## Autumn 2021

#### Nick Irons: Introduction to Bayesian Data Analysis

##### Student: Xuweiyi (William) Chen

##### Slides , Writeup

*Prerequisites:*Knowledge of expectations and probability distributions at the level of STAT 340-341 and some knowledge of R.

#### Alex Ziyu Jiang: Clustering and music genre classification

##### Student: Yitong (Eva) Shan

##### Slides , Writeup

*Prerequisites: Knowledge of probability at the level of Stat 311 or beyond; Some coding experiences, preferably in Python or R; It will be fantastic if you also happen to like listening to music ;)*

#### David Marcano and Daniel Suen: Cluster Analysis

##### Students: Townson Cocke and Renee Chien

##### Renee Slides , Renee Writeup

*Prerequisites: Basic knowledge of R or Python, statistical background equivalent to STAT 311 is recommended*

#### Anna Neufeld: Multiple Testing

##### Student: Cathy Qi

##### Slides , Writeup

*Prerequisites: Stat 311 and some knowledge of R will be helpful, but not required.*

#### Michael Pearce: Voting, Ranking, and Preference Modeling

##### Student: Carolina Sawyer

##### Slides , Writeup

*Prerequisites:*Stat 311 or equivalent

#### Seth Temple: Statistical Genetics I, Pedigrees and Relatedness

##### Student: Michael Yung

##### Slides , Writeup

*Prerequisites:*STAT 311; some programming experience preferred

#### Vydhourie R.T. Thiyageswaran: Graph Clustering

##### Student: Dawei Wang

##### Slides , Writeup

*Prerequisites:*Introductory Linear Algebra (and interest in basic introductory graph theory would be helpful)

#### Steven Wilkins-Reeves: An Introduction to Causal Inference and Sensitivity Analysis

##### Student: Hadi Nazirool Bin Yusri

##### Slides , Writeup

*Prerequisites:*Stat 311 (would be useful to have familiarity with linear regression)

#### Kenny Zhang: Basics of Causal Inference

##### Student: Qiguang Yan

##### Slides , Writeup

*Prerequisites:*STAT 311 level statistics, some familarity with regression is a plus.

## Spring 2021

#### Peter Gao: Ethics of Algorithmic Decision Making

##### Student: Kevin Hoang

##### Slides , Writeup

*Prerequisites:*None

#### Alex Ziyu Jiang: Sampling methods, Markov Chain Monte Carlo and Cryptography

##### Student: Kathleen Cayha

##### Slides , Writeup

*Prerequisites:*Basic knowledge of probability is recommended (STAT 311 level). Some prior coding experience with R would be great but not necessary

#### Alan Min and Anupreet Porwal: Expectations and Sampling methods

##### Students: Kai Gong and Aubrey Yan

##### Kai Slides , Kai Writeup

##### Aubrey Slides , Aubrey Writeup

*Prerequisites:*STAT 340-341 and some knowledge of R ; Basics of expectations and probability distributions.

#### Anna Neufeld: Infectious disease modeling

##### Student: Kayla Kenyon

##### Slides , Writeup

*Prerequisites:*Some comfort in R; experience with calculus and differential equations will be useful but not required.

#### Michael Pearce: Nonlinear Regression

##### Student: Muhammad Anas

##### Slides , Writeup

*Prerequisites:*A basic knowledge of linear regression and some experience in R

#### Taylor Okonek: Disease Mapping

##### Student: Wuwei Zhang

##### Slides , Writeup

*Prerequisites:*STAT 340; Interest in public health applications; Familiarity with R

#### Sarah Teichman: Ethics of Algorithmic Decision Making

##### Student: Liwen Peng

##### Slides , Writeup

*Prerequisites:*None

#### Apara Venkat: Networks and Choice Modeling

##### Student: Andrey Risukhin

##### Slides , Writeup

*Prerequisites:*Calculus (MATH 126) and exposure to probability theory (STAT 340). Linear Algebra (MATH 308) suggested, but not necessary. A general interest and curiosity about math and the world.

## Winter 2021

#### Peter Gao: Survey of Data Journalism

##### Student: Rohini Mettu

##### Slides , Writeup

*Prerequisites:*None

#### Richard Guo: Making probability rigorous

##### Student: Mark Lamin

*Prerequisites:*Probability theory at the level of MATH/STAT 394 and 395

#### Bryan Martin: Statistical Learning with Sparsity

##### Student: Jerry Su

*Prerequisites:*Familiarity with regression, up to a STAT 311 level

#### Eric Morenz and Yiqun Chen: See what's not there

##### Student: Suh Young Choi

##### Slides , Writeup

*Prerequisites:*Experience with linear regression, probability, or data manipulation will allow a deep dive into the content. It is not a requirement for students who are interested in the subject.

#### Taylor Okonek: Topics in Biostatistics

##### Student: Anna Elias-Warren

##### Slides , Writeup

*Prerequisites:*Introductory statistics a plus but not required, interest in public health applications

#### Michael Pearce: Nonlinear Regression

##### Student: Alejandro Gonzalez

##### Slides , Writeup

*Prerequisites:*A basic knowledge of linear regression and some experience in R

#### Sarah Teichman: Multivariate Data Analysis

##### Student: Lindsey Gao

##### Slides , Writeup

*Prerequisites:*Stat 311, and linear algebra would be helpful but not necessary

#### Seth Temple: Statistical Genetics and Identity by Descent

##### Student: Selma Chihab

##### Slides , Writeup

*Prerequisites:*STAT 311; some programming experience preferred

#### Apara Venkat: Networks and Choice Modeling

##### Student: Xuling Yang

##### Slides , Writeup

*Prerequisites:*Calculus (MATH 126) and exposure to probability theory (STAT 340). Linear Algebra (MATH 308) suggested, but not necessary. A general interest and curiosity about math and the world.

