Associate Professor of Statistics
Ethan’s research focuses on creating methods for analyzing high dimensional neural signals in order to understand the neural processing that underlies object recognition, working memory, and other cognitive processes. His teaching interests span a range of topics, including statistics, machine learning, data science, and computational neuroscience.
This class is an introduction to descriptive and inferential statistics that are useful for analyzing data from a variety of fields. Topics covered include summary statistics, graphical methods, and resampling and parametric inference methods for calculating confidence intervals and conducting hypothesis tests. Students will learn how to use the R programming language to explore statistical concepts and to analyze real data. Assignments will consist of weekly problem sets and a final class project where students will gain experience analyzing a dataset in more depth. By the end of the class students will be able to understand the concepts that underlie statistical analyses and will be able to apply statistical methods to gain insight into data that they collect.
Description: With the rise of Internet and other new technologies, large datasets are now available that can give deep insights into questions about science, human nature, and society. However, to extract useful information from this data, powerful data analysis methods are needed. Data Science is a field that addresses this issue by using computational tools to gain insights from large datasets. In this class, students will learn how to apply Data Science methods, and the R programming language, to visualize, manipulate, and make predictions from data. Assignments will consist of weekly readings of data journalism articles, weekly problem sets to practice particular skills, and a midterm and a final project where students will explore a dataset in more depth. By the end of the class students will be able to visualize and analyze data in order to answer a range of interesting questions.
The field of Statistics aims to interpret large data sets that contain random variation. Baseball is a game that contains a high degree of randomness, and because professional baseball has been played since the 19th century, a large amount of data has been collected about players' performance. In this class we examine key concepts in Statistics using baseball as a motivating example, and students will learn how to use the R programming language to analyze data. Assignments will consist of weekly problem sets, two class presentations, and a short final project. By taking this class students will develop an understanding of key statistical concepts that will be useful for interpreting data from many fields.
Our brains underlie our ability to perform complex tasks, but exactly how neural activity enables behavior is not well understood. To gain insight into this question, neuroscientists have developed a variety of technologies to record neural activity, however to turn these recorded signals into meaningful insights data analysis methods are needed. In this class students will learn how to analyze neural data by researching how information is coded in neural activity. In particular, the class will work together to analyze data sets that consist of neural spiking activity from different regions of macaque cortex with the aim of producing a publishable quality research paper. Methods that will be covered will range from classical univariate statistics such as ANOVAs, to multivariate machine-learning-based 'decoding' analyses, and students will learn how analyze data using Matlab. We might also examine data from other recording modalities including fMRI, EEG, and behavioral experiments, and students can potentially work on other neural data analysis research projects depending on their interests. Work for this class includes reading research papers, analyzing data in Matlab and R, and presenting on research findings. Prerequisites: prior experience with Statistics and computer programming.