## Spring 2022

#### Andrea Boskovic and Harshil Desai: NBA Analytics and Machine Learning

##### Student: Kobe Sarausad

##### Slides , Writeup .

##### Student: Pranav Natarajan

##### Slides , Writeup .

*Prerequisites:*Some experience in R or Python; some knowledge about basketball

#### Nina Galanter: Optimal Treatment Rules: Causal Inference and Statistical Learning

##### Student: Max Bi

##### Slides , Writeup

*Prerequisites:*Some familiarity with conditional probability, linear regression, and R

#### Anna Neufeld and Alan Min: Introduction to Computational Biology

##### Student: Wei Jun Tan

##### Slides , Writeup

##### Student: Iris Zhou

*Prerequisites:*Programming experience (preferably in R). Knowledge of probability distributions at the level of Math/Stat 394 or Stat 340 is preferred but not required.

#### Reading and Research Opportunity on Voting

#### Mentors: Prof. Elena Erosheva, Michael Pearce, Prof. Conor Mayo-Wilson

##### Students: Minghe (Mia) Zhang and Man (Terry) Yuan

##### Slides , Writeup

*Prerequisites:*Prerequisites: Computational skills (R required; other knowledge and experience, e.g., with python, is desirable). Preference given to Statistics and CSE majors and to candidates with interest and possibility to continue with the project in Summer and Fall 2022

#### Antonio Olivas: Estimation for cancer screening models using deconvolution

##### Student: Jia Zeng

##### Slides , Writeup .

*Prerequisites:*Calculus (MATH 126) and exposure to probability theory (STAT 340).

#### Rrita Zejnullahi: Introduction to Human Rights Statistics

##### Student: Cindy Elder

##### Slides , Writeup

*Prerequisites:*Some exposure to survey sampling and regression analysis.

## Winter 2022

#### Medha Agarwal: Statistical Simulations

##### Student: Evana Sorfina Mohd Nazri

##### Slides , Writeup

*Prerequisites:*STAT 311, programming experience (preferably in R/Python)

#### Michael Cunetta: Sabermetrics

##### Student: David Wang

##### Slides , Writeup

*Prerequisites:*Familiarity with the rules of major league baseball. Some familiarity with R.

#### Nina Galanter: Optimal Treatment Rules: Causal Inference and Statistical Learning

##### Student: Leah Jia

##### Slides , Writeup

*Prerequisites:*Some familiarity with conditional probability, linear regression, and R.

#### Jess Kunke: Survey statistics and R

##### Student: Mekias Kebede

##### Slides , Writeup

*Prerequisites:*The project can be tailored based on the student's background knowledge; some prior exposure to concepts such as mean, variance, and probability would be helpful.

#### Nick Irons: Bayesian Data Analysis

##### Student: Qianqian (Emma) Yu

##### Slides , Writeup

*Prerequisites:*Knowledge of probability at the level of STAT 311 and some familiarity with programming.

#### Erin Lipman: Bayesian perspectives on statistical modeling

##### Student: Zhengyang (Anthony) Xu

##### Slides , Writeup

*Prerequisites:*Some familiarity with multivariate linear regression will be helpful, as will some familiarity with R. Our project can be either more technical or more conceptual depending on the background and interests of the student.

#### Anna Neufeld: Introduction to Clinical Trials

##### Student: Hisham Bhatti

##### Slides , Writeup

*Prerequisites:*None.

#### Sarah Teichman: Multivariate Data Analysis

##### Student: Huong Ngo

##### Slides , Writeup

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

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

##### Student: Saleh Wehelie

##### Slides , Writeup

*Prerequisites:*STAT 311, and some programming experience

#### Drew Wise: Introduction to Nonparametric Statistics

##### Student: Xinyi (Vicky) Xiang

##### Slides , Writeup

*Prerequisites:*An introductory statistics class is all that's needed. Some programming experience would be a plus.

## 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.