#### Jerry Wei: Topological Data Analysis

##### Student: Joia Zhang

##### Slides , Writeup

*Prerequisites: Exposure to probability theory and linear algebra*

#### Kenny Zhang: Deep Learning for Computer Vision

##### Student: Angela Zhao

##### Slides , Writeup

*Prerequisites:*Proficiency in a programming language (preferably python). Some exposure in basic probability rules and computer science would be helpful.

## Autumn 2020

#### Peter Gao: Statistics for Data Journalism: Election Forecasting

##### Student: Andy Qin

##### Slides

*Prerequisites:*Experience with introductory stats (at the level of any of the intro classes) would help.

#### Zhaoqi Li: Statistical Illusions

##### Student: Yeji Sohn

##### Slides

*Prerequisites:*Motivation to think about interesting problems and readiness for the brain to be teased. Some mathematical maturity would be beneficial.

#### Shane Lubold: Random Network Models

##### Student: Peter Liu

##### Slides

*Prerequisites:*Intro statistics and some programming experience (R or Python).

#### Bryan Martin: Ethics in Data Science and Statistics

##### Student: Jinghua Sun

##### Slides

*Prerequisites: None*

#### Ronak Mehta: The Magical Properties of the SVD

##### Student: Claire Gao

*Prerequisites:*Linear Algebra (Math 308 or equivalent). Some statistical background, preferably at the level of 340.

#### Anna Neufeld: Infectious Disease Modeling

##### Student: Harper Zhu

##### Slides , Shiny App

*Prerequisites:*Some comfort in R; experience with calculus and differential equations will be useful but not required.

#### Michael Pearce: History and Practice of Data Communication

##### Student: Ziyi Li

##### Writeup

*Prerequisites:*None; some experience with R or Python may be helpful but is not required.

#### Subodh Selukar: Introduction to Survival Analysis

##### Student: Howard Baek

##### Writeup , Shiny App

*Prerequisites:*Familiarity with R; familiarity with survival analysis

#### Sarah Teichman: Phylogenetic Trees

##### Student: Lexi Xia

##### Writeup

*Prerequisites:*An intro stats class. Some R experience is useful but not required.

#### Seth Temple: Statistical Genetics and Identity by Descent

##### Student: Rachel Ferina

##### Slides Writeup

*Prerequisites:*None; keen interest in the biological sciences

## Spring 2020

We ran a limited number of projects due to COVID-19.

#### Sheridan Grant: Causal Inference: DAGs and Potential Outcomes

##### Student: Grace Shen

##### Slides

*Prerequisites:*Familiarity with linear regression and facility with Gaussian distributions (preferably multivariate)

#### Shane Lubold: Random Graphs

##### Student: Gordon An

*Prerequisites:*Some exposure to probability. Some exposure to, or an interest in, graph theory.

#### Anna Neufeld: Disease Modeling

##### Student: Rachael Ren

##### Writeup , Slides

*Prerequisites:*Knowledge of R will be useful!

## Winter 2020

#### Peter Gao: Introduction to Gaussian Processes

##### Student: Hannah Chang

*Prerequisites:*None; interest in programming encouraged

#### Kristof Glauninger: Nonparametric Regression

##### Student: Eli Grosman

##### Writeup

*Prerequisites:*Familiarity with linear regression and basic probability, comfort with algebra, some calculus

#### Zhaoqi Li: Statistical Machine Learning and Data Analysis

##### Student: Zhijun Peng

##### Writeup

*Prerequisites*Knowledge of probability theory and Maximum Likelihood Estimation at the level of Stat 340 is preferred; some familiarity of basic programming is preferred; an enthusiasm of reading and experimenting is encouraged.

#### Shane Lubold: Random Graphs

##### Student: Tahmin Talukder

*Prerequisites:*Some exposure to probability. Some exposure to, or an interest in, graph theory.

#### Bryan Martin: R Package Development

##### Student: Thomas Serrano

##### Writeup

*Prerequisites:*Familiarity with R

#### Anna Neufeld: Statistical Natural Language Processing

##### Student: Christina Nick

*Prerequisites:*Proficiency in a programming language. Knowledge of basic probability rules at the level of Stat 311.

#### Michael Pearce: Nonlinear Regression

##### Student: Oliver Bejar Tjalve

##### Writeup

*Prerequisites:*A basic knowledge of linear regression and some experience in R

#### Anupreet Porwal: Bayesian Linear regression and applications

##### Student: Yuchen Sun

##### Writeup

*Prerequisites:*Basic knowledge of probability distributions at the level of Stat 394 or Stat 340. Knowledge of Linear Algebra is essential for this project. Familiarity with a programming language may be helpful.

#### Sarah Teichman: Networks

##### Student: Josiah Thulin

##### Writeup

*Prerequisites:*Stat 311. Some R is useful but not required.