Assistant 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.
Ethan’s teaching interest span a range of topics including statistics, machine learning, data science and computational neuroscience.
The field of Statistics aims to interpret large data sets that contain random variation. Baseball is a simple 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 and Data Science 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.
This class is an introduction to statistical methods that are useful for analyzing data. Topics will include descriptive statistics (summary statistics and graphical methods), and resampling and parameter 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 that cover newly introduced topics, and cumulative learning checks that reinforce the topics that have been covered. By the end of the class students should be able to understand the concepts that underlie statistical analyses used in a variety of fields, and should be able to apply statistical methods to gain insight into data that they collect.
Data Science is a field that uses computational tools to extract insight from large data sets. In this class will discuss several of the main topics in Data Science including data visualization, how to manipulate large data sets (data wrangling), and how to make predictions from data (machine learning). Students will learn how to use the R programming language to analyze data. Assignments will consist of weekly problem sets that cover newly introduced topics, and a midterm and a final project. By the end of the class students should be able to effectively visualize and analyze data in order to extract information for large data sets. The class has no prerequisites; past programming experience will be useful.
This course is an upper-level research seminar designed for students who wish to learn electrophysiological techniques and how to analyze electrophysiology data. Course requirements will consist of reading primary research articles, executing an event related potential (ERP) research project on visual processing, and analyzing the data that is collected. The class will cover all elements of setting up an ERP research project and we will focus on both the theory and practical aspects of developing and running research study. The data analyses methods will cover a range of techniques from classical univariate statistical techniques to more advanced multivariate statistical learning methods. Students are expected to work independently.
The rise of computers and large datasets over the past 30 years has led to the development of new methods for analyzing data. These 'statistical learning' methods blend classical statistical concepts with ideas from computer science and are widely used by data scientists to analyze complex datasets. In this class we will cover the basic concepts in statistical learning including: regression, supervised learning (classification), unsupervised learning (clustering and dimensionality reduction), cross-validation methods, and model selection. We will use the R programming language to explore the usefulness of different methods and to analyze real data. The class work will consist of weekly programming problems and a final project. Prerequisites: Prior experience with programming and statistics, either through a class or from other experiences.