daily solar energy data set.

An exploration of blind signal separation using convex optimization. Materials are covered in EE 364A Convex Optimization at Stanford University

Sparse matrix multiplication using Spark RDDs.

This is based on a problem presented in EE 178 Probabilistic Systems Analysis at Stanford University and the paper “Models for Transcript Quantification from RNA-Seq” by Lior Pachter.

This is based on lectures from EE 278 Statistical Signal Processing at Stanford University

This is based on a problem presented in EE 263 Linear Dynamical Systems at Stanford University