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Portfolio item number 1

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Portfolio item number 2

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Paper Title Number 1

Published in Journal 1, 2009

This paper is about the number 1. The number 2 is left for future work.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf

Paper Title Number 2

Published in Journal 1, 2010

This paper is about the number 2. The number 3 is left for future work.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf

Paper Title Number 3

Published in Journal 1, 2015

This paper is about the number 3. The number 4 is left for future work.

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf

Subject-to-subject variability in the relative timing and coupling of population bursts in mouse visual cortex

Published: December 04, 2023

The Allen Brain Observatory Visual Coding Neuropixels data set contains simultaneous recordings from hundreds of neurons across many brain areas in multiple mice. We study the variation, across mice, of the functional connectivity among brain visual areas.

36-236: Probability and Statistical Inference II (Spring 2023)

Course, Statistics and Datascience Department, CMU, 2023

This is the second half of a two-semester, calculus-based course sequence that introduces theoretical aspects of probability and statistical inference to students. The course covers specific probability distributions and their inferential applications, starting with the normal distribution and continuing with the binomial and Poisson distributions, etc., and their use in point and interval estimation, hypothesis testing, and regression. Also covers topics related to multivariate distributions: marginal and conditional distributions, covariance, and conditional distribution moments.

36-225: Introduction to Probability Theory (Fall 2023)

Course, Statistics and Datascience Department, CMU, 2023

This is the first half of a year-long course which provides an introduction to probability and mathematical statistics for undergraduate students in the data sciences. Topics include elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, and the central limit theorem.