Biostatistics students in both the Masters and PhD programs are immersed in the division, assisting on high-impact national and international research projects, supporting the Division’s course offerings, and conducting their own research. Our students work closely with faculty advisers; with a student-to-faculty ratio that is one of the lowest among leading Biostatistics programs, students are able to get the training and personal attention they need to maximize their potential.

Students are actively involved in organizing both academic and social activities. The Student Seminar Series provides an opportunity to present and discuss work in progress, practice presentations for upcoming conferences, and learn new computing skills. Student seminars are typically closed to faculty. For a list of upcoming and previous student seminar presentations as well as other student resources, visit our Biostatistics Student Seminar page.

Meet Our Students

Michael Anderson
Andres Esteban Arguedas Leiva
Jordan Aron
Martha Barnard
Sydney Benson
Benjamin Bruncati
Jessica Butts
Rui Cao
Wenhao Cao
Sandra Castro-Pearson
Justin Clark
Christian Coffman
Jennifer Czachura
Simion De
Chloe Falke
Souradipto Ghosh Dastidar
Lillian Haine
Zuofu Huang
Ziyu Ji
Zhiyu Kang
Jonathan Kim
Zhaotong Lin
Limeng Liu
Han Lu
Mykhaylo Malakhov
David McGowan
Tanvi Mehta
Aidan Neher
Quinton Neville
Elise Palzer
Nirali Patel
Jennifer Proper
Zexi Rao
Kollin Rott
Sarah Samorodnitsky
Nitya Shah
Milena Silva
Michelle Sonnenberger
Josey Sorenson
Aparajita Sur
Lindsey Turner
Jiuzhou Wang
Wei Wang
Zheng Wang
Solvejg Wastvedt
Jack Wolf
Tiankai Xie
Kaifeng Yang
Lujun Zhang
Kody Baron
Seth Bergstedt
Emma Bidwell
Emma Billmyer
Amanda Brunton
Emmanuel Chea
Yixun Chen
Rachel Cho
Zhirui Deng
Samira Deshpande
Dhananjai Dhokarh
Madelyn Duffy
Ryan Gavin
Katherine Giorgio
Maria Godinez
Amelia Hoffbeck
Jiayi Hu
Monica Iram
Ziren Jiang
Elzbieta Jodlowska-Siewert
Tyler Jubenville
Julia Kancans
Sarah Leismer
Esteban Lemus Wirtz
Jiayu Lin
Chen Ling
Jialing Liu
Losha Ndemeno-Tegomoh
Denis Ostroushko
Mingming Pan
Neelanzana Paudel
Brandon Payne
Benjamin Pedersen
Tyler Richter
You-Shan Shen
Boya Shi
Kailash Velusamy
Abby Vogel
Daniel Waller
Jianfeng Wang
Zilin Wang
Yaqiao Wei
Yansong Wen
Cheng-Chang Wu
Yang Xie
Xizhi Xu
Yuanqi Yang
Qingyi Zeng
Bohua (Edward) Zhai
Can Zhang
Mengting Zhao
Yuntian Zuo
Nicolas Alamo
Michala Carlson
Santiago Charry
Meghan Devlin
Ashley Dvergsten
Bibin Joseph
Luis Silva
Michalina Thiel
Sarah Vadnais
Charly Vang

Upcoming Student Presentations

No upcoming presentations at this time.

Recent Student Presentations

Name Adviser Title
Linnea York Weihua Guan Pace of Aging in Proteomics as a Predictor for Cancer Incidence and Cancer Mortality
Clara Drew Cavan Reilly Instrument Selection to Maximize Power and Minimize Bias for Nonlinear Mendelian Randomization in Finite Samples
Robert Aidoo Sandra Safo Bronchodilator Response as a Predictor of Lung Function Decline Among People Living With HIV
Yi Kang Ann Brearley Models of Moderation in Regression
Andy Becker Julian Wolfson Clustering Methods for Correlated Data
Daniel Whitford Jared Huling Estimating Modified Treatment Effects: A Simulation Comparing Estimation Methods and Estimation of the Effect of Interventions by Public Health Nurses during Home Visits on Mental Health Outcomes
Ziou Jiang Tom Murray Analysis of Metformin Treatment Effect on Obese COVID Patients’ Hypoxemia Outcome and Primary Outcome with Multiple Hypoxemia Definition
Jingqi Liu Anne Eaton Bias in the Hazard Ratio for Progression-Free Survival Due to Delayed Detection of Progression and Different Clinic Visit Schedules by Treatment Group: A Simulation Study
Eric Connor Tom Murray Empirical Evaluation of Approaches for Handling Partially Missing Ordinal Outcome Data with Application to the ALPS-COVID Trial
Andrés Arguedas Ashley Petersen Use of Penalized Regression Techniques for Biomarker Discovery with Applications to Ovarian Cancer Screening
Anthony Johnson Weihua Guan Polygenic Risk Score Analysis on Kidney Transplant Outcomes
Mengli Xiao Haitao Chu Innovative Statistical Methods for Meta-Analyses With Between-Study Heterogeneity
